kwreview

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