Buscar la tarea, el método o el artefacto que se necesita y luego usarlo en Claude o ChatGPT.
Los kits de tareas conectan recursos existentes en rutas completas con insumos, decisiones, artefactos, verificaciones, modificaciones y recuperación. Los recursos independientes siguen disponibles. Las páginas de tareas en inglés no cambian el idioma de sus fuentes.
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12 kits de tareas y 396 recursos independientes.
Kit de tareas conectado
Una Teoría del Cambio y un marco lógico coherentes, con supuestos causales y decisiones de medición pendientes visibles.
Traer: Descripción narrativa; Marco de resultados
4 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Construir y verificar una estructura de resultadosKit de tareas conectado
Un conjunto de indicadores alineado, hojas de referencia y plan de M&E con carga de recolección viable y un registro de decisiones explícito.
Traer: Indicadores; Marco de resultados; Plan de M&E
5 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Hacer viable la mediciónKit de tareas conectado
Un plan de línea de base y muestreo cuyo momento, marco, selección y afirmaciones de precisión estén respaldados por insumos reales.
Traer: Indicadores; Marco muestral; Informe de línea de base
3 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Planificar una línea de base y una muestra defendibleKit de tareas conectado
Un instrumento revisado, restricciones de muestreo y plan de piloto, seguido de revisiones solo cuando se proporcione evidencia real del piloto.
Traer: Preguntas de estudio; Cuestionario; Marco muestral
4 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Preparar un instrumento para una prueba piloto realKit de tareas conectado
Un análisis y una entrega trazables, o una especificación no ejecutada honesta cuando los datos legibles o la ejecución no están disponibles.
Traer: Datos de la encuesta; Libro de códigos; Plan de análisis
4 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Analizar la evidencia de encuestas de manera responsableKit de tareas conectado
Preguntas alineadas, matriz de evidencia y términos de referencia, revisados dentro de las restricciones reales de tiempo y recursos.
Traer: Documentos del programa; TOR de evaluación; Preguntas de evaluación
4 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Encargar una evaluación cuyas preguntas puedan responderseKit de tareas conectado
Un plan de DQA adecuado o una revisión basada en evidencia que muestre las verificaciones sin confirmar y las decisiones correctivas reales.
Traer: Plan de DQA; Informe de DQA; Registros de monitoreo
2 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Planificar o revisar la calidad operativa de los datosKit de tareas conectado
Una valoración QED específica para el diseño, con límites de aplicabilidad, supuestos de identificación y afirmaciones causales explícitos.
Traer: Diseño de evaluación; Datos de resultados; Estrategia de comparación
2 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Diseñar o revisar una evaluación cuasiexperimentalKit de tareas conectado
Una evaluación económica centrada en la toma de decisiones, con costos y resultados comparables, aritmética válida e incertidumbre explícita.
Traer: Datos de costos; Datos de resultados; Marco económico
2 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Evaluar el valor por dinero o la relación costo-efectividadKit de tareas conectado
Un informe de progreso verificado y modificado, con hallazgos trazables, cifras conciliadas y respuestas propuestas o acordadas que puedan llevarse a cabo.
Traer: Registros de monitoreo; Informe de progreso; Registros financieros
5 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Convertir la evidencia de informes en acciónKit de tareas conectado
Una agenda de aprendizaje, una guía de reflexión y un plan de uso vinculado a la evidencia que separa las decisiones reales de las propuestas.
Traer: Hallazgos; Agenda de aprendizaje; Registro de decisión
4 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Conectar el aprendizaje con las decisiones y su usoKit de tareas conectado
Un camino de codificación y síntesis verificado que utiliza la secuencia de prompts cualitativos existente y extractos reales.
Traer: Transcripciones; Hallazgos cualitativos
4 etapas conectadas con artefactos nombrados, verificaciones y recuperación.
Abrir kit de tareas: Conectar la evidencia cualitativa con el aprendizajeUsar un método individual directamente o regresar a su kit de tareas relacionado. Las descargas mantienen su formato real e idioma de origen. Los paquetes de instalación anteriores son separados de los métodos portátiles actuales.
Draft a theory of change that explains the program pathway, tests its assumptions, and identifies practical measures and risks.
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Build a logical framework with goals, outcomes, outputs, activities, indicators, means of verification, and assumptions.
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Develop a monitoring, evaluation, and learning plan with indicators, data collection methods, timelines, and responsibilities.
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Build an evaluation matrix linking evaluation questions to criteria, indicators, data sources, and methods.
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Generate specific, measurable, achievable, relevant, and time-bound indicators from your program objectives.
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Document how each indicator is defined, measured, disaggregated and reported, with baseline and target status.
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Develop learning questions, methods, and a plan for how your program will capture and use lessons.
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Map out who needs what data, when, and how it will inform decisions at each level of your program.
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Identify and categorize stakeholders by their interest, influence, and role in your M&E system.
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Build a justified M&E budget with line items for data collection, staffing, analysis, and reporting.
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Build a multi-level results chain showing how inputs lead to activities, outputs, outcomes, and impact.
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Design channels for collecting, processing, and responding to feedback from the people your program serves.
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Write a structured progress report for your donor with activities, results, challenges, and next steps.
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Write up evaluation findings into a professional report with methodology, results, and recommendations.
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Summarize a longer M&E document for decision-makers, preserving its verified content, caveats, and priorities within the required length.
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Write a structured case study documenting a program success, challenge, or lesson learned.
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Write terms of reference for commissioning an external evaluation, including scope, questions, and methodology.
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Capture and organize lessons from program implementation into a structured document for future use.
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Write the monitoring and evaluation section of a funding proposal with approach, indicators, and budget.
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Plan how to share M&E findings with different audiences : donors, staff, communities, and partners.
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Get structured feedback on your logframe's logic, indicator quality, assumptions, and completeness.
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Find the most important gaps in an existing MEL plan and turn them into practical revisions.
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Check the causal links and assumptions in a theory of change, with focused revisions and evidence questions.
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Review indicator wording and measurement rules, preserve agreed definitions, and identify the fixes that matter most.
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Test whether an evaluation design can answer its questions within the available evidence, resources and ethical limits.
