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Get Started Free →Automatically find relevant context from knowledge graph and code relationships while coding. Detects when context would be helpful (new files, unfamiliar code, architectural decisions) and surfaces related entities, prior decisions, and code dependencies.
.claude/skills/aiskillstore-code-context-finder/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-13 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 0% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 13% | 0% |
| case-20 | ✗→✓ | ▲ Improved | -35% | 0% |
| case-21 | ✗→✓ | ▲ Improved | -15% | 0% |
Find and surface relevant context while coding by combining knowledge graph search with code relationship analysis. Uses smart detection to identify when additional context would be helpful, then retrieves:
This skill activates automatically when detecting:
| Trigger | What to Search | |---------|----------------| | Opening unfamiliar file | Knowledge graph for file/module context, code for imports/dependencies | | Working on new feature | Prior decisions, related concepts, similar implementations | | Debugging errors | Related issues, error patterns, affected components | | Refactoring code | Dependent files, callers/callees, test coverage | | Making architectural decisions | Past ADRs, related design docs, established patterns | | Touching config/infra files | Related deployments, environment notes, past issues |
For detection triggers reference, load references/detection_triggers.md.
Identify triggers that suggest context would help:
Signals to watch:
- New/unfamiliar file opened
- Error messages mentioning unknown components
- Questions about "why" or "how" something works
- Changes to shared/core modules
- Architectural or design discussionsUse MCP memory tools to find relevant entities:
# Search for related context
mcp__memory__search_nodes(query="<topic>")
# Open specific entities if known
mcp__memory__open_nodes(names=["entity1", "entity2"])
# View relationships
mcp__memory__read_graph()Search strategies:
Find code-level context:
python# Find what imports this module grep -r "from module import" --include="*.py" grep -r "import module" --include="*.py" # Find function callers grep -r "function_name(" --include="*.py" # Find class usages grep -r "ClassName" --include="*.py" # Find test coverage find . -name "*test*.py" -exec grep -l "module_name" {} \;
For common search patterns, load references/search_patterns.md.
Present findings concisely:
markdown## Context Found **Knowledge Graph:** - [Entity]: Relevant observation - [Decision]: Prior architectural choice **Code Relationships:** - Imported by: file1.py, file2.py - Depends on: module_a, module_b - Tests: test_module.py (5 tests) **Suggested Actions:** - Review [entity] before modifying - Consider impact on [dependent files]
| Intent | Query Pattern | |--------|---------------| | Find project context | search_nodes("project-name") | | Find prior decisions | search_nodes("decision") or search_nodes("<feature>") | | Find related concepts | search_nodes("<concept>") | | Find people/owners | search_nodes("<person-name>") | | Browse all | read_graph() |
| Intent | Command | |--------|---------| | Find importers | grep -r "from X import\|import X" | | Find callers | grep -r "function(" | | Find implementations | grep -r "def function\|class Class" | | Find tests | find -name "*test*" -exec grep -l "X" | | Find configs | grep -r "X" *.json *.yaml *.toml |
detection_triggers.md - Detailed trigger patterns for smart detectionsearch_patterns.md - Common search patterns for code relationshipsfind_code_relationships.py - Analyze imports, dependencies, and call graphs| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 4,491 | 11,517 | +156% | 1 | 1 | 0% | 372 | 1,570 | +322% | 0 | 0 | — |
case-02 | fail→fail | 22,002 | 7,808 | -65% | 1 | 1 | 0% | 3,592 | 1,649 | -54% | 0 | 0 | — |
case-03 | fail→fail | 10,963 | 7,159 | -35% | 1 | 1 | 0% | 1,732 | 1,518 | -12% | 0 | 0 | — |
case-04 | pass→fail | 10,021 | 4,950 | -51% | 1 | 1 | 0% | 2,123 | 1,283 | -40% | 0 | 0 | — |
case-05 | pass→fail | 12,603 | 6,694 | -47% | 1 | 1 | 0% | 2,299 | 1,265 | -45% | 0 | 0 | — |
case-06 | pass→pass | 6,562 | 5,487 | -16% | 1 | 1 | 0% | 1,100 | 1,789 | +63% | 0 | 0 | — |
case-07 | fail→fail | 6,387 | 5,944 | -7% | 1 | 1 | 0% | 418 | 1,449 | +247% | 0 | 0 | — |
case-08 | fail→fail | 8,956 | 4,131 | -54% | 1 | 1 | 0% | 1,438 | 1,561 | +9% | 0 | 0 | — |
case-09 | fail→fail | 10,831 | 5,165 | -52% | 1 | 1 | 0% | 1,134 | 1,257 | +11% | 0 | 0 | — |
case-10 | pass→pass | 13,636 | 6,070 | -55% | 1 | 1 | 0% | 2,327 | 2,037 | -12% | 0 | 0 | — |
case-11 | pass→pass | 5,212 | 3,494 | -33% | 1 | 1 | 0% | 874 | 1,572 | +80% | 0 | 0 | — |
case-12 | pass→pass | 7,357 | 4,191 | -43% | 1 | 1 | 0% | 1,187 | 1,666 | +40% | 0 | 0 | — |
case-13 | fail→pass | 8,046 | 3,512 | -56% | 1 | 1 | 0% | 1,376 | 1,628 | +18% | 0 | 0 | — |
case-14 | pass→fail | 19,888 | 25,518 | +28% | 1 | 1 | 0% | 2,025 | 1,438 | -29% | 0 | 0 | — |
case-15 | pass→fail | 11,594 | 6,672 | -42% | 1 | 1 | 0% | 1,801 | 1,485 | -18% | 0 | 0 | — |
case-16 | fail→fail | 14,766 | 14,372 | -3% | 1 | 1 | 0% | 2,338 | 2,462 | +5% | 0 | 0 | — |
case-17 | fail→fail | 13,686 | 7,620 | -44% | 1 | 1 | 0% | 2,179 | 2,400 | +10% | 0 | 0 | — |
case-18 | fail→pass | 16,633 | 10,107 | -39% | 1 | 1 | 0% | 2,540 | 2,542 | +0% | 0 | 0 | — |
case-19 | fail→pass | 17,125 | 12,232 | -29% | 1 | 1 | 0% | 2,604 | 2,943 | +13% | 0 | 0 | — |
case-20 | fail→pass | 12,756 | 1,796 | -86% | 1 | 1 | 0% | 1,913 | 1,234 | -35% | 0 | 0 | — |
case-21 | fail→pass | 10,357 | 1,640 | -84% | 1 | 1 | 0% | 1,487 | 1,261 | -15% | 0 | 0 | — |
case-22 | fail→pass | 13,430 | 1,783 | -87% | 1 | 1 | 0% | 2,172 | 1,269 | -42% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 14 counted toward the lift figure. The other 8 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +9 percentage points is the difference between those two pass rates over the 14 comparable cases. 5 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.