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Get Started Free →Model Context Protocol (MCP) integration specialist. Use when creating MCP server configurations, implementing MCP integrations, or optimizing MCP performance. Specializes in MCP server architecture and integration patterns.
.claude/skills/aiskillstore-mcp-integration/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-16 | ✗→✓ | ▲ Improved | -17% | 0% |
| case-15 | ✓→✓ | ＝ Same ✓ | 15% | 0% |
| case-01 | ✓→✓ | ＝ Same ✓ | 7% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 17% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 34% | 0% |
This skill creates and optimizes Model Context Protocol (MCP) integrations, including server configurations, authentication, and performance optimization.
Create an MCP integration for GitHub APIConfigure MCP server for database accessOptimize this MCP server configuration for performancejson{ "mcpServers": { "ServiceName MCP": { "command": "npx", "args": ["-y", "package-name@latest"], "env": { "API_KEY": "required-env-var" } } } }
API Integration MCPs:
Development Tool MCPs:
Input: Create GitHub MCP integration
Output:
markdown## MCP Integration: GitHub ### Configuration
{ "mcpServers": { "GitHub Integration MCP": { "command": "npx", "args": "-y", "github-mcp@latest"], "env": { "GITHUB_TOKEN": "ghp_your_token_here", "GITHUB_API_URL": "https://api.github.com" } } } }
### Usage
Install with: `npx claude-code-templates@latest --mcp="github-integration"`
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-15 | pass→pass | 12,996 