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Improve an existing instrument with item-level fixes, marked revisions and a focused pretest plan.
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Strengthen a proposal MEAL section with evidence-linked fixes and clearly bounded funder-alignment advice.
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Turn a report draft into a prioritized set of evidence checks, clear edits and stronger conclusions.
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Analyze survey results with clear denominators, data-quality checks and conclusions the design can support.
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Develop traceable themes from interview transcripts while preserving participants’ meaning and divergent views.
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Build or refine a usable qualitative codebook with clear rules and transparent sample coding.
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Condense a report into a readable summary while preserving its evidence, limitations and recommendations.
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Create a precise, source-linked register of findings, values, recommendations and evidence gaps.
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Compare two measurement rounds with explicit comparability checks and cautious interpretation of change.
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Find supported trends, anomalies and relationships, with reproducible rules and useful follow-up checks.
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Examine gender-related participation and outcomes without overstating disparities or exposing small groups.
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Draft a household questionnaire that fits the decisions, respondents and safeguards of the proposed survey.
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Create a focused interview guide with neutral questions, useful probes and realistic fieldwork safeguards.
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Plan a focused group discussion with clear questions, inclusive moderation and honest privacy limits.
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Turn an observation purpose into clear, usable criteria with consistent recording and rating rules.
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Choose a defensible sampling approach with transparent assumptions, selection steps and limits on inference.
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Plan a baseline study with clear measures, feasible fieldwork and a defensible basis for later comparison.
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Plan a lean assessment around an urgent decision, the available time and the safest useful evidence.
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Design a midterm evaluation that answers practical program decisions without promising unsupported impact findings.
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Understand an M&E concept through a plain-language explanation, a practical example and a common mistake to avoid.
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Compare evaluation options at the right level and identify what fits the actual decision and constraints.
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Choose an evaluation methodology from the actual questions, evidence and constraints, with practical implementation advice.
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Select focused, feasible indicators for the program results and decisions that matter.
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Build a step-by-step analysis plan that matches the question, data and intended decisions.
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Build an evidence-based contribution story while testing alternative explanations and acknowledging uncertainty.
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Connect monitoring and learning to clear adaptation decisions, responsibilities and follow-up.
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Create a practical after-action review template that helps a team learn from a completed activity.
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Facilitate a focused review of evidence and turn useful learning into proposed program decisions.
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Synthesize available evidence around a learning question and show what it supports for the next decision.
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Compare continue, adapt and stop options using actual performance evidence, uncertainty and decision authority.
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Turn supported program findings into a clear knowledge product for the readers who need them.
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Capture a specific lesson with its evidence, transfer conditions and practical implications.
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Plan meaningful story collection and transparent selection without turning selected stories into prevalence or impact estimates.
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Design a practical learning event that helps participants apply evidence within their actual authority.
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Plan how named users will apply evaluation evidence to real decisions, with appropriate sharing and follow-up.
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Build a feasible GESI analysis framework around actual results, inclusion questions and safe evidence collection.
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Plan age-appropriate Washington Group integration and accessible survey participation using the exact official materials.
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Analyze program data through an intersectionality lens, examining how overlapping dimensions of identity (gender, age, disability, location, ethnicity) may relate to patterns of inclusion or exclusion.
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Create an environmental and climate screening checklist for program monitoring, identifying environmental risks, mitigation measures, and compliance requirements across program activities.
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Create climate-sensitive indicators and a climate risk monitoring framework that tracks both program contributions to climate resilience and exposure to climate-related risks.
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Analyze program monitoring data through a conflict sensitivity and Do No Harm lens, identifying how the program may be interacting with conflict dynamics and recommending adjustments.
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Create a protection mainstreaming monitoring framework with safeguarding indicators, complaint mechanism tracking, and protection risk analysis tools for humanitarian or development programs.
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Create a Value for Money (VfM) assessment framework using the 4Es model (Economy, Efficiency, Effectiveness, Equity), with indicators, benchmarks, and reporting templates.
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Compare supplied alternatives using auditable costs and results, with cost-effectiveness claims only when outcomes and comparisons support them.
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Plan locally led M&E with negotiated decision and data rights, community-defined measures and resources suited to local partners.
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Design an accountability framework linking accessible information, participation, safe feedback, response and decision rights.
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Review an M&E plan or data collection tool for potential harm, ethical risks, safeguarding gaps, and unintended negative consequences for participants and communities.
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Build a contribution analysis framework with contribution claims, evidence assessment criteria, and systematic testing of alternative explanations for observed outcomes.
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Build a complete Most Significant Change (MSC) data collection and selection protocol, including story collection guides, selection criteria, and domain definitions.
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Design an Outcome Mapping framework with boundary partners, progress markers, outcome journals, and strategy maps following the IDRC methodology.
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Build an Outcome Harvesting protocol with outcome description templates, substantiation questions, verification procedures, and analysis frameworks.
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Assess whether a quasi-experimental design is defensible, then plan the identification strategy, comparison and checks the evidence can support.
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Analyze qualitative data using a structured coding framework that combines deductive codes from your theory of change with inductive codes emerging from the data, culminating in thematic analysis.
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Create a Developmental Evaluation design for innovation or complex programming contexts, with real-time feedback loops, emergent learning questions, and adaptive evaluation architecture.
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Design a participatory evaluation approach that engages beneficiaries and stakeholders as co-evaluators, with inclusive methods, power analysis, and capacity building components.
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Analyze program data using a realist evaluation approach to develop and refine Context-Mechanism-Outcome (CMO) configurations that explain what works, for whom, and under what circumstances.
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Create a process tracing protocol for causal inference in single-case or small-n evaluations, with hypothesis formulation, evidence tests, and Bayesian confidence updating.
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Design a mixed-methods evaluation with integration points, sequencing decisions, a methods matrix, and a plan for combining quantitative and qualitative strands.
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Create an evaluability assessment to determine if a program is ready for evaluation, examining program design clarity, data availability, stakeholder readiness, and feasibility constraints.
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Build a stakeholder engagement plan for an evaluation: influence-interest mapping, tailored engagement methods, communication channels, and an implementation timeline.
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Design an evaluation findings dissemination strategy with audience-specific products, communication channels, and a timeline for maximizing evaluation use.
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Design a monitoring dashboard specification with KPIs, visualization types, update frequencies, user roles, and data flow architecture.
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Create a data quality audit protocol with verification procedures, sampling strategy, scoring rubric, and corrective action framework for assessing M&E data reliability.
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Draft a structured evaluation inception report outline covering methodology, workplan, evaluation matrix, risk assessment, team composition, and logistics.
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Create an enumerator training agenda and manual outline for a data collection exercise, covering methodology, tools, ethics, practice sessions, and field protocols.
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Create a field supervision checklist and data quality spot-check protocol for monitoring enumerator performance and data integrity during data collection.
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Draft a professional Scope of Work for hiring an M&E consultant or evaluation firm, including background, deliverables, qualifications, timeline, and evaluation criteria.
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Create a technical scoring matrix for evaluating proposals from evaluation firms or M&E consultants, with weighted criteria, scoring scales, and consensus procedures.
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Create a data management plan covering collection protocols, storage, cleaning, analysis workflows, archiving, ethical safeguards, and data sharing agreements.
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Translate M&E findings into a structured data storytelling brief with a narrative arc, key messages, supporting data points, and audience-specific framing.
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Draft a structured outline for an M&E findings presentation deck with slide-by-slide content, visualization recommendations, and speaker notes.
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Create structured content for an M&E infographic with headline stats, supporting data points, visual hierarchy, and layout guidance organized for a designer.
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Draft a concise stakeholder brief from supplied findings, with clear limitations and recommendations suited to its audience and required format.
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Analyze a dataset description and recommend the best chart types for each variable or comparison, with justification grounded in data visualization best practices.
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Create a one-page results snapshot (factsheet) with headline indicators, progress-to-target tracking, and key achievements for a reporting period.
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Draft a simplified community feedback report that presents M&E findings in plain language for beneficiary communities, with visual aids and actionable next steps.
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Create a structured evidence brief that synthesizes findings across multiple evaluations or studies on a specific M&E topic, with strength-of-evidence ratings.
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Create an M&E capacity assessment tool that evaluates organizational and individual M&E competencies across key domains, with scoring rubrics and gap analysis.
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Create an M&E capacity building plan with learning objectives, activities, timeline, responsible parties, and success indicators linked to assessment findings.
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Create a multi-day M&E training curriculum with detailed session plans, learning objectives, interactive exercises, and a complete materials list.
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Design an M&E mentoring and coaching framework with competency milestones, structured session plans, progress tracking tools, and matching criteria.
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Create a partner M&E capacity assessment and strengthening plan for sub-grantees or implementing partners, with risk-based tiering and tailored support packages.
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Draft a facilitation guide for an M&E training session including icebreakers, group exercises, case studies, debrief questions, and time management tips.
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Create an M&E staff competency framework with job families, proficiency levels, behavioral indicators, and development pathways for career progression.
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Design a Community of Practice structure for M&E practitioners with meeting cadence, knowledge sharing protocols, engagement strategies, and sustainability plan.
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Design a structured KoboToolbox or ODK-compatible survey form with question types, skip logic, validation rules, calculation fields, and deployment guidance.
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Create a mobile and digital data collection protocol covering device management, data syncing, offline procedures, quality checks, and troubleshooting.
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Create a remote monitoring plan for contexts where direct field access is limited, covering phone surveys, satellite imagery, third-party monitoring, and remote verification methods.
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Design a real-time monitoring system with automated alerts, threshold triggers, escalation protocols, and dashboard specifications for timely program decision-making.
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Create an ethics review or Institutional Review Board submission package with protocol summary, risk assessment, informed consent forms, and data protection measures.
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Create an informed consent form template for M&E data collection that meets ethical standards, is written in plain language, and covers voluntary participation, confidentiality, risks, and data use.
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Create a Data Protection Impact Assessment (DPIA) for an M&E data system covering data flows, privacy risks, legal basis, and mitigation measures aligned with GDPR and humanitarian data protection standards.
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Review an M&E data collection plan for ethical compliance against UNEG ethical guidelines, covering informed consent, do no harm, confidentiality, and vulnerable population protections.
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Design an M&E framework for a health program integrated with HMIS and DHIS2 data systems, WHO and SPHERE benchmarks, and sector-standard health indicators.
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Design a health household survey instrument using validated question modules for immunization, nutrition, WASH, and maternal health, with sampling strategy and field protocols.
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Analyze HMIS or DHIS2 routine health data to identify trends, coverage gaps, performance outliers, and actionable recommendations for program improvement.
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Design an M&E framework for an education program aligned to INEE Minimum Standards, with indicators for access, retention, learning outcomes, and education system strengthening.
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Design a literacy, numeracy or combined assessment blueprint using the actual curriculum, learners and authoritative assessment materials.
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Design monitoring for the WASH components actually provided, with defensible service classifications and water-quality evidence.
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Design a household WASH survey around the actual decision and setting, using authorized modules and supported service classifications.
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Design monitoring for actual food security and livelihoods activities using sourced instruments and clearly bounded classifications.
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Analyze supplied food-security and market evidence, preserving measurement limits and separating it from official IPC classification.
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Design a service-specific protection monitoring framework using minimal authorized data and specialist-approved safeguards.
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Compare evaluation approaches and get a recommendation for which method best fits your program context, resources, and evidence needs.
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Review a dataset or data collection process for quality issues across five dimensions: completeness, accuracy, consistency, timeliness, and validity.
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Scan a donor RFP or solicitation for every M&E-related instruction (compliance requirements, mandatory indicators, reporting expectations, evaluation commitments) so nothing is missed during proposal drafting.
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Assess whether your organization should pursue an RFP based on M&E fit: indicator feasibility, data collection context, capacity alignment, and risk flags surfaced in the M&E requirements. Produces a go, no-go, or go-with-conditions recommendation with caveats.
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Reason about what competing organizations are likely to propose on M&E for this RFP based on their public track records and sector norms, so your proposal differentiates where it matters and converges where convergence is the stronger move.
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Develop an M&E capture plan covering team structure, level of effort, subcontractor and consortium needs, data collection rounds with approximate budgets, and the open decision points that will shape the final MEL plan.
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Draft an M&E staffing plan for a proposal that specifies roles, level of effort (LOE), reporting lines, qualifications, and lifecycle coverage, sized to the program's scale and MEL plan workload with a defensible cost as a percentage of the program budget.
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Draft an activity-based M&E budget narrative using supplied quantities, rates, and proposal requirements, with unpriced items and assumptions made clear.
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Draft a proposal-ready data collection plan specifying methods, schedule, sampling (with design effect and non-response buffer), responsibilities, and instruments for every indicator in the MEL plan, plus an explicit digital-vs-paper decision.
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Transform an existing program narrative into a structured theory of change with causal pathway, intermediate outcomes, and testable assumptions. Useful when the program description is written but the ToC is not yet formalized.
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Operationalize a finalized theory of change into a 4x4 logframe matrix with goal, outcomes, outputs, and activities as rows and narrative, indicators, means of verification, and assumptions as columns, aligned to donor template expectations.
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Given a proposal due in 48-72 hours, produce an hour-by-hour workflow that sequences M&E section drafting: RFP extraction, ToC, logframe, MEL plan, staffing, budget narrative. Routes tasks through AI-assisted drafting, human review, and compliance checks.
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Conduct a red-team review of a draft proposal M&E section, identifying gaps, weaknesses, donor-compliance risks, and credibility problems a reviewer would flag. Purpose is to surface issues before submission, not to polish phrasing.
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Scrutinize an M&E budget for proposal submission. Check budget as percentage of program, itemization completeness, staffing LOE match to MEL plan workload, and flags for donor reviewers.
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Capture specific lessons from the supplied project or milestone and connect them to future decisions.
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Design an after-action review for the supplied completed activity, with practical facilitation and unconfirmed follow-up commitments.
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Design a prioritized learning agenda for the actual program duration and geography, organized around decisions.
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Design a reflection workshop around the team’s recent evidence and next decisions, fitted to the available time.
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Synthesize evidence-based patterns and contrasts across the supplied portfolio without erasing context or overstating confidence.
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Design a lightweight knowledge-management strategy around actual workflows, users, permissions and staff capacity.
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Connect routine program monitoring to an operational learning loop with meaningful signals, decision rights and follow-up.
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Distill policy-relevant insights from the supplied evidence for the stated topic, geography and policy decision.
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Assess whether documented prior lessons changed current program design and practice, using observable uptake evidence.
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Design a forward-looking pause-and-reflect session for the actual team, evidence and upcoming implementation decisions.
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Adapt verified program lessons for donor, peer and internal readers without changing their evidence or certainty.
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Document learning from program underperformance honestly, without blame and with explanations bounded by evidence.
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Find the most important evidence, reasoning and reporting problems in an evaluation draft, with practical revision advice.
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Review the supplied MEL plan draft for evidence-based completeness, feasible operations and conditions for the next review.
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Review a donor progress report for supported claims, consistent figures and documented reporting obligations before submission.
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Review the supplied survey instrument’s items, routing and safeguards, with clearly bounded field-readiness advice.
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Review the supplied logframe’s causal logic, measures and verification against actual program evidence and requirements.
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Review the evaluation ToR for clear purpose, defensible scope, feasible delivery and conditions still to resolve before issue.
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Review the indicator set’s definitions, result coverage, duplication and feasible measurement against actual requirements.
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Review the inception report’s design and safeguards, identifying conditions for the responsible owner before fieldwork.
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Review major report findings against their actual evidence, contrary cases and limits on inference.
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Review recommendations for clear actions, evidence links, feasible ownership and implementation tradeoffs.
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Design an evaluation matrix showing how the actual priority questions will be answered and judged.
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Design a sampling plan with defensible selection, size or qualitative scope, field procedures and inference limits.
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Design a monitoring system that connects actual indicators and capture processes to useful, authorized decisions.
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Describe the actual or proposed data flow with clear actors, source records, transfers, controls and use points.
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Design a results framework for the supplied program scope, distinguishing intended changes from achieved results.
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Review whether triangulation serves the evaluation questions and makes corroboration, differences and limitations visible.
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Review whether the sample rationale supports the actual quantitative or qualitative analyses and is feasible in practice.
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Review whether limitations are specific, supported and connected to the findings they constrain.
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Review whether an analysis plan makes the question-to-method choices and implementation steps explicit.
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Review whether a mixed-methods plan links its strands substantively and handles disagreement transparently.
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Review stakeholder engagement for relevant voices, safe participation, meaningful influence and accountable follow-through.
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Review a sampling frame’s source, coverage, currency and cleaning before selection, separately from sample-size justification.
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Draft an approach-appropriate qualitative coding frame, using provisional notes and reflexive memos when a fixed codebook does not fit.
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Draft an adaptive-management plan linking actual monitoring triggers to authorized decisions and documented follow-up.
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Design an instrument pilot that can reveal priority problems and support a later authorized readiness decision.
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Review each indicator against SMART using its definition, associated target and intended measurement context.
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Review baseline and target evidence, direction, feasibility and milestone logic without inventing values or universal trajectories.
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Review indicator sources and methods for specificity, fit, feasibility and decision-relevant collection frequency.
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Review only an indicator statement’s wording, using linked definitions to clarify units or comparators without expanding the task.
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Review the design of a composite or index indicator (component selection, weighting, aggregation, missing-data, interpretability).
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Review whether indicators are correctly classified at output, outcome, or impact levels with attribution-distance and time-frame checks.
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Review whether an indicator set aligns with the required donor framework (coverage, mapping, definition alignment, disaggregation, reporting readiness).
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Review the justification for a proxy indicator (rationale, link defense, bounded validity, triangulation, limitations).
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Review a disaggregation plan across the data pipeline (planning, collection, analysis, reporting, use).
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Review an executive summary of an evaluation or research report for stand-alone value, key-finding salience, coverage, plain language, and length discipline.
