kwreview

<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