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Featured Prompts

The deliverables most M&E work begins from.See all 233 prompts

Create a Theory of Change

Build a theory of change showing how your program activities lead to outcomes and impact, with assumptions and risks identified.

has a scoring rubric

Personalize, Optional

Create a Logframe

Build a logical framework with goals, outcomes, outputs, activities, indicators, means of verification, and assumptions.

has a scoring rubric

Personalize, Optional

Create SMART Indicators

Generate specific, measurable, achievable, relevant, and time-bound indicators from your program objectives.

Personalize, Optional

Draft a Progress Report

Write a structured progress report for your donor with activities, results, challenges, and next steps.

has a scoring rubric

Personalize, Optional

Design a Household Survey

Create a structured survey questionnaire for household or beneficiary data collection.

has a scoring rubric

Personalize, Optional

Create an Evaluation Matrix

Build an evaluation matrix linking evaluation questions to criteria, indicators, data sources, and methods.

Personalize, Optional

All Prompts

Planning a Program

Create a Theory of Change: Build a theory of change showing how your program activities lead to outcomes and impact, with assumptions and risks identified.
Create a Logframe: Build a logical framework with goals, outcomes, outputs, activities, indicators, means of verification, and assumptions.
Create a MEL Plan: Develop a monitoring, evaluation, and learning plan with indicators, data collection methods, timelines, and responsibilities.
Create a Data Use Plan: Map out who needs what data, when, and how it will inform decisions at each level of your program.
Create a Stakeholder Map: Identify and categorize stakeholders by their interest, influence, and role in your M&E system.
Create an M&E Budget: Build a justified M&E budget with line items for data collection, staffing, analysis, and reporting.
Create a Results Framework: Build a multi-level results chain showing how inputs lead to activities, outputs, outcomes, and impact.
Draft an M&E Section for a Proposal: Write the monitoring and evaluation section of a funding proposal with approach, indicators, and budget.
Review My Logframe: Get structured feedback on your logframe's logic, indicator quality, assumptions, and completeness.
Review My MEL Plan: Get feedback on your MEL plan's completeness, feasibility, indicator quality, and alignment with program design.
Review My Theory of Change: Get feedback on your ToC's causal logic, assumptions, evidence base, and completeness.
Review My Proposal M&E Section: Get feedback on the M&E section of your proposal for technical quality, completeness, and donor alignment.
Design a Baseline Study: Plan your program's baseline data collection : what to measure, how, and what tools you need.
Protection Mainstreaming Monitoring Framework: Create a protection mainstreaming monitoring framework with safeguarding indicators, complaint mechanism tracking, and protection risk analysis tools for humanitarian or development programs.
Locally-Led M&E Plan: Create a locally-led M&E plan that centers community ownership of data, indicators, and evaluation processes, aligned with Grand Bargain localization commitments.
Design an Outcome Mapping Framework: Design an Outcome Mapping framework with boundary partners, progress markers, outcome journals, and strategy maps following the IDRC methodology.
Create a Stakeholder Engagement Plan: Build a stakeholder engagement plan for an evaluation: influence-interest mapping, tailored engagement methods, communication channels, and an implementation timeline.
Design a Monitoring Dashboard Brief: Design a monitoring dashboard specification with KPIs, visualization types, update frequencies, user roles, and data flow architecture.
Create an Education M&E Framework: 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.
Create a WASH M&E Framework: Design an M&E framework for a WASH program using JMP service ladders, water quality monitoring protocols, and sustainability indicators.
Create a Food Security and Livelihoods M&E Framework: Draft a food security and livelihoods monitoring framework using named instruments, justified sampling and explicit links to verified official IPC or FEWS NET context.
Create a Protection M&E Framework: Design an M&E framework for a protection program covering child protection, GBV prevention and response, and psychosocial support, aligned to IASC and GPC standards with strong data protection protocols.
Extract Donor M&E Requirements from an RFP: 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.
M&E Go/No-Go Assessment: 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.
Competitive M&E Analysis for a Proposal: 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.
Design an M&E Capture Plan for a Proposal: 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.
Draft an M&E Staffing Plan: 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.
Draft an M&E Budget Narrative: Draft an activity-based M&E budget narrative using supplied quantities, rates, and proposal requirements, with unpriced items and assumptions made clear.
Draft a Theory of Change from a Program Narrative: 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.
Generate a Logframe from an Existing Theory of Change: 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.
Build a 48-Hour Proposal M&E Workflow: 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.
Red-Team a Draft Proposal M&E Section: 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.
Review M&E Budget Adequacy: 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.
Review a MEL Plan Draft: Review a draft MEL plan for completeness and operational readiness.
Review Logframe Quality: Review a draft logframe for SMART indicators, assumptions, and coherence.
Design a Results Framework: Design a results framework with outputs, outcomes, and impact statements.
Review a Stakeholder Engagement Approach: Review the stakeholder engagement approach in a MEL plan or evaluation methodology for identification, mode specificity, timing, feedback, and accountability.
Review a MEL Data Flow Diagram: Review the data flow diagram inside a MEL plan for completeness, role clarity, system specification, quality gates, and feasibility.
Review ToC Assumptions: Review the assumptions section of a Theory of Change for explicit statement, causal-link coverage, plausibility, testability, and prioritization.
Review ToC Pathway Completeness: Review whether a Theory of Change pathway covers full causal logic from inputs to impact without gaps.
Review ToC Causal Logic: Review the causal logic of a Theory of Change for plausibility, evidence base, and link integrity.
Review a MEL Roles and Responsibilities Matrix: Review the roles and responsibilities matrix inside a MEL plan for function coverage, position specificity, RACI discipline, escalation path, and staffing feasibility.
Review a Logframe Assumptions Column: Review the assumptions column of a logframe for coverage of levels, specificity, causal reasoning, testability, and risk differentiation.