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Review the findings section of an evaluation, monitoring, or research report for source citation, triangulation visibility, qual-quant integration, inference strength, and contrary evidence.
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Review the recommendations section of a report for specificity, ownership, timeframe, resource realism, and finding linkage.
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Review the methods section of an evaluation or research report for design justification, data collection detail, analysis approach, QA, and reproducibility.
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Review the traceability and logic from findings to recommendations in a report.
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Review a needs assessment report for scope coverage, methods, data quality, prioritization logic, and use-readiness.
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Review a report (or section) for plain-language accessibility: reading level, jargon, structure, visuals, and audience fit.
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Review the theory-based evaluation findings (ToC restatement, per-link evidence, assumption verification, alternatives, ToC revision recs).
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Review a contribution analysis section for story construction, counterfactual reasoning, co-factor acknowledgment, calibrated language, and reader verifiability.
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Review the ethical standards in a methodology, evaluation design, or research protocol.
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Review the gender equality and social inclusion treatment in a MEL plan, evaluation methodology, or report.
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Review how deeply gender is integrated into an M&E design beyond sex-disaggregation.
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Review whether an M&E design treats identity intersections (gender, disability, age, ethnicity) as distinct analytic categories.
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Review evaluator self-reflection on bias, perspective, and positionality in qualitative methods.
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Review the protections for vulnerable populations in an evaluation or research design.
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Review data storage and privacy practices in a methodology or data management plan.
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Draft a do-no-harm risk identification and mitigation protocol for an evaluation or research activity.
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Draft an informed consent form or script with information completeness, comprehension aids, voluntariness, confidentiality, and special-population accommodations.
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Draft safeguarding procedures including training, reporting channels, escalation chain, response protocols, and audit trail.
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Review survey questions for clarity, neutrality, single-concept, specificity, and reading-level fit.
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Review response options (Likert scales, response categories) for exhaustiveness, mutual exclusivity, balance, clarity, and don't-know handling.
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Review a translated survey for semantic fidelity, cultural adaptation, back-translation evidence, response-option consistency, and field-testing.
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Review a focus group discussion guide for structure, opening/closing, probe quality, sensitivity handling, and facilitator notes.
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Review probe questions in a focus group discussion guide for depth, neutrality, sequencing, coverage, and facilitation guidance.
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Review a key informant interview guide for structure, question quality, sensitivity, sequencing, and facilitator notes.
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Review the question design in a key informant interview guide for openness, depth trajectory, informant tailoring, specificity, and time discipline.
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Review the protocol scaffolding (consent, recording, transcription, data handling) for FGD or KII data collection.
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Review a structured observation checklist for observability, inter-rater clarity, field usability, coverage, and open-notes space.
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Review the data flow diagram inside a MEL plan for completeness, role clarity, system specification, quality gates, and feasibility.
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Review the learning questions section of a MEL plan for strategic relevance, answerability, decision linkage, scope, and operationalization.
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Review the assumptions section of a Theory of Change for explicit statement, causal-link coverage, plausibility, testability, and prioritization.
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Review whether a Theory of Change pathway covers full causal logic from inputs to impact without gaps.
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Review the causal logic of a Theory of Change for plausibility, evidence base, and link integrity.
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Review the roles and responsibilities matrix inside a MEL plan for function coverage, position specificity, RACI discipline, escalation path, and staffing feasibility.
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Review whether indicators in a MEL plan are linked to specific decisions and learning needs.
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Review the assumptions column of a logframe for coverage of levels, specificity, causal reasoning, testability, and risk differentiation.
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Review the means of verification column of a logframe for source specificity, method appropriateness, frequency, responsibility, and feasibility.
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Review the evaluation questions section of a ToR for scope coherence, answerability, decision relevance, criterion coverage, and right-sizing.
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Review the budget in an evaluation ToR against scope and methods for cost alignment, breakdown specificity, field-cost realism, hidden costs, and contingency.
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Review the governance and oversight structure for an evaluation (steering committee, reference group, decision rights, COI, escalation).
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Review the stakeholder engagement plan in an inception report for identification, mode specificity, timing, feedback, and accountability.
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Review the financial commentary in a progress report (budget vs actuals plus variance explanation, burn rate, reallocation logic).
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Review the charts and figures in a report for chart-type appropriateness, labeling completeness, honest scaling, accessibility, and caption quality.
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Design the evaluator selection criteria and scoring matrix for an evaluation ToR (expertise specificity, experience calibration, scoring transparency, diversity, fairness).
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Review the workplan section of an evaluation inception report for phase decomposition, milestone definition, dependency mapping, buffer time, and resource loading.
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Build a source-by-source adjudication ledger for disputed monitoring values, with verification requests and traceable correction proposals.
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Separate contacts, within-period people and cumulative unique people across partners or periods using documented overlap evidence and explicit limitations.
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Map old and new indicator definitions and plan recoding, bridge measurement or an explicit series break.
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Reconcile final survey dispositions, diagnose response imbalance and assess defensible adjustment and sensitivity options.
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Trace the baseline cohort through follow-up, assess selective loss, and define the population supported by paired-change analysis.
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Map missing-value codes and patterns to defensible treatment choices, assumptions, sensitivity checks and a traceable decision log.
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Assess historical, retrospective and prospective measurement options when preintervention data were not collected.
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Check whether an existing dataset can support a new question, with claim-specific fit, provenance, coverage and use conditions.
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Specify permitted matching rules, ambiguous-link handling and error checks from safe source schemas before any record linkage.
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Prepare item-specific neutral probes and a session script to investigate comprehension, recall, judgment and response choices.
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Plan source-fidelity, uncertainty, translation and safe-label checks before qualitative coding.
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Trace existing outputs to source versions, calculations, environment details and actual verification evidence for a successor analyst.
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Score how well a deliverable links its data, indicators, or findings to specific decisions across five dimensions using AI. Use to assess MEL plans, indicator reference sheets, monitoring briefs, evaluation reports, learning agendas, and any deliverable that should drive action rather than only describe state.
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Score the assumptions column of a logframe for completeness, testability, and link to causal logic. Paste the logframe to assess whether assumptions are doing their analytic job. Use this for the logframe or results framework column; for the assumptions section of a Theory of Change narrative see toc-assumptions-quality.
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Score the means of verification (MoV) column of a logframe for source specificity, method appropriateness, and frequency clarity. Paste the logframe to assess whether the MoV column will guide data collection.
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Score a logframe across five dimensions using AI. Paste your logframe into the prompt to get a structured quality assessment with dimension scores, evidence citations, and priority revisions.
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Score the data flow diagram inside a MEL plan for completeness, role clarity, and feasibility. Paste the diagram (or its narrative description) to get a structured assessment with revision priorities.
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Score the learning questions section of a MEL plan for strategic relevance, answerability, and decision-linkage. Paste the section to assess whether questions will drive learning that informs program adaptation.
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Score a Monitoring, Evaluation and Learning plan across five dimensions using AI. Paste your MEL plan to get a structured quality assessment with dimension scores, evidence citations, and priority revisions.
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Score the roles and responsibilities matrix inside a MEL plan for completeness, specificity, and feasibility. Paste the matrix to assess whether every M&E function has named ownership.
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Score a Theory of Change across five dimensions using AI. Paste your ToC narrative or diagram description to get a structured quality assessment with dimension scores, evidence citations, and priority revisions.
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Score the causal logic of a theory of change in five dimensions using AI. This is a focused component rubric for the causal pathway only, useful when a ToC is embedded in a logframe, MEL plan, contribution analysis, or adaptive management memo. For a holistic assessment of a complete ToC document including stakeholder engagement and presentation, use the theory-of-change-assessment rubric instead.
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Score the assumptions section of a Theory of Change. Paste the assumptions to assess whether they are stated explicitly, testable, prioritized, and connected to specific causal links. Use this for the ToC narrative assumptions section; for the assumptions column of a logframe or results framework see logframe-assumptions-column.
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Score whether the causal pathway in a Theory of Change covers the full logic from inputs to impact. Paste the ToC to assess gaps, jumps, and missing intermediate steps.
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Score the baseline values and targets attached to indicators for plausibility, evidence basis, and ambition calibration. Paste the indicator with baseline and target to assess whether the numbers are defensible.
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Score the design of a composite or index indicator for component selection, weighting logic, and transparency. Paste the indicator definition to assess construction quality.
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Score data disaggregation quality across the full data pipeline (planning, collection, analysis, reporting, use) using AI. Applies to MEL plans, indicator frameworks, surveys, evaluation reports, donor progress reports, and monitoring briefs where data should be disaggregated.
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Review alignment against supplied, applicable funding-agreement indicator definitions and reporting instructions. Unverified obligations remain unresolved.
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Score any indicator or set of indicators in any M&E document (logframes, MEL plans, PIRS, indicator reference sheets, results frameworks) for SMART criteria, definition clarity, disaggregation, source documentation, and indicator family coherence using AI.
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Score whether the means of verification (data source plus collection method) named for each indicator are appropriate, specific, and feasible. Paste the indicator with its MoV to assess source-method fit.
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Score just the wording of an indicator statement for clarity, precision, and unit specification. Paste the indicator statement to get a focused diagnostic on language quality.
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Score whether indicators are correctly classified at output, outcome, or impact levels. Paste indicators to identify mis-classifications that obscure the results chain.
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Score the justification for using proxy indicators. Paste the proxy indicator and its rationale to assess whether the proxy-to-outcome link is defended and bounded.
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Score one or more indicators specifically against the SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound). Paste the indicator(s) to get a per-criterion assessment with revision suggestions.
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Score a focus group discussion guide across five dimensions using AI. Paste your guide to get a scored assessment of question design, structure, ethics, research alignment, and facilitator guidance. Use when reviewing an FGD guide as a standalone document or embedded in an inception report, evaluation design, or needs assessment.
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Score the probe questions in a Focus Group Discussion guide for depth, neutrality, and sequencing. Paste the FGD guide to assess whether probes will surface meaningful insight.
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Score a focus group discussion or key informant interview protocol across five dimensions using AI. Paste your protocol to get a structured assessment of question design, structure, ethics, alignment, and facilitator guidance before fielding.
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Score a key informant interview guide across five dimensions using AI. Paste your guide to get a scored assessment of question design, structure, ethics, research alignment, and interviewer guidance. Use when reviewing a KII guide as a standalone document or embedded in an inception report, evaluation design, or needs assessment.
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Score the question design in a Key Informant Interview guide for depth, openness, and tailoring to informant type. Paste the KII guide to assess whether questions will surface expert insight.
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Score the design of a structured observation checklist for observability, inter-rater consistency, and field-usability. Paste the checklist to assess whether observations will be reliable.
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Score a sampling design across five dimensions using AI. Paste your sampling plan to get a structured assessment of strategy, size, selection, coverage, and replicability before fielding begins.
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Use AI to review a survey questionnaire before field deployment. Get proposed corrections and a prioritized revision list for review and pilot testing.
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Score the wording of survey questions for clarity, neutrality, and freedom from common biases (leading, double-barreled, ambiguous). Paste your survey questions to get a per-question diagnostic.
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Score the response options in a survey for exhaustiveness, mutual exclusivity, and scale design. Paste the survey to assess whether response choices will yield clean, analyzable data.
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Score the quality of a translated survey instrument for fidelity, cultural appropriateness, and back-translation evidence. Paste source and translation to assess equivalence.
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Score an evaluation inception report across five dimensions using AI. Paste your draft to get a structured assessment of refined questions, methodology, tools, analysis plan, and risk/ethics provisions to identify documentation gaps, revision priorities, and conditions requiring accountable review.
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Score the governance and oversight structure for an evaluation. Paste the section to assess whether the management arrangements will sustain the evaluation through delivery.
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Score an evaluation Terms of Reference across five dimensions using AI. Paste your draft ToR to get a structured assessment of scope, questions, methodology, deliverables, and ethics before procurement.
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Score the evaluator selection criteria and scoring matrix in a ToR. Paste the section to assess whether the criteria will identify the right candidate.
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Score the stakeholder engagement plan section of an inception report. Paste the section to assess whether stakeholders will be appropriately engaged across the evaluation.
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Score the workplan section of an evaluation inception report for timeline realism, milestone clarity, and dependency mapping. Paste the workplan to assess whether the evaluation will deliver on time.
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Score whether the budget in an evaluation Terms of Reference is fit for the scope and methods. Paste the budget and scope sections to assess feasibility.
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Score the evaluation questions in a Terms of Reference for scope, answerability, and decision-relevance. Paste the questions section to assess whether they will produce useful findings.
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Score the adaptive management plan that operationalizes how findings flow back to program decisions. Paste the section to assess whether learning will actually drive program change.
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Score the analysis plan section of any document where one is required, including MEL plans, inception reports, evaluation reports, contribution analyses, and needs assessments. Use to confirm the plan maps to questions, names methods specifically, and includes triangulation and quality checks.
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Score the documentation of instrument pilot testing (what was piloted, with whom, what was revised). Paste the pilot report to assess documentation; instrument readiness is a separate evidenced decision.
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Score the methodology limitations disclosure in a report for honesty, completeness, and impact-on-findings explanation. Paste the limitations section to assess credibility.
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Score the methodological rigor of an evaluation, study, or assessment across five dimensions using AI. Use to assess MEL plans, evaluation ToRs, inception reports, evaluation reports, contribution analyses, needs assessments, sampling plans, and any document where methodology choices need justification and quality safeguards.
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Score how qualitative and quantitative data are integrated in a mixed-methods design. Paste the methodology or analysis section to assess whether integration is substantive (not parallel).
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Score a qualitative coding frame (codebook) for structure, mutual exclusivity, and grounded-theory hygiene. Paste the codebook to assess whether it will yield consistent coding.
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Draft, review, and improve a quasi-experimental evaluation with design-specific assumptions, evidence limits, and safeguards against unsupported causal claims.
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Score the sample size and selection rationale in a methodology or sampling plan. Paste the sampling section to assess whether the sample will support the intended analyses. Use this for sample-size justification and selection rationale (analytic purpose, power, method fit, feasibility); for the sampling frame itself (the list or source the sample is drawn from) see sampling-frame-quality.
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Score the sampling frame itself (the list or source from which the sample is drawn) for completeness, currency, and coverage. Separate from sample-size justification. Use this for the frame itself (coverage, currency, cleaning, frame limitations); for the sample-size justification and selection rationale see sample-justification.
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Score the quality of stakeholder engagement across five dimensions using AI. Use to assess MEL plans, evaluation ToRs, inception reports, evaluation reports, learning agendas, contribution analyses, and any deliverable involving stakeholder consultation.
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Score the triangulation approach across methods, sources, and perspectives in an evaluation or analysis plan. Paste the relevant section to assess whether findings will be defensibly cross-corroborated. Use this for the triangulation approach itself (method, source, investigator, conflict resolution, reporting visibility); for per-finding evidentiary review in a report see findings-evidence-strength.
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Create, review and improve a value for money assessment with explicit cost boundaries, credible results, equity tradeoffs and proportionate conclusions.
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Score the financial commentary in a progress report (budget vs actuals plus variance explanation). Paste the narrative section to assess transparency and decision-readiness.
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Score a formal contribution analysis write-up. Paste the section to assess whether contribution claims are appropriately bounded by evidence and counterfactual reasoning.
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Score the charts and data visualizations in a report for chart-type appropriateness, labeling, and integrity. Paste the report (with viz descriptions if no images) to assess whether visuals communicate cleanly.
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Score a donor progress report across five dimensions using AI. Paste your narrative report to get a structured quality assessment with dimension scores, evidence citations, and priority revisions before submission.
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Use AI to score an evaluation report across five quality dimensions. Get a structured verdict with evidence citations and a revision brief before accepting or publishing the report.
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Score the executive summary of an evaluation report for completeness, key-finding clarity, and stand-alone value. Paste the executive summary to assess whether it can serve as a decision-maker's brief.
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Score whether each finding in a report is properly evidenced. Paste the findings section to assess source citation, triangulation, and inference strength per finding. Use this for per-finding evidentiary review in a report; for scoring the triangulation approach itself (method, source, investigator, conflict resolution) see triangulation-strength.
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Score how well findings translate into actionable recommendations across five dimensions using AI. Use to assess evaluation reports, donor progress reports, contribution analyses, learning briefs, monitoring briefs, case studies, and any document where findings should drive recommendations.
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Score the lessons learned section of a report for actionability, generalizability, and evidence basis. Paste the lessons learned to assess whether they will be useful beyond this program.
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Score the methods section of an evaluation report for transparency, completeness, and replicability. Paste the methods section to assess whether a reader could reconstruct the study.
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Score a needs assessment report across five dimensions using AI. Paste your report to get a structured assessment of problem definition, methodology, needs identification, population voice, and recommendations before program design.
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Draft, review, and improve a purpose-specific DQA plan or assessment while separating verified findings, evidence gaps, and operational limits.
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Score the plain language quality and accessibility of any M&E deliverable across five dimensions using AI. Use to assess donor reports, monitoring briefs, learning briefs, adaptive memos, evaluation reports, case studies, and any deliverable communicated to a non-specialist audience.
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Score recommendations in a report for clarity, ownership, and actionability. Paste the recommendations section to assess whether decision-makers can act on them.
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Score the explicit ToC test in the findings narrative. Paste the section to assess whether the theory of change has actually been evaluated, not just used as a planning artifact.
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Score the data storage and privacy practices in a methodology or data management plan. Paste the relevant section to assess whether respondent data will be protected end-to-end.
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Score the do-no-harm risk identification and mitigation protocol for an evaluation, monitoring, or research activity. Paste the protocol to assess coverage of physical, psychological, social, and economic risks. Use this for the substantive risk-and-mitigation analysis; for the organizational machinery (training, reporting channels, escalation chain, audit trail) see safeguarding-procedures.
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Score the ethical standards of an M&E or research deliverable across five dimensions using AI. Use to assess MEL plans, evaluation ToRs, inception reports, surveys, FGD/KII protocols, sampling plans, evaluation reports, case studies, and any deliverable involving human participants or sensitive data.
