Plan Review

Plan Review

Reviews PRDs, brainstorms, and technical plans using dynamically selected reviewer personas. Spawns parallel sub-agents that return structured JSON, then merges and deduplicates findings into a single report.

When to Use

• After writing a PRD
• After writing a technical plan
• When feedback is needed on any planning document
• Can be invoked standalone or called by iterative:brainstorming/iterative:tech-planning skills

Priority Scale

All reviewers use HIGH/MEDIUM/LOW:

Reviewers

6 personas in three tiers. See references/persona-catalog.md for the full catalog.

Document-type (exactly 1 — selected by document type):

Always-on (every review):

Conditional (selected per document):

Review Scope

The document type naturally regulates the review. A simple PRD gets 2 reviewers (prd-reviewer + coherence-reviewer). A complex tech plan with architecture decisions gets 4 (tech-plan-reviewer + coherence-reviewer + skeptic-reviewer + feasibility-reviewer). No separate "mode" is needed.

How to Run

Stage 1: Identify document

Identify the document to review from argument, conversation context, or ask user. Determine the document type — PRD, brainstorm, or tech plan — from filename, content, and context. Treat brainstorm documents and PRDs synonymously. Record the document path and document type.

Read the full document content. This is needed for both reviewer selection and spawning.

Stage 2: Select reviewers

Read the document content from Stage 1. The document-type reviewer and coherence are automatic. For each conditional persona in the catalog (references/persona-catalog.md), decide whether the document warrants it. This is agent judgment, not keyword matching.

Announce the team before spawning:

Review team:
- prd-reviewer (document-type)
- coherence-reviewer (always)
- scope-guardian-reviewer — PRD has 12 requirements across 3 priority levels with dependency conflicts

This is progress reporting, not a blocking confirmation.

Stage 3: Spawn sub-agents

Spawn each selected reviewer as a parallel sub-agent using the template in references/subagent-template.md. Each sub-agent receives:

1. Their persona file content (identity, failure modes, calibration, suppress conditions)
2. The JSON output contract from references/findings-schema.json
3. Review context: document type, document path, document content

Sub-agents are read-only: they review and return structured JSON. They do not edit files, run commands, or propose fixes.

Each sub-agent returns JSON matching references/findings-schema.json:

{
  "reviewer": "prd-reviewer",
  "findings": [...],
  "residual_concerns": [...]
}

Stage 4: Merge findings

Convert multiple reviewer JSON payloads into one deduplicated, confidence-gated finding set.

1. Validate. Check each output against the schema. Drop malformed findings (missing required fields). Record the drop count.
2. Confidence gate. Suppress findings below 0.50 confidence. Record the suppressed count.
3. Deduplicate. Compute fingerprint: normalize(section) + line_bucket(line, ±5) + normalize(title). When fingerprints match, merge: keep highest priority, keep highest confidence with strongest evidence, union evidence, note which reviewers flagged it.
4. Promote residual concerns. Scan residual_concerns for issues that overlap with findings from other reviewers or that describe concrete blocking risks (missing dependencies, unowned prerequisites, timeline impossibilities). Promote these to MEDIUM findings with confidence 0.55-0.65. A residual concern that corroborates an existing finding strengthens the case; one that identifies a distinct blocking risk deserves to be surfaced rather than buried.
5. Sort. Order by priority (HIGH first) → confidence (descending) → document order.
6. Collect coverage data. Union remaining (non-promoted) residual_concerns across reviewers.

Stage 5: Synthesize and present

Assemble the final report using the template in references/review-output-template.md:

1. Header. Document path, type, reviewer team with per-conditional justifications.
2. Findings. Grouped by priority (HIGH, MEDIUM, LOW). Each finding shows section, issue, reviewer(s), confidence.
3. Coverage. Suppressed count, residual concerns, failed/timed-out reviewers.
4. Synthesis. Patterns, tensions between reviewers, quick wins. Not a binary verdict — plans don't have "ready/not ready." The synthesis helps the author decide what to address.

Do not include time estimates.

After Review

When invoked from iterative:brainstorming or iterative:tech-planning: return findings directly — the calling skill owns the fix loop and workflow transitions. Do not enter the standalone fix loop below.

When invoked standalone: run the standalone fix loop.

Standalone Fix Loop

After presenting the synthesized findings (Stage 5), offer to fix issues when running standalone. Plan fixes are text edits with low cascading risk — keep the loop simple.

If zero findings, the review is done — no further prompts needed.

Step 6: Fix Offer

Use the platform's interactive question tool — AskUserQuestion (Claude Code) or request_user_input (Codex). Both platforms provide an automatic "Other" free-form option — do not add one manually.

Present a single prompt listing all priority levels with findings. No intermediate "choose which..." step.

Claude Code — use AskUserQuestion with multiSelect: true:

When HIGH issues exist, pre-check the HIGH option:

• ☑ HIGH (Recommended) — N issues that block execution
• ☐ MEDIUM — N issues
• ☐ LOW — N issues

When only MEDIUM/LOW issues exist, nothing pre-checked:

• ☐ MEDIUM — N issues
• ☐ LOW — N issues

Only include priority levels that have findings.

Codex — use request_user_input (single-select, build combined options):

When HIGH issues exist:

• Fix HIGH (Recommended) — N issues
• Fix HIGH + MEDIUM — N issues
• Fix all — N issues
• Skip fixes

When only MEDIUM/LOW issues exist:

• Fix MEDIUM only — N issues
• Fix MEDIUM + LOW — N issues
• Skip fixes

Only include options where findings exist at those levels. Omit options that would duplicate another (e.g., if no LOW, omit "Fix all" since it equals the line above).

Step 7: Apply Fixes

Fix only the selected priorities. Spawn a single subagent with the filtered findings, document path, and document type. The subagent applies targeted fixes, preserves the document's voice and decisions, and commits.

Wait for the subagent to complete.

Step 8: Done

After fixes land (or user skipped), present an interactive choice:

• Another review round — fresh reviewers on the updated document
• Done — review complete

If another round: run the full Stage 1–Step 8 flow again. No workflow transition options — the user knows what to do next.

Fallback

If the platform doesn't support parallel sub-agents, run reviewers sequentially. Everything else (stages, output format, merge pipeline) stays the same.