impact report builder pro

    1

    Turns raw program data into credible, stakeholder-ready impact reports with validated metrics and evidence labels.

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    impact report builder pro

    Example session with this skill installed

    Review our draft Q3 literacy program report. We trained 50 teachers and student scores rose by 15%. I want to claim the training caused the score increase and include 'Jane's' success story.

    • Read your context and instructions
    • Compiled the impact report builder

    I have reviewed the Q3 Literacy Program draft. While the activity and output data are clear, the causal link between teacher training and student scores requires more calibration due to missing baseline data.

    Impact Review: Q3 Literacy Program

    MetricValueClassificationSource / PeriodValidation Note
    Teachers Trained50Verified QuantitativeQ3 Training LogsMatches attendance records.
    Student Score Increase15%ReportedEnd-of-term SurveyMissing baseline; cannot verify delta.
    Unique Reach1,200 studentsEstimatedClassroom EnrollmentPotential overlap between cohorts.

    Attribution Assessment

    The claim that training "caused" the 15% increase is currently

    Weak. Without a baseline or control group, the improvement could be associated with seasonal curriculum shifts or other school interventions.
    Recommended Phrasing: "Student scores improved by 15% following the teacher training program; available data shows a positive association, though external factors were not controlled."

    Stakeholder Evidence: 'Jane'

    • Status: Blocked from Publication.
    • Reason: No documented consent provided in the input.

    Action: Please confirm if informed consent covers the use of Jane's real name and specific demographic details. Until then, I have drafted a pseudonymized version for internal review only.

    Evidence Gaps

    1. Missing Baseline: Q2 scores were not provided to calculate the 15% growth formula.
    2. Attribution: No data on concurrent literacy initiatives in the same district.

    Next steps

    1. Provide the Q2 student score raw data to validate the percentage increase.
    2. Confirm the consent status for the 'Jane' case study.
    3. Update the executive summary to reflect the "Association" rather than "Causality" regarding student outcomes.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    Program reports often suffer from "impact inflation," where activity counts are confused for actual change and causal claims lack evidence. Stakeholders struggle to trust narratives that hide uncertainty or ignore missing baselines.

    What it does

    • Separates activities and outputs from genuine outcomes to prevent causal overreach.
    • Performs numerical validation on totals, percentages, and denominators to ensure data consistency.
    • Classifies evidence quality by source, distinguishing between verified data, estimates, and anecdotes.
    • Reviews stakeholder stories for consent, privacy risks, and representative accuracy.
    • Generates structured reports including executive summaries, evidence-gap registers, and future priorities.

    Why this beats prompting it yourself

    General-purpose LLMs often default to promotional, optimistic tones that undermine reporting credibility. This skill enforces strict evidence classification and attribution modeling, ensuring your reporting stands up to audit and rigorous donor scrutiny.

    Use cases

    • Converting raw monitoring and evaluation (M&E) data into donor-ready annual reports.
    • Auditing existing impact narratives for weak attribution or inconsistent metrics.
    • Drafting learning reports that highlight program failures and evidence gaps for internal strategy.
    • Creating public-facing impact pages backed by a source-verified evidence register.

    Known limitations

    Cannot independently verify the truth of input data. Requires explicit consent status for stakeholder stories to approve publication.

    Cannot independently verify the truth of input data. Requires explicit consent status for stakeholder stories to approve publication.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    What's inside

    Frequently Asked Questions

    The skill uses a structured validation framework to separate simple activity counts from genuine outcomes, flagging claims that lack sufficient baseline data or causal evidence to prevent "impact inflation."

    You will receive the Impact Report Builder Pro skill file, a template for structuring your raw M&E data, and a guide on interpreting the generated evidence-gap registers.

    The skill is designed for use with leading LLM platforms (such as ChatGPT, Claude, or Gemini) that support custom system instructions or agent builders; simply import the provided configuration to get started.

    While the skill performs rigorous internal consistency checks on your totals and percentages, it cannot verify the external truth of your raw input; it relies on the data you provide being accurate at the source.

    It classifies data into verified metrics, estimates, and anecdotes, ensuring that qualitative stakeholder stories are handled with privacy-risk reviews and consent-status checks.

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