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Score any M&E deliverable for GESI integration across five dimensions using AI. Use to assess MEL plans, evaluation ToRs, inception reports, indicator frameworks, surveys, FGD/KII protocols, and evaluation reports.
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Score how deeply gender is integrated into an M&E design (beyond disaggregation). Paste the gender-relevant sections to assess whether gender analysis informs methods, interpretation, and use. Use this for the depth of gender as a single analytic lens; for multi-axis identity analysis (gender x disability x age x ethnicity) see intersectionality-analysis.
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Score the informed consent form or process for clarity, comprehension, and voluntariness. Paste the consent script or form to assess whether respondents will truly give informed consent.
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Score whether the M&E design treats identity intersections (gender, disability, age, ethnicity) as distinct analytic categories, beyond single-axis disaggregation. Use this for multi-axis intersectional analysis; for the depth of gender as a single analytic lens (methods, interpretation, use) see gender-integration-depth.
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Score evaluator self-reflection on bias, perspective, and positionality. Paste the relevant section to assess whether qualitative findings are grounded in reflexive practice.
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Score the safeguarding procedures in a research or evaluation design. Goes deeper than do-no-harm: training, escalation chain, response protocol, audit trail. Use this for the organizational machinery of safeguarding; for substantive risk identification and mitigation see do-no-harm-protocol.
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Score the protections for vulnerable populations (children, refugees, survivors, people with disabilities, etc.) in an evaluation or research design. Paste the relevant section to assess safeguards.
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Prioritize answerable questions tied to the decisions an evaluation must support.
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Match evaluation questions to feasible evidence, methods and limitations.
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Draft a source-linked TOR with feasible scope and unresolved decisions visible.
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Define each indicator so people can collect and interpret it consistently.
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Calculate collection workload and compare practical simplification options.
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Build a practical plan and check that definitions, responsibilities and use agree.
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Reconcile reported results and identify which comparisons the evidence supports.
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Turn supported findings into a traceable proposed management response.
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Compare a theory of change, logframe and indicators for gaps and conflicts, keeping source versions visible.
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Draft a sampling plan with visible calculations, or check the sizing and selection of an existing one.
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Plan a risk-based data quality assessment with proposed checks, owners and timing.
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Plan baseline measurement for every indicator, or check a baseline against the framework, without inventing starting values.
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Check results logic, indicators, assumptions and means of verification.
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Examine the results chain, causal assumptions and evidence behind it.
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Assess definitions, SMART and CREAM qualities, disaggregation and measurability.
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Identify gaps and inconsistencies in a measurement and learning plan.
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Review the approach, evaluation arrangements, resources and staffing in a proposal.
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Review commissioning scope, methodological feasibility, ethics and deliverables.
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Check questionnaire quality, structure and data collection feasibility.
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Review the sampling approach, assumptions, frame and calculations.
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Review baseline evidence, indicator coverage and the basis for later comparison.
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Check reported numbers, explanations and evidence-linked conclusions.
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Review data quality evidence, verification and proposed improvement actions.
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Review design, evidence, findings, conclusions and recommendations.
Habilidad
Fuente en inglés
Move from evaluation decisions to questions, methods and a revised TOR.
Flujo de trabajo
Fuente en inglés
Move from indicator review to reference sheets and a feasible M&E plan.
Flujo de trabajo
Fuente en inglés
Move from checked results to a revised report and proposed actions.
Flujo de trabajo
Fuente en inglés
Choose a task, copy one complete stage to your assistant, check the output, and resume from retained artifacts. Claude packages are optional.
Guía
Fuente en inglés
One fictional program taken through evaluation questions, a feasible measurement plan and a report with proposed actions.
Guía
Fuente en inglés
How to credit M&E Studio when you share or adapt these guides and worked files, and how the plugin license differs from the guides.
Guía
Fuente en inglés
The fictional source pack for the worked project: a garden course, its attendance register and its feedback form, used by all three jobs.
Guía
Fuente en inglés
Full hand-written example outputs for the worked project, from questions and matrix to the evidence report. They were not produced by a skill run.
Guía
Fuente en inglés
A short template to fill in when you pause a skill or workflow, so you can resume in a new session from the actual artifacts and decisions so far.
Guía
Fuente en inglés
Existing guided template. Adapt it to the actual task and requirements.
Descargar
Fuente en inglés · docx
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Fuente en inglés · pdf · Ejemplo ficticio o plantilla
Existing guided template. Adapt it to the actual task and requirements.
Descargar
Fuente en inglés · docx
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Fuente en inglés · pdf · Ejemplo ficticio o plantilla
Existing guided template. Adapt it to the actual task and requirements.
Descargar
Fuente en inglés · xlsx
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Fuente en inglés · pdf · Ejemplo ficticio o plantilla
Existing guided template. Adapt it to the actual task and requirements.
Descargar
Fuente en inglés · docx
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Fuente en inglés · pdf · Ejemplo ficticio o plantilla
Existing guided template. Adapt it to the actual task and requirements.
Descargar
Fuente en inglés · docx
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Fuente en inglés · pdf · Ejemplo ficticio o plantilla
Existing guided template. Adapt it to the actual task and requirements.
Descargar
Fuente en inglés · docx
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Fuente en inglés · pdf · Ejemplo ficticio o plantilla
Existing guided template. Adapt it to the actual task and requirements.
Descargar
Fuente en inglés · docx
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Fuente en inglés · pdf · Ejemplo ficticio o plantilla
Existing guided template. Adapt it to the actual task and requirements.
Descargar
Fuente en inglés · docx
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Fuente en inglés · pdf · Ejemplo ficticio o plantilla
Existing guided template. Adapt it to the actual task and requirements.
Descargar
Fuente en inglés · docx
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Fuente en inglés · pdf · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · xlsx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · xlsx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · xlsx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · pdf · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · xlsx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · pdf · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · pdf · Ejemplo ficticio o plantilla
Fictional worked material. Adapt its structure; do not treat example figures as program evidence.
Descargar
Verificar el idioma de origen · docx · Ejemplo ficticio o plantilla
