dev
307d2fd471
Adds the project-review workflow for code review independent of the git diff. The scope is parsed from the user instruction: 全面/整个项目 -> whole-project (score every source file), otherwise feature + target keyword (locate the code with semantic search + graph queries). - scoring_tools.py: score_review_func gains all_files=True to score every source file in the graph via store.get_all_files() - main.py: score_review_tool gains all_files param; registers the project_review MCP prompt (prompts 6->7) - prompts.py: project_review_prompt(scope, target) with whole-project and feature branches (fixed a precedence bug that truncated the feature text) - skills.py + skills/project-review/: new read-only project-review skill with shared checklists - .opencode/command/code-review-graph-project-review.md: slash command - tests: test_project_review.py (prompt rendering), TestProjectReviewPrompt, skill count assertions 5->6, all_files wiring checks - docs: prompts (6->7) + project-review entries across COMMANDS, CLAUDE, README (+localized), INDEX, architecture, LLM-OPTIMIZED-REFERENCE, CHANGELOG
44 lines
2.3 KiB
Markdown
44 lines
2.3 KiB
Markdown
---
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description: Run the three-layer unified review (CRG graph context + score_review + dedupe_findings + generate_report).
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agent: build
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---
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# Unified Review
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Run the three-layer unified code review using the MCP prompt workflow.
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$ARGUMENTS
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**Token optimization:** Before starting, call `get_docs_section_tool(section_name="unified-review")` for the optimized workflow.
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## Steps
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1. **Load the workflow** by calling the `unified_review` MCP prompt (or the `unified-review` skill). This drives the full READ-ONLY review pipeline.
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2. **Ensure the graph is current** by calling `build_or_update_graph_tool()`.
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3. **Get the review context** by calling `get_review_context_tool()` — changed files, blast radius, source snippets.
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4. **Detect changes** by calling `detect_changes_tool()` — risk score, changed functions, test gaps, affected flows.
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5. **Score objectively** by calling `score_review_tool()` — SQL risk, exception coverage, redundancy, high-risk density, vulnerability heuristic (good/warn/fail grades). LLM-judged metrics are in `llm_judged`.
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6. **Review the changed code** (Layer 1 chain decomposition): interface, business, data, utility, error handling, security, performance, observability. Produce findings with severity (blocker/major/minor), confidence (1-10), file:line, and proposed fix.
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7. **Merge findings** by calling `dedupe_findings_tool(findings=<your findings>)` — fingerprint dedup, multi-source confidence boost, PR quality score.
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8. **Generate the report** by calling `generate_report_tool(review_data=<verdict, tier, scope, metrics, merged findings>)` — writes `code-review-report.html` and `code-review-report.md` (default `format="both"`).
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9. **Report** the verdict (✅ PASS / ❌ FAIL), severity counts, each issue with confidence + fix, and manual-review items.
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## Important Rules
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- **READ-ONLY.** This workflow never modifies code, commits, or pushes. Every finding waits for a manual fix decision.
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- **Any blocker → verdict ❌ FAIL**, regardless of other scores.
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- Tier (fast / standard / strict) comes from `.code-review.yaml` at the repo root, or the `tier` argument.
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## Tips
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- For large diffs (50+ lines), dispatch specialist subagents (testing, maintainability, security, performance, data-migration, api-contract) in parallel before dedupe.
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- Security and data-migration are insurance specialists — always run even when silent.
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