openclaw/.github/copilot-instructions.md

Civyk Repo Index - Code Intelligence

Always use civyk-repoix MCP tools for code discovery, navigation, and analysis - provides semantic understanding with symbol-level precision and AI-aware caching.

Always Use civyk-repoix For

• Finding code → search_symbols, get_file_symbols (not grep/find)
• Understanding structure → get_symbol, get_components (not reading entire files)
• Checking dependencies → get_dependencies, get_imports (not manual tracing)
• Finding tests → get_tests_for, get_recommended_tests (not guessing)
• Impact analysis → analyze_impact, get_references (not searching manually)
• PR context → build_delta_context_pack (not reading all changed files)

Tools by Scenario

Scenario	Tools
Explore	list_files, get_file_symbols, search_symbols
Understand	recall_understanding, get_symbol, get_file_symbols
Dependencies	get_dependencies, get_imports, find_circular_dependencies
Architecture	get_components, get_api_endpoints, get_inheritance_tree
Testing	get_tests_for, get_code_for_test, get_recommended_tests
Review	build_delta_context_pack, get_recent_changes, analyze_impact
Navigate	get_definition, get_references, get_callers
Quality	get_dead_code, get_hotspots, find_circular_dependencies

AI Cache - Context Preservation

Persist understanding across sessions at file, module, and project levels:

# Check cached understanding before reading
recall_understanding(scope="file", target="path/file.py")
recall_understanding(scope="module", target="src/auth")
recall_understanding(scope="project")

# Store after analyzing - hierarchical workflow
store_understanding(scope="file", target="path/file.py", purpose="...", key_points=[...])
store_understanding(scope="module", target="src/auth", purpose="...", key_points=[...])
store_understanding(scope="project", purpose="...", key_points=[...])

Hierarchical Workflow:

1. Read file → store_understanding(scope="file", ...)
2. After all files in module → store_understanding(scope="module", ...)
3. After all modules → store_understanding(scope="project", ...)

Recall Response:

• found=true + fresh=true → Use cached, skip read
• found=true + fresh=false → Changed, re-read and update
• found=false → Read, analyze, then store

Token-Efficient Patterns

1. Cache first: recall_understanding before reading files
2. Context packs: build_context_pack(task="...", token_budget=800)
3. Symbols over files: get_file_symbols uses ~10x fewer tokens
4. Save insights: store_understanding after analysis

Tool Sequences

Action	Sequence
Session start	get_understanding → recall_understanding for key files → resume
New task	build_context_pack → recall_understanding → work
Understand function	get_symbol → get_callers → get_references
Find location	search_symbols → get_file_symbols → get_definition
Refactor	get_references → analyze_impact → get_recommended_tests
Review PR	build_delta_context_pack → analyze_impact

Before Commit

1. Build - No errors or warnings
2. Format - Run formatter (prettier, black, gofmt, etc.)
3. Lint - Fix all issues (eslint, ruff, markdownlint, etc.)
4. Type check - If applicable (tsc, mypy, etc.)
5. Test changes - get_recommended_tests(changed_files=[...])
6. Full tests - Ensure no regression