Kw:review

#$ARGUMENTS

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:

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

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

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:

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