---
name: outcome-indicator-validator
description: "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 collection feasibility check. Use when reviewing, refining, or approving outcome indicators for a logframe, results framework, theory of change, or M&E plan."
argument-hint: "[indicator] [program-context]"
allowed-tools: ["Read"]
---

# Outcome Indicator Validator

## 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.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — 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

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