9thlevelsoftware/legion/review-panel
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Assembles a review panel of specialist agents matched to the code or artifact being reviewed, each evaluating against domain-specific rubrics, then synthesizes findings into a consolidated report. Called by review-loop for /legion:review. Use when the user needs multi-reviewer feedback, peer review simulation, rubric-based code evaluation, or expert critique from multiple perspectives.
Install
npx vskill@latest install 9thlevelsoftware/legion/review-panelSKILL.md
Review Panel
Composes context-aware multi-perspective review teams from the 48-agent pool (roster defined in CLAUDE.md Division table; count computed at runtime from agents/ directory). Each reviewer evaluates through domain-specific weighted rubrics with non-overlapping criteria. Produces a synthesized consolidated report.
Used by /legion:review when panel mode is selected. Replaces the static phase-type-to-agent mapping with dynamic selection via agent-registry recommendation algorithm.
Quick Start
Three phases: Compose (Section 1) → Review (agents evaluate independently against rubrics from Section 2) → Synthesize (Section 3 deduplicates, filters, and produces consolidated report). Intent filtering narrows scope when flags like --just-security are passed.
Section 1: Panel Composition Algorithm
How to assemble a review panel dynamically based on what's being reviewed.
1.2 Intent-Based Panel Filtering
When review command specifies intent (e.g., --just-security):
- Load Intent Domains
- From intent-teams.yaml security-only template
- Domains: security, owasp, stride, authentication, authorization
- Filter Agents by Domain
- For each candidate agent:
- Check agent's primary domains in authority-matrix.yaml
- Include only if domains overlap with intent domains
- Example: engineering-security-engineer (security domain) → INCLUDE
- Example: engineering-frontend-developer (ui domain) → EXCLUDE
- Override Recommendations
- If intent mode: Use intent template agents, skip registry recommendation
- If full mode: Use existing recommendation algorithm
- Domain-Only Review Scope
- Agents review ONLY their intent-matching domains
- Non-security findings deferred or excluded
- Prevents out-of-domain opinions (already filtered by authority in Phase 37)
Panel Output Contract (consumed by review-loop dispatch)
review-panel does NOT spawn agents. It emits a composition artifact that review-loop Section 2 consumes and review-loop Section 3/4 dispatches. The contract below is the ONLY surface review-loop reads.
Emitted artifact: in-memory object with shape:
Dispatch handoff to review-loop:
| Field | Value | |
|---|---|---|
| When | AFTER Step 6 user confirmation succeeds (confirmed_by_user: true). If user declines all options: emit <escalation severity=warning type=scope> and return empty panel — do NOT fabricate a panel. | |
| Why no dispatch here | review-panel is pure composition. Spawning is the responsibility of review-loop Section 3 Step 4 (review agents) and Section 5 Step 4 (fix agents). Separation prevents double-dispatch bugs when panel is reused across cycles. | |
| How many reviewers | Exactly the count emitted in panel.reviewers (2-4 per Step 4 cap). review-loop must not add, drop, or reorder reviewers without re-invoking review-panel. | |
| Mechanism | Return value to review-loop caller. review-loop reads panel.reviewers[*].agent_id and treats each as input to Section 3 prompt construction, then Section 3 Step 4 dispatch spec. |
Preconditions (verify before emitting artifact):
- Step 1.0 preconditions passed.
- Step 3 review-capability filter produced ≥ 1 eligible agent.
- Step 4 cap produced
count(reviewers) ∈ {2, 3, 4}. - Step 5 rubric assignment succeeded for every reviewer (no silent default).
- Step 6 user confirmation returned a closed-set option (never free text without validation).
If any precondition fails: emit <escalation severity=blocker type=quality> and return empty panel. Do NOT return partial panel.
Review Conduct Rules
These rules are injected into every review agent's prompt to ensure rigorous, actionable feedback.
Mandatory Conduct
- Verify before implementing — Confirm feedback is technically correct for THIS codebase before acting on it. Grep for the function, read the file, check the dependency.
- Pushback is expected — Reviewers MUST challenge when: suggestion would break existing functionality, reviewer lacks context for the change, recommendation violates YAGNI, feedback is technically incorrect for the runtime/framework in use, or suggestion conflicts with prior architectural decisions.
- No performative agreement — Do NOT use phrases like "great point!", "you're absolutely right!", "excellent suggestion!". State agreement factually: "Confirmed: the null check is missing."
- Specificity required — Every finding MUST include: file path and line number, what is wrong, why it matters, and how to fix it. Findings without all four elements are rejected.
- Severity accuracy — Do NOT mark nitpicks (formatting, naming preferences) as BLOCKER. BLOCKER is reserved for: crashes, data loss, security vulnerabilities, broken builds. WARNING is for logic errors, missing edge cases, performance issues. INFO is for style, conventions, suggestions.
- Clear verdict mandatory — Every review MUST end with an explicit verdict: "Ready to merge? Yes / No / With fixes". No ambiguous conclusions.
Section 2: Domain Rubric Registry
Non-overlapping evaluation criteria for each reviewer specialty. Each rubric defines what that reviewer checks — and implicitly, what they do NOT check (that's another reviewer's rubric).
How Rubrics Work
This is the start of the file. Read the full SKILL.md on GitHub.
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/review-panel/SKILL.md
Popularity
- Repository stars
- 62
- Repository forks
- 7
- 7-day trend score
- 21
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
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