/kw-review

name: kw:review
description: Multi-reviewer quality check for knowledge work. Runs strategic alignment and data accuracy reviewers on plans, briefs, and strategy docs.
argument-hint: "[file path or content to review]"

<review_target> #$ARGUMENTS </review_target>

Review

Two automated reviewers check your work for the errors that damage credibility: wrong strategy and wrong data.

When to Use

• After `/kw:plan` to validate a plan before executing

• Before sharing a strategy doc, brief, or analysis with stakeholders

• "Review this plan", "Check this brief", "Is the data right?"

• Any knowledge work artifact that will be seen by decision-makers

What Gets Reviewed

The most recently produced artifact. Determined by context:

| Situation | What to review | | --------------------- | ---------------------------------------------------------------------- | | `/kw:plan` just ran | The plan file it produced | | User points to a file | That file | | User pastes content | That content | | Ambiguous | Ask: "What should I review? Provide a file path or paste the content." |

Process

Step 1: Load the content

Read the file or accept pasted content. If the content references data (metrics, conversion rates, financial figures), also load:

• Any data context files referenced in the project's CLAUDE.md

• Check freshness of any data files cited

Step 2: Run both reviewers in parallel

<parallel_tasks>

1. **Strategic Alignment Reviewer** — Launch Task agent: `compound-knowledge:review:strategic-alignment-reviewer`

• Pass: the full content + any business context from the project's CLAUDE.md
• It checks: goal clarity, falsifiable hypothesis, success metrics, scope proportionality, resource awareness, strategic consistency

2. **Data Accuracy Reviewer** — Launch Task agent: `compound-knowledge:review:data-accuracy-reviewer`

• Pass: the full content + any data context files referenced in the project's CLAUDE.md
• It checks: source citations, comparison baselines, canonical definitions, freshness, caveats, hardcoded numbers

</parallel_tasks>

Both agents return findings in `[P1|P2|P3]` format. Wait for both to complete before proceeding.

Step 3: Run editorial check (if external-facing)

If the content will be published, emailed, or posted publicly:

• Check for AI writing patterns (generic phrasing, stock transitions, vague claims)

• Check tone and voice consistency with project style guides

If the content is internal (plan, brief, analysis for the team): skip this step.

Step 4: Merge and present findings

Combine findings from both reviewers. Group all findings by severity:

## Review: [Document Title]

### P1 — Blocks Shipping
[These must be fixed before sharing. Wrong data, wrong goal, unfalsifiable hypothesis.]

### P2 — Should Fix
[Important but not blocking. Missing sources, unclear metrics, scope concerns.]

### P3 — Nice to Have
[Minor refinements. Wording, additional context, formatting.]

### Clean
[Sections that passed all checks — explicitly note what's good.]

**Severity definitions:**

| Severity | What qualifies | Examples | | ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------ | | **P1 Critical** | Factual error, wrong data source, missing goal, unfalsifiable hypothesis | "Metric cited from wrong source" | | **P2 Important** | Missing source citation, stale data, unclear success metric | "Conversion rate has no comparison basis" | | **P3 Nice-to-have** | Minor framing, additional context, formatting | "Could specify the time period for this metric" |

Step 5: Offer next steps

Use AskUserQuestion:

**Question:** "Review complete. \[N] findings (\[P1 count] critical, \[P2 count] important). What next?"

**Options:**

1. **Fix P1/P2 issues now** — Address findings inline, then re-review 2. **Run `/kw:work`** — Plan passes. Start executing it 3. **Run `/kw:compound`** — Save review insights as learnings 4. **Push to Proof** — Share review findings for discussion 5. **Ship as-is** — Acknowledge findings and proceed without fixing

Important Rules

• **P1 = hard gate.** A factual error in a strategy doc is worse than a typo. Say so clearly.

• **Verify, don't assume.** If a number is cited, check it against the actual source if possible. Don't just check formatting.

• **Flag staleness.** Data older than 48 hours gets a freshness warning. Data older than 7 days gets a P2.

• **Be specific.** "Data might be wrong" is not useful. "Revenue cited as $X but source shows $Y as of \[date]" is.

• **Credit what's good.** Don't only flag problems. Note sections that are well-grounded and clearly structured.

Pipeline Mode

When invoked with `disable-model-invocation` context (e.g., from an orchestrator or automation):

• Skip all AskUserQuestion prompts
• Use sensible defaults for all choices
• Write output files without waiting for confirmation
• Proceed to the next suggested skill automatically
• Output structured results that the calling context can parse