code-review-graph/code-review-graph-main/skills/unified-review/SKILL.md

name, description

name	description
unified-review	Three-layer unified code review fusing CRG graph context with ai-code-review scoring methodology and gstack-review fix-first workflow

Unified Review

Perform a three-layer, read-only code review that fuses:

• CRG graph context (blast radius, test gaps, affected flows)
• ai-code-review methodology (Layer-1 chain decomposition, Layer-2 quantitative scoring, Layer-3 acceptance)
• gstack-review workflow (confidence calibration, fix-first, specialist subagents, review-log persistence)

This skill is READ-ONLY. Every finding is presented to the user for a manual fix decision. Never apply code changes, commit, or push.

Token Efficiency Rules

• ALWAYS start with get_minimal_context(task="unified review"). Use detail_level="minimal" on all calls; escalate to "standard" only when a metric or finding needs evidence.

Step 0 - Scope and tier

Read .code-review.yaml at the repo root (default tier standard). Tiers: fast (Layer-1 + blockers only), standard (all layers), strict (full + every blocker/major fix needs per-item user confirmation). Single-invocation overrides: 快速审查 → fast, 严格审查 → strict. Detect the project language/framework and the review scope (change/file/service/chain level). Declare both in the report header.

Step 1 - Graph context (CRG)

1. Call build_or_update_graph_tool() to ensure the graph is current.
2. Call get_review_context_tool() for changed files, blast radius, source snippets and review guidance.
3. Call detect_changes_tool() for risk-scored change analysis, test gaps and affected flows.

Step 2 - Layer 1: Chain decomposition (ai-code-review)

Inspect the changed code across eight categories: interface, business, data, utility, error handling, security, performance, observability. Mark each ✅ Clean / ⚠️ Issues Found / — N/A. Apply the gstack CRITICAL categories as a sub-pass: SQL & Data Safety, Race Conditions & Concurrency, LLM Output Trust Boundary, Shell Injection, and Enum & Value Completeness. Enum completeness requires reading code OUTSIDE the diff (Grep for sibling values, then Read each consumer).

Step 3 - Layer 2: Quantitative scoring

Call score_review_tool() for the objective metrics (SQL risk, exception coverage, redundancy, high-risk density, vulnerability heuristic). The remaining metrics (requirement coverage, logic alignment, trust boundaries) are judged by you from the requirements doc or a generic baseline; without a requirements doc halve their weight in the verdict.

Step 4 - Specialist dispatch (gstack, diff >= 50 lines)

When the diff has 50+ changed lines, dispatch specialist subagents in parallel via the Agent/task tool, each with a fresh context and its own checklist: testing, maintainability, security, performance, data-migration, api-contract. Security and data-migration always run (insurance). Collect each specialist's JSON findings.

Step 5 - Merge and dedupe

Call dedupe_findings_tool(findings=<all raw findings>) to merge by fingerprint (path:line:category), boost multi-source confidence (+1, cap 10), route low-confidence findings to the appendix, and compute the PR quality score.

Step 6 - Manual adjudication (READ-ONLY)

Present every merged finding with its severity (🔴 blocker / 🟡 major / 🔵 minor), confidence (1-10), file:line and a proposed fix. Group by severity and ask the user per batch: fix / skip / self-fix. 🔴 blockers cannot be batch-skipped. Record skipped findings for prior-review suppression on the next run. Do not modify code.

Step 7 - Acceptance gate (ai-code-review)

Any 🔴 blocker → verdict ❌ FAIL regardless of other scores. Classify each finding as Ready / Needs Fix / Unusable. Verify the change does not deviate from requirements or architecture conventions.

Step 8 - Report

Call generate_report_tool(review_data=<collected verdict, metrics, findings, tier, scope>) to write code-review-report.html and code-review-report.md (default format="both"). Pass reviewed_files: [path, ...] (changed/reviewed files) so the report header lists them in a collapsible <details> list. Also present the text report inline.

Step 9 - Persistence (optional)

If the gstack-review-log binary is available, record the review outcome (status, counts, quality score, per-finding actions). If it is unavailable, skip silently.

Output Format

Unified Review: N issues (X blocker, Y major, Z minor) — verdict: ✅ PASS / ❌ FAIL. List each issue with severity, confidence, file:line, problem, and proposed fix. List manual-review items (payment, order, inventory, permission, distributed-lock, data-migration) explicitly.

Token Efficiency Rules

• ALWAYS start with get_minimal_context(task="unified review") before any other graph tool.
• Use detail_level="minimal" on all calls. Only escalate to "standard" when minimal is insufficient.
• Target: complete a unified review in ≤8 tool calls and ≤1200 total output tokens.