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Outcome indicator validator

Validates a proposed outcome indicator against SMART criteria and attribution/contribution standards, scoring each dimension, flagging specific weaknesses, and delivering rewritten indicator options plus a data…

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Purpose

Assess a single proposed outcome indicator against SMART criteria and attribution/contribution logic, then return a scored diagnosis, flagged weaknesses, and two to three rewritten alternatives that are measurable in the field. Solves the common problem of indicators that read well in a proposal but cannot actually be measured, attributed, or reported against.

When to Use

Invoke this skill when the user asks to:

  • Check whether a proposed indicator is SMART or well-formed
  • Validate or approve indicators for a logframe, results framework, or M&E plan
  • Fix an indicator that's too vague, too broad, or unmeasurable
  • Assess whether an outcome can be attributed to the program or only contributed to
  • Review a partner's or grantee's proposed indicators before sign-off
  • Convert an activity/output-level metric into a genuine outcome indicator

Inputs Required

Before starting, collect:

  • Proposed indicator: The exact indicator statement as currently written
  • Outcome it measures: The result-level change the indicator is meant to track
  • Program context: What the intervention does and for whom
  • Target population: Who the change is expected to occur in
  • Timeframe: Program duration and reporting cadence
  • Data reality: What data sources, baseline, and collection capacity exist
  • Contribution vs. attribution expectation: Whether the funder expects the program to be the sole cause or one of several contributors

If contribution vs. attribution expectation is not provided, default to a contribution framing and note the assumption.

Instructions

Step 1: Classify the Indicator Level

Confirm the indicator is actually at the outcome level before assessing anything else.

  • Output indicator (misclassified): Counts what the program delivered — trainings held, materials distributed, people reached
  • Outcome indicator (correct): Measures a change in behavior, condition, knowledge, or status in the target population
  • Impact indicator (too high): Measures long-term, population-wide change beyond the program's reasonable horizon

If the indicator is an output disguised as an outcome, flag this first — it overrides all other scoring — and note that SMART fixes won't resolve a level mismatch.

Step 2: Score Against SMART Criteria

Rate each dimension Strong / Adequate / Weak with a one-line justification.

  • Specific: Does it name a single, unambiguous change? Flag compound indicators ("increased knowledge and behavior") — these must be split.
  • Measurable: Is there a clear unit (%, count, rate, score)? Flag adjectives without measurement ("improved," "strengthened," "enhanced") that lack a defined metric.
  • Achievable: Is the expected magnitude plausible given the intervention size, dose, and timeframe? Flag targets that imply unrealistic effect sizes.
  • Relevant: Does it directly reflect the stated outcome — not a proxy so distant it measures something else? Flag proxy drift.
  • Time-bound: Does it specify by when and at what reporting interval? Flag missing baselines and endlines.

Step 3: Assess Attribution vs. Contribution

Determine whether the indicator can credibly be linked to the program.

  • Attribution feasibility: Can change in this indicator be causally isolated to the program? Requires comparison/control or strong counterfactual logic. Rare in complex programs.
  • Confounding risk: List the external factors (other actors, policy shifts, economic conditions) that could move the indicator independently.
  • Contribution framing: If attribution isn't feasible, restate the indicator so it honestly claims contribution — pairing it with a contribution narrative or process tracing rather than sole-cause language.
  • Directionality trap: Flag indicators where an external shock could move the number the "right" way for the wrong reasons.

Step 4: Check Data Collection Feasibility

An indicator is only as good as the data behind it.

  • Source: Is there a named, realistic data source (survey, admin records, direct observation)?
  • Baseline: Does a baseline exist or can one be established before activities begin?
  • Frequency & cost: Can it be collected at the reporting cadence without disproportionate burden?
  • Disaggregation: Can it be broken out by relevant subgroups (sex, age, geography) if required?

Flag any indicator that scores Strong on SMART but is infeasible to measure — this is the most common failure mode.

Step 5: Deliver Verdict and Rewrites

Give an overall verdict: Approve as-is / Approve with revision / Rewrite required. Then provide 2–3 rewritten indicator options, each with:

  1. The full revised indicator statement (with unit, target logic, and timeframe)
  2. What it fixes relative to the original
  3. Its attribution/contribution stance
  4. The data source that makes it measurable

Be opinionated — recommend one option and say why.

Output Format

  • Structure: Level classification → SMART scorecard (table) → attribution/contribution assessment → data feasibility check → verdict and 2–3 rewrites
  • Length: 700–1,000 words
  • Format: Structured document with a SMART scorecard table and labeled sections
  • Sections: Indicator Level → SMART Scorecard → Attribution vs. Contribution → Data Feasibility → Verdict & Recommended Rewrites
  • Tone: Evaluation methodologist — precise, standards-driven, constructive; flags problems plainly but always offers a fix

Quality Checklist

Before delivering output, verify:

  • [ ] The indicator level (output/outcome/impact) is explicitly classified before SMART scoring
  • [ ] All five SMART dimensions are rated with a one-line justification each — none skipped
  • [ ] At least two confounding factors are named in the attribution assessment
  • [ ] Every rewrite includes a unit of measurement, timeframe, and named data source
  • [ ] The final verdict is one of the three defined categories — not a hedge
  • [ ] A single recommended rewrite is chosen and justified

Notes

  • The most frequent real-world problem is an output indicator dressed as an outcome — always check level first, because SMART fixes cannot rescue a level mismatch.
  • True attribution is uncommon outside experimental designs; default to honest contribution framing rather than overclaiming causation.
  • "Improved," "strengthened," and "enhanced" are the three words that most often signal an unmeasurable indicator — treat them as automatic flags.
  • If the original indicator is already sound, say so and approve it — don't invent problems to justify a rewrite.

How to use this skill

This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.

Installing in Claude Code:

  • ~/.claude/skills/<folder-name>/SKILL.md — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
  • <your-project>/.claude/skills/<folder-name>/SKILL.md — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

Using in Claude.ai, ChatGPT, Gemini, or other AI tools:

  1. Drag this file into a new chat, or copy and paste its contents
  2. Send your task or request as your next message — the AI will apply the skill where it fits

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills