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Get Started Free →Direct access to GitHub Copilot MCP server tools for AI-powered development assistance
.claude/skills/aiskillstore-copilot-mcp-server/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 288% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -36% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 30% | 0% |
When to use this skill:
General-purpose AI assistant for coding help, debugging, and architecture design.
javascriptmcp__plugin__copilot__ask-copilot( prompt="string", // Required: The question or task for Copilot context="string", // Optional: Additional context model="string", // Optional: Specific model to use (default: claude-sonnet-4.6) allowAllTools=true/false // Optional: Allow Copilot to use all available tools )
Professional code review with focus on specific areas.
javascriptmcp__plugin__copilot__copilot-review( code="string", // Required: Code to review focusAreas=["security", "performance", "maintainability", "best-practices"] // Optional: Specific areas to focus )
Get detailed explanations of code snippets.
javascriptmcp__plugin__copilot__copilot-explain( code="string", // Required: Code to explain model="string" // Optional: Model to use )
Debug errors in code with context-aware analysis.
javascriptmcp__plugin__copilot__copilot-debug( code="string", // Required: Code with error error="string", // Required: Error message context="string" // Optional: Additional context )
Get suggestions for code refactoring and improvements.
javascriptmcp__plugin__copilot__copilot-refactor( code="string", // Required: Code to refactor goal="string" // Required: Refactoring goal (e.g., "improve performance") )
Generate unit tests for existing code.
javascriptmcp__plugin__copilot__copilot-test-generate( code="string", // Required: Code to test framework="string" // Optional: Testing framework (e.g., jest, pytest, mocha) )
Get CLI command suggestions for specific tasks.
javascriptmcp__plugin__copilot__copilot-suggest( task="string", // Required: Task description model="string" // Optional: Model to use )
Start a new conversation session with context tracking.
javascriptmcp__plugin__copilot__copilot-session_start()
Retrieve conversation history for continuity.
javascriptmcp__plugin__copilot__copilot-session_history( sessionId="string" // Optional: Specific session ID )
Choose from available models based on task complexity:
javascriptmcp__plugin__copilot__ask-copilot( prompt="Implement a REST API endpoint for user authentication with JWT", model="claude-sonnet-4.6", allowAllTools=true )
javascriptmcp__plugin__copilot__copilot_review( code=`function login(username, password) { const query = \`SELECT * FROM users WHERE username = '\${username}' AND password = '\${password}'\`; return db.query(query); }`, focusAreas=["security", "sql-injection", "authentication"] )
javascriptmcp__plugin__copilot__copilot_debug( code="const result = await fetchData().json;", error="TypeError: fetchData(...).json is not a function", context="Trying to parse JSON response from API" )
javascriptmcp__plugin__copilot__copilot_test_generate( code=`function isPrime(n) { if (n <= 1) return false; for (let i = 2; i * i <= n; i++) { if (n % i === 0) return false; } return true; }`, framework="jest" )
If MCP server is unavailable:
copilot, mcp, ai, code review, debugging, testing, refactoring, github copilot, claude, gpt
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 12,702 | 16,413 | +29% | 1 | 1 | 0% | 1,407 | 2,057 | +46% | 0 | 0 | — |
case-02 | fail→fail | 21,359 | 9,070 | -58% | 1 | 1 | 0% | 2,703 | 2,815 | +4% | 0 | 0 | — |
case-03 | fail→fail | 5,741 | 6,104 | +6% | 1 | 1 | 0% | 1,208 | 1,971 | +63% | 0 | 0 | — |
case-04 | fail→pass | 15,258 | 9,231 | -40% | 1 | 1 | 0% | 1,746 | 2,127 | +22% | 0 | 0 | — |
case-05 | fail→pass | 8,652 | 7,900 | -9% | 1 | 1 | 0% | 527 | 2,044 | +288% | 0 | 0 | — |
case-06 | fail→pass | 19,970 | 8,503 | -57% | 1 | 1 | 0% | 3,353 | 2,161 | -36% | 0 | 0 | — |
case-07 | fail→pass | 11,392 | 8,920 | -22% | 1 | 1 | 0% | 1,827 | 2,177 | +19% | 0 | 0 | — |
case-08 | fail→pass | 8,364 | 2,700 | -68% | 1 | 1 | 0% | 1,534 | 2,001 | +30% | 0 | 0 | — |
case-09 | fail→pass | 14,195 | 11,760 | -17% | 1 | 1 | 0% | 2,276 | 2,370 | +4% | 0 | 0 | — |
case-10 | fail→pass | 19,613 | 10,418 | -47% | 1 | 1 | 0% | 2,353 | 2,384 | +1% | 0 | 0 | — |
case-11 | fail→fail | 12,157 | 14,772 | +22% | 1 | 1 | 0% | 1,246 | 1,702 | +37% | 0 | 0 | — |
case-12 | fail→fail | 33,662 | 11,939 | -65% | 1 | 1 | 0% | 4,639 | 1,925 | -59% | 0 | 0 | — |
case-13 | fail→fail | 11,646 | 17,172 | +47% | 1 | 1 | 0% | 1,340 | 2,075 | +55% | 0 | 0 | — |
case-14 | fail→fail | 11,787 | 9,419 | -20% | 1 | 1 | 0% | 2,323 | 1,800 | -23% | 0 | 0 | — |
case-15 | fail→fail | 8,613 | 16,001 | +86% | 1 | 1 | 0% | 489 | 1,647 | +237% | 0 | 0 | — |
case-16 | fail→fail | 7,944 | 15,005 | +89% | 1 | 1 | 0% | 397 | 2,055 | +418% | 0 | 0 | — |
case-17 | pass→pass | 10,295 | 9,152 | -11% | 1 | 1 | 0% | 898 | 2,169 | +142% | 0 | 0 | — |
case-18 | pass→pass | 11,177 | 7,546 | -32% | 1 | 1 | 0% | 1,726 | 1,929 | +12% | 0 | 0 | — |
case-19 | fail→pass | 11,795 | 3,110 | -74% | 1 | 1 | 0% | 1,818 | 2,032 | +12% | 0 | 0 | — |
case-20 | pass→pass | 7,451 | 2,255 | -70% | 1 | 1 | 0% | 436 | 1,858 | +326% | 0 | 0 | — |
case-21 | pass→pass | 12,311 | 10,280 | -16% | 1 | 1 | 0% | 1,404 | 2,380 | +70% | 0 | 0 | — |
case-22 | pass→pass | 8,731 | 2,969 | -66% | 1 | 1 | 0% | 752 | 1,979 | +163% | 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 +36 percentage points is the difference between those two pass rates over the 14 comparable cases.
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.