Indicators

Create SMART Indicators: Generate specific, measurable, achievable, relevant, and time-bound indicators from your program objectives.
Create an Indicator Reference Sheet: Document how each indicator is defined, measured, disaggregated, and reported : with baselines and targets.
Review My Indicators: Get each indicator assessed for SMART criteria, measurability, and alignment with your results framework.
Help Me Choose Indicators: Describe your program objectives and get suggestions for appropriate indicators to track progress.
Create a Health M&E Framework: 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.
Review an Indicator Set: Review a set of indicators for SMART criteria and definition clarity.
Review Indicators Against SMART: Score indicators specifically against SMART (Specific / Measurable / Achievable / Relevant / Time-bound) with per-criterion feedback.
Review Baselines and Targets: Review baseline values and targets for documentation, justification, plausibility, ambition calibration, and milestone coherence.
Review an Indicator Statement: Review the wording of an indicator statement (subject, action, unit, comparator, brevity).
Review Indicator Level Classification: Review whether indicators are correctly classified at output, outcome, or impact levels with attribution-distance and time-frame checks.
Review a Proxy Indicator Justification: Review the justification for a proxy indicator (rationale, link defense, bounded validity, triangulation, limitations).

Collecting Data

Create a Beneficiary Feedback System: Design channels for collecting, processing, and responding to feedback from the people your program serves.
Review My Data Collection Tool: Get feedback on your survey, interview guide, or checklist for clarity, bias, skip logic, and completeness.
Design a Household Survey: Create a structured survey questionnaire for household or beneficiary data collection.
Design an Interview Guide: Create a semi-structured interview guide with opening, probing, and closing questions.
Design a Focus Group Guide: Create a focus group discussion guide with icebreakers, key questions, probes, and facilitation notes.
Design an Observation Checklist: Create a structured checklist for field observations with clear criteria and rating scales.
Design a Sampling Strategy: Determine how to select your study sample : method, size, stratification, and practical considerations.
Design a Rapid Assessment: Plan a proportionate assessment around an urgent decision, the actual deadline, available sources, access constraints, and ethical requirements.
Disability-Inclusive Survey Design: Design disability-inclusive data collection tools using the Washington Group Questions, with guidance on ethical protocols, accessible survey administration, and disability-disaggregated analysis.
Beneficiary Accountability Framework: Create a beneficiary accountability framework with feedback loops, complaint mechanisms, response protocols, and community participation structures aligned with the Core Humanitarian Standard.
Create a Data Quality Audit Protocol: Create a data quality audit protocol with verification procedures, sampling strategy, scoring rubric, and corrective action framework for assessing M&E data reliability.
Create an Enumerator Training Plan: Create an enumerator training agenda and manual outline for a data collection exercise, covering methodology, tools, ethics, practice sessions, and field protocols.
Create a Field Supervision Checklist: Create a field supervision checklist and data quality spot-check protocol for monitoring enumerator performance and data integrity during data collection.
Design a KoboToolbox/ODK Survey Form: Design a structured KoboToolbox or ODK-compatible survey form with question types, skip logic, validation rules, calculation fields, and deployment guidance.
Create a Mobile Data Collection Protocol: Create a mobile and digital data collection protocol covering device management, data syncing, offline procedures, quality checks, and troubleshooting.
Create a Remote Monitoring Plan: 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.
Design a Health Household Survey: Design a health household survey instrument using validated question modules for immunization, nutrition, WASH, and maternal health, with sampling strategy and field protocols.
Design a Learning Assessment Tool: Design a learning assessment tool for literacy and numeracy with grade-level benchmarks, scoring rubrics, administration protocols, and analysis specifications aligned to EGRA/EGMA methodology.
Design a WASH Household Survey: Design a WASH household survey using JMP and WHO standard question modules for water service levels, sanitation access, hygiene practices, and water quality perceptions.
Review Data Quality: Review a dataset or data collection process for quality issues across five dimensions: completeness, accuracy, consistency, timeliness, and validity.
Draft a Data Collection Plan: 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.
Build an Operational Learning Loop: Build a learning loop into routine program operations.
Review a Survey Instrument: Review a draft survey instrument before field deployment.
Design a Sampling Plan: Design a sampling plan with frame, method, size, and selection criteria.
Design a Monitoring System: Design an end-to-end monitoring system for a program.
Design a Data Flow Diagram: Design a data flow diagram for a MEL plan, showing who collects, checks, stores and uses each source of program information.
Review a Sample Justification: Review the sample size and selection rationale in a methodology document for analytic-purpose match, statistical/qualitative justification, and feasibility.
Review a Sampling Frame: Review the sampling frame (source, coverage, currency, cleaning, limitations) before drawing a sample.
Design an Instrument Pilot Test: Design a pilot test for a survey, KII guide, FGD guide, or observation checklist, including participant scope, issue catalog, revision protocol, and field-readiness criteria.
Review Indicator Sources and Methods: Review the means of verification (data source + collection method) named per indicator for specificity, fit, feasibility, and frequency.
Review Survey Question Wording: Review survey questions for clarity, neutrality, single-concept, specificity, and reading-level fit.
Review Survey Response Options: Review response options (Likert scales, response categories) for exhaustiveness, mutual exclusivity, balance, clarity, and don't-know handling.
Review Survey Translation Quality: Review a translated survey for semantic fidelity, cultural adaptation, back-translation evidence, response-option consistency, and field-testing.
Review an FGD Guide: Review a focus group discussion guide for structure, opening/closing, probe quality, sensitivity handling, and facilitator notes.
Review FGD Probes: Review probe questions in a focus group discussion guide for depth, neutrality, sequencing, coverage, and facilitation guidance.
Review a KII Guide: Review a key informant interview guide for structure, question quality, sensitivity, sequencing, and facilitator notes.
Review KII Question Design: Review the question design in a key informant interview guide for openness, depth trajectory, informant tailoring, specificity, and time discipline.
Review an FGD/KII Protocol: Review the protocol scaffolding (consent, recording, transcription, data handling) for FGD or KII data collection.
Review an Observation Checklist: Review a structured observation checklist for observability, inter-rater clarity, field usability, coverage, and open-notes space.
Review a Logframe Means of Verification Column: Review the means of verification column of a logframe for source specificity, method appropriateness, frequency, responsibility, and feasibility.

Analysis and Evaluation

Create an Evaluation Matrix: Build an evaluation matrix linking evaluation questions to criteria, indicators, data sources, and methods.
Draft Evaluation Terms of Reference: Write terms of reference for commissioning an external evaluation, including scope, questions, and methodology.
Review My Evaluation Design: Get feedback on your evaluation methodology, questions, sampling, and analysis plan before fieldwork.
Analyze Survey Data: Get help calculating frequencies, means, cross-tabulations, and interpreting patterns in your survey results.
Analyze Interview Transcripts: Get help identifying themes, extracting key quotes, and summarizing findings from interview or FGD transcripts.
Code Qualitative Data: Create a codebook and systematically code interview or focus group data into themes and sub-themes.
Extract Key Findings from a Document: Pull out the main findings, data points, and recommendations from a report or evaluation.
Compare Baseline vs Endline Results: Analyze changes between baseline and endline data, identify significant shifts, and interpret what they mean.
Identify Patterns Across Datasets: Find trends, outliers, and patterns across multiple data sources or reporting periods.
Design a Mid-Term Evaluation: Plan a mid-term evaluation to check program progress, relevance, and early outcomes.
Compare Evaluation Approaches: Understand the differences between evaluation methodologies and when to use each one.
Walk Me Through Data Analysis: Get step-by-step guidance on how to analyze your specific type of M&E data.
Create a Most Significant Change Guide: Create a Most Significant Change story collection and selection guide with interview protocols, selection criteria, and analysis methods.
Conflict Sensitivity Analysis of Monitoring Data: 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.
Value for Money Assessment Framework (4Es): Create a Value for Money (VfM) assessment framework using the 4Es model (Economy, Efficiency, Effectiveness, Equity), with indicators, benchmarks, and reporting templates.
Cost-Effectiveness Analysis Across Interventions: Analyze program cost-effectiveness by comparing unit costs across interventions, sites, or time periods, with benchmarking against sector standards and actionable efficiency recommendations.
Create a Contribution Analysis Framework: Build a contribution analysis framework with contribution claims, evidence assessment criteria, and systematic testing of alternative explanations for observed outcomes.
Create a Most Significant Change Protocol: Build a complete Most Significant Change (MSC) data collection and selection protocol, including story collection guides, selection criteria, and domain definitions.
Create an Outcome Harvesting Protocol: Build an Outcome Harvesting protocol with outcome description templates, substantiation questions, verification procedures, and analysis frameworks.
Design a Quasi-Experimental Evaluation: Design a quasi-experimental evaluation with matching strategy, comparison group selection, difference-in-differences analysis plan, and threats to validity assessment.
Analyze Qualitative Data with Structured Coding: 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.
Design a Participatory Evaluation: Design a participatory evaluation approach that engages beneficiaries and stakeholders as co-evaluators, with inclusive methods, power analysis, and capacity building components.
Analyze Data Using Realist Evaluation: 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.
Create a Process Tracing Protocol: Create a process tracing protocol for causal inference in single-case or small-n evaluations, with hypothesis formulation, evidence tests, and Bayesian confidence updating.
Design a Mixed-Methods Evaluation: Design a mixed-methods evaluation with integration points, sequencing decisions, a methods matrix, and a plan for combining quantitative and qualitative strands.
Create an Evaluability Assessment: Create an evaluability assessment to determine if a program is ready for evaluation, examining program design clarity, data availability, stakeholder readiness, and feasibility constraints.
Draft an Evaluation Inception Report: Draft a structured evaluation inception report outline covering methodology, workplan, evaluation matrix, risk assessment, team composition, and logistics.
Draft an M&E Scope of Work: Draft a professional Scope of Work for hiring an M&E consultant or evaluation firm, including background, deliverables, qualifications, timeline, and evaluation criteria.
Create an Evaluation Proposal Scoring Matrix: Create a technical scoring matrix for evaluating proposals from evaluation firms or M&E consultants, with weighted criteria, scoring scales, and consensus procedures.
Analyze Health Routine Data: Analyze HMIS or DHIS2 routine health data to identify trends, coverage gaps, performance outliers, and actionable recommendations for program improvement.
Analyze Food Security Monitoring Data: Describe food security monitoring scores, trends and uncertainty; distinguish local evidence synthesis from verified official IPC classifications and identify evidence gaps.
Choose the Right Evaluation Approach: Compare evaluation approaches and get a recommendation for which method best fits your program context, resources, and evidence needs.
Synthesize Lessons Across Projects: Synthesize learning across a portfolio of projects.
Evaluate Uptake of Prior Learning: Evaluate whether prior lessons have been integrated into current practice.
Document a Program Failure: Document and structure learning from a program that did not meet its targets.
Review an Evaluation Report Draft: Review an evaluation report draft for quality, evidence, and recommendations.
Review ToR Readiness: Review an evaluation Terms of Reference before issuing.
Review an Inception Report: Review an evaluation inception report before approving.
Review Findings for Evidence Strength: Review the findings section of a report for evidence quality.
Design an Evaluation Matrix: Design an evaluation matrix linking questions to methods, sources, and analysis.
Review a Triangulation Approach: Review the triangulation approach in an evaluation methodology or analysis plan, covering method, source, investigator, conflict-resolution, and reporting visibility.
Review an Analysis Plan: Review a data analysis plan for question alignment, method specificity, integration plan, and validation procedures.
Review a Mixed-Methods Integration: Review the mixed-methods integration section of a design for stage, strategy, sequencing logic, joint-display plan, and conflict handling.
Draft a Qualitative Coding Frame: Draft a qualitative coding frame (codebook) with code definitions, hierarchy, examples, and a reliability plan.
Review a Composite Indicator: Review the design of a composite or index indicator (component selection, weighting, aggregation, missing-data, interpretability).
Review ToR Evaluation Questions: Review the evaluation questions section of a ToR for scope coherence, answerability, decision relevance, criterion coverage, and right-sizing.
Review Evaluation Management Arrangements: Review the governance and oversight structure for an evaluation (steering committee, reference group, decision rights, COI, escalation).
Review an Inception Stakeholder Engagement Plan: Review the stakeholder engagement plan in an inception report for identification, mode specificity, timing, feedback, and accountability.
Design Evaluator Selection Criteria: Design the evaluator selection criteria and scoring matrix for an evaluation ToR (expertise specificity, experience calibration, scoring transparency, diversity, fairness).
Review an Inception Workplan: Review the workplan section of an evaluation inception report for phase decomposition, milestone definition, dependency mapping, buffer time, and resource loading.

Reporting

Draft a Progress Report: Write a structured progress report for your donor with activities, results, challenges, and next steps.
Draft an Evaluation Report: Write up evaluation findings into a professional report with methodology, results, and recommendations.
Draft an Executive Summary: Summarize a longer M&E document for decision-makers, preserving its verified content, caveats, and priorities within the required length.
Draft a Case Study: Write a structured case study documenting a program success, challenge, or lesson learned.
Draft a Communication Plan for Findings: Plan how to share M&E findings with different audiences : donors, staff, communities, and partners.
Review My Report Draft: Get structured feedback on a report draft for clarity, evidence, logic, and actionable recommendations.
Summarize a Long Report: Get a structured summary of a lengthy M&E document : key findings, recommendations, and action items.
Create a Dissemination Strategy: Design an evaluation findings dissemination strategy with audience-specific products, communication channels, and a timeline for maximizing evaluation use.
Create a Data Storytelling Brief: Translate M&E findings into a structured data storytelling brief with a narrative arc, key messages, supporting data points, and audience-specific framing.
Draft a Findings Presentation Deck Outline: Draft a structured outline for an M&E findings presentation deck with slide-by-slide content, visualization recommendations, and speaker notes.
Create M&E Infographic Content: Create structured content for an M&E infographic with headline stats, supporting data points, visual hierarchy, and layout guidance organized for a designer.
Draft a Stakeholder Brief: Draft a concise stakeholder brief from supplied findings, with clear limitations and recommendations suited to its audience and required format.
Analyze Chart Selection for M&E Data: Analyze a dataset description and recommend the best chart types for each variable or comparison, with justification grounded in data visualization best practices.
Create a Results Snapshot: Create a one-page results snapshot (factsheet) with headline indicators, progress-to-target tracking, and key achievements for a reporting period.
Draft a Community Feedback Report: Draft a simplified community feedback report that presents M&E findings in plain language for beneficiary communities, with visual aids and actionable next steps.
Distill Policy-Relevant Insights: Distill policy-relevant insights from evaluation findings.
Package Lessons by Audience: Re-package lessons for different audiences (donor, peer, internal).
Review a Donor Progress Report: Review a donor progress report for clear claims, consistent data and compliance with reporting requirements before submission.
Review Recommendations for Actionability: Review recommendations in a report for clarity, ownership, and feasibility.
Review a Limitations Disclosure: Review the limitations section of an evaluation report or study for completeness, method-specificity, and impact-on-findings explanation.
Review Donor Indicator Alignment: Review whether an indicator set aligns with the required donor framework (coverage, mapping, definition alignment, disaggregation, reporting readiness).
Review an Executive Summary: Review an executive summary of an evaluation or research report for stand-alone value, key-finding salience, coverage, plain language, and length discipline.
Review a Findings Section: Review the findings section of an evaluation, monitoring, or research report for source citation, triangulation visibility, qual-quant integration, inference strength, and contrary evidence.
Review a Recommendations Section: Review the recommendations section of a report for specificity, ownership, timeframe, resource realism, and finding linkage.
Review a Methods Section: Review the methods section of an evaluation or research report for design justification, data collection detail, analysis approach, QA, and reproducibility.
Review Findings-to-Recommendations Linkage: Review the traceability and logic from findings to recommendations in a report.
Review a Needs Assessment Report: Review a needs assessment report for scope coverage, methods, data quality, prioritization logic, and use-readiness.
Review Plain-Language Accessibility: Review a report (or section) for plain-language accessibility: reading level, jargon, structure, visuals, and audience fit.
Review a Theory-Based Evaluation Write-Up: Review the theory-based evaluation findings (ToC restatement, per-link evidence, assumption verification, alternatives, ToC revision recs).
Review a Contribution Analysis: Review a contribution analysis section for story construction, counterfactual reasoning, co-factor acknowledgment, calibrated language, and reader verifiability.
Review a ToR Budget: Review the budget in an evaluation ToR against scope and methods for cost alignment, breakdown specificity, field-cost realism, hidden costs, and contingency.
Review a Budget vs Actuals Narrative: Review the financial commentary in a progress report (budget vs actuals plus variance explanation, burn rate, reallocation logic).
Review Data Visualization Quality: Review the charts and figures in a report for chart-type appropriateness, labeling completeness, honest scaling, accessibility, and caption quality.

Ethics and Data Protection

Do No Harm Review of M&E Tools: Review an M&E plan or data collection tool for potential harm, ethical risks, safeguarding gaps, and unintended negative consequences for participants and communities.
Create a Data Management Plan: Create a data management plan covering collection protocols, storage, cleaning, analysis workflows, archiving, ethical safeguards, and data sharing agreements.
Create an Ethics Review / IRB Submission: Create an ethics review or Institutional Review Board submission package with protocol summary, risk assessment, informed consent forms, and data protection measures.
Create an Informed Consent Form: 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.
Create a Data Protection Impact Assessment: 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.
Review Ethical Compliance of a Data Collection Plan: 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.
Review Ethical Standards: Review the ethical standards in a methodology, evaluation design, or research protocol.
Review Reflexivity and Positionality: Review evaluator self-reflection on bias, perspective, and positionality in qualitative methods.
Review Vulnerable Population Protections: Review the protections for vulnerable populations in an evaluation or research design.
Review Data Storage and Privacy Practices: Review data storage and privacy practices in a methodology or data management plan.
Draft a Do-No-Harm Protocol: Draft a do-no-harm risk identification and mitigation protocol for an evaluation or research activity.
Draft an Informed Consent Form: Draft an informed consent form or script with information completeness, comprehension aids, voluntariness, confidentiality, and special-population accommodations.
Draft Safeguarding Procedures: Draft safeguarding procedures including training, reporting channels, escalation chain, response protocols, and audit trail.

Gender and Inclusion

Conduct a Gender Analysis: Analyze your M&E data through a gender lens : disaggregation, differential impacts, and inclusion gaps.
GESI Analysis Framework for M&E: Create a Gender Equality and Social Inclusion (GESI) analysis framework that integrates into your M&E system, with GESI-sensitive indicators, data collection guidance, and reporting requirements.
Intersectional Data Analysis: 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.
Review a Disaggregation Plan: Review a disaggregation plan across the data pipeline (planning, collection, analysis, reporting, use).
Review a GESI-Responsive Design: Review the gender equality and social inclusion treatment in a MEL plan, evaluation methodology, or report.
Review Gender Integration Depth: Review how deeply gender is integrated into an M&E design beyond sex-disaggregation.
Review Intersectionality in M&E Design: Review whether an M&E design treats identity intersections (gender, disability, age, ethnicity) as distinct analytic categories.

Climate

Environmental and Climate Screening Checklist: Create an environmental and climate screening checklist for program monitoring, identifying environmental risks, mitigation measures, and compliance requirements across program activities.
Climate Indicators and Risk Monitoring Framework: Create climate-sensitive indicators and a climate risk monitoring framework that tracks both program contributions to climate resilience and exposure to climate-related risks.

Learning

Create a Learning Agenda: Develop learning questions, methods, and a plan for how your program will capture and use lessons.
Draft Lessons Learned: Capture and organize lessons from program implementation into a structured document for future use.
Explain an M&E Concept: Get a clear, jargon-free explanation of any M&E concept : from theory of change to contribution analysis.
Suggest the Right Methodology: Describe your program and constraints, and get recommendations for the best evaluation approach.
Help Me Understand Contribution Analysis: Walk through the six steps of contribution analysis to build a credible case for your program's contribution to observed changes, without needing a control group.
Create an Adaptive Management Plan: Create an adaptive management plan with decision triggers, pivot criteria, and learning cycles for your program.
Create an After-Action Review Template: Create an After-Action Review template for program activities or events, with structured reflection questions and documentation formats.
Draft a Pause and Reflect Session Guide: Draft a Pause and Reflect session facilitation guide with discussion prompts, data review activities, and adaptation planning.
Create a Learning Brief: Create a learning brief that synthesizes evidence from multiple M&E sources into actionable recommendations for program decision-makers.
Analyze a Pivot Decision: Analyze program data to recommend whether to continue, adapt, or stop an intervention based on evidence and decision criteria.
Create a Knowledge Product: Create a knowledge product (evidence brief, learning note, or practice paper) from program data to share lessons with internal and external audiences.
Draft a Lessons Learned Database Entry: Draft a structured lessons learned database entry with context, lesson, evidence, and recommendations for organizational knowledge systems.
Design a Learning Event: Design a learning event agenda (workshop, webinar, or community of practice session) based on M&E findings to promote evidence-based practice.
Create an Evaluation Utilization Plan: Create an evaluation utilization plan that maps findings to decisions, audiences, and communication channels to ensure evidence drives action.
Create a Developmental Evaluation Design: Create a Developmental Evaluation design for innovation or complex programming contexts, with real-time feedback loops, emergent learning questions, and adaptive evaluation architecture.
Create an Evidence Brief: Create a structured evidence brief that synthesizes findings across multiple evaluations or studies on a specific M&E topic, with strength-of-evidence ratings.
Design a Real-Time Monitoring System: Design a real-time monitoring system with automated alerts, threshold triggers, escalation protocols, and dashboard specifications for timely program decision-making.
Capture Project Lessons: Capture lessons learned at a project closure or major milestone.
Design an After-Action Review: Design and structure an after-action review for a completed activity.
Generate a Learning Agenda: Generate a learning agenda for a multi-year program.
Facilitate a Reflection Workshop: Design a facilitation guide for a team reflection workshop.
Design a Knowledge Management Strategy: Design a KM strategy for an organization or program.
Design a Pause-and-Reflect Session: Design a structured pause-and-reflect session for a program team.
Draft an Adaptive Management Plan: Draft an adaptive management plan covering trigger conditions, decision routes, adaptation mechanisms, memory, and donor communication.
Review a MEL Learning Agenda: Review the learning questions section of a MEL plan for strategic relevance, answerability, decision linkage, scope, and operationalization.
Review Decision-Linked Measurement: Review whether indicators in a MEL plan are linked to specific decisions and learning needs.

Professional Development

Create an M&E Capacity Assessment Tool: Create an M&E capacity assessment tool that evaluates organizational and individual M&E competencies across key domains, with scoring rubrics and gap analysis.
Create an M&E Capacity Building Plan: Create an M&E capacity building plan with learning objectives, activities, timeline, responsible parties, and success indicators linked to assessment findings.
Create an M&E Training Curriculum: Create a multi-day M&E training curriculum with detailed session plans, learning objectives, interactive exercises, and a complete materials list.
Design an M&E Mentoring Framework: Design an M&E mentoring and coaching framework with competency milestones, structured session plans, progress tracking tools, and matching criteria.
Create a Partner M&E Assessment: Create a partner M&E capacity assessment and strengthening plan for sub-grantees or implementing partners, with risk-based tiering and tailored support packages.
Draft a Training Facilitation Guide: Draft a facilitation guide for an M&E training session including icebreakers, group exercises, case studies, debrief questions, and time management tips.
Create an M&E Competency Framework: Create an M&E staff competency framework with job families, proficiency levels, behavioral indicators, and development pathways for career progression.
Design an M&E Community of Practice: Design a Community of Practice structure for M&E practitioners with meeting cadence, knowledge sharing protocols, engagement strategies, and sustainability plan.