feat: add project-review workflow (whole-project / single-feature review)
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
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@@ -809,8 +809,8 @@ _SKILLS: dict[str, dict[str, str]] = {
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"### Step 8 - Report\n"
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"Call `generate_report_tool(review_data=<collected verdict, "
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"metrics, findings, tier, scope>)` to write "
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"`code-review-report.html`. Also present the text report "
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"inline.\n\n"
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"`code-review-report.html` and `code-review-report.md` (default "
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"`format=\"both\"`). Also present the text report inline.\n\n"
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"### Step 9 - Persistence (optional)\n"
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"If the `gstack-review-log` binary is available, record the "
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"review outcome (status, counts, quality score, per-finding "
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@@ -830,6 +830,89 @@ _SKILLS: dict[str, dict[str, str]] = {
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"≤1200 total output tokens."
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),
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},
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"project-review.md": {
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"name": "project-review",
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"description": (
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"Whole-project or single-feature code review (not diff-based) "
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"using graph-wide analysis and objective scoring"
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),
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"body": (
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"## Project Review\n\n"
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"Review the entire codebase or a single feature/module, "
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"independent of the git diff. Two scopes, driven by the "
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"user's instruction:\n"
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"- **whole-project**: \"对项目代码进行全面审查\", \"全面审查\", "
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"\"整个项目\" → review every source file in the graph.\n"
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"- **feature**: \"审查 <功能/模块> 的代码\" (e.g. payment, "
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"auth) → review only the code related to the target.\n\n"
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"**This skill is READ-ONLY.** Every finding is presented to the "
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"user for a manual fix decision. Never apply code changes, "
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"commit, or push.\n\n"
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"### Token Efficiency Rules\n"
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'- ALWAYS start with `get_minimal_context(task="project review")`. '
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'Use `detail_level="minimal"` on all calls; escalate to '
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'"standard" only when a metric or finding needs evidence.\n\n'
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"### Step 0 - Parse the scope\n"
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"Read the user's instruction and set scope: whole-project "
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"(contains 全面/整个项目/所有/all) or feature + target "
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"(extract the feature/module keyword). Declare both in the "
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"report header.\n\n"
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"### Step 1 - Graph ready\n"
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"1. Call `build_or_update_graph_tool()` to ensure the graph is "
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"current.\n"
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"2. Call `get_minimal_context_tool(task=\"project review\")` "
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"for stats and community overview.\n\n"
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"### Step 2 - Architecture map\n"
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"Call `get_architecture_overview_tool(detail_level=\"minimal\")` "
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"and `list_communities_tool(detail_level=\"minimal\")` to map "
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"the module structure.\n\n"
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"### Step 3 - High-risk scan (whole-project)\n"
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"Call `get_knowledge_gaps_tool()`, `get_hub_nodes_tool()`, "
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"`get_bridge_nodes_tool()`, `find_large_functions_tool()` and "
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"`get_surprising_connections_tool()` to locate hotspots, "
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"chokepoints, untested areas and odd coupling.\n\n"
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"### Step 4 - Objective scoring\n"
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"- whole-project: `score_review_tool(all_files=True)` scores "
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"every source file in the graph.\n"
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"- feature: locate the target files with "
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"`semantic_search_nodes_tool(query=<target>)` and "
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"`query_graph_tool(pattern=\"children_of\", target=<target>)`, "
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"then `score_review_tool(changed_files=<files>)` and "
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"`get_impact_radius_tool(changed_files=<files>)` for the "
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"blast radius.\n\n"
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"### Step 5 - Chain decomposition\n"
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"Inspect the scored code across eight categories (interface, "
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"business, data, utility, error handling, security, "
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"performance, observability) and apply the gstack CRITICAL "
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"sub-pass (SQL & Data Safety, Race Conditions, LLM Output "
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"Trust Boundary, Shell Injection, Enum Completeness). Mark "
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"each ✅ / ⚠️ / —.\n\n"
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"### Step 6 - Manual adjudication (READ-ONLY)\n"
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"Present every finding with severity (🔴 blocker / 🟡 major / "
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"🔵 minor), confidence (1-10), file:line and a proposed fix. "
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"Group by severity and ask the user per batch: fix / skip / "
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"self-fix. 🔴 blockers cannot be batch-skipped. **Do not "
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"modify code.**\n\n"
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"### Step 7 - Acceptance gate\n"
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"Any 🔴 blocker → verdict `❌ FAIL`. Classify each finding as "
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"Ready / Needs Fix / Unusable.\n\n"
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"### Step 8 - Report\n"
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"Call `generate_report_tool(review_data=<verdict, scope, "
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"metrics, findings>)` to write `code-review-report.html` and "
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"`code-review-report.md` (default `format=\"both\"`).\n\n"
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"### Output Format\n"
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"`Project Review: N issues (X blocker, Y major, Z minor) — "
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"verdict: ✅ PASS / ❌ FAIL`. List each issue with severity, "
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"confidence, file:line, problem, and proposed fix.\n\n"
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"## Token Efficiency Rules\n"
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'- ALWAYS start with `get_minimal_context(task="project review")` '
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"before any other graph tool.\n"
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'- Use `detail_level="minimal"` on all calls. Only escalate to '
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'"standard" when minimal is insufficient.\n'
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"- Target: complete a project review in ≤12 tool calls and "
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"≤1800 total output tokens."
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),
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},
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"debug-issue.md": {
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"name": "debug-issue",
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"description": "Systematically debug issues using graph-powered code navigation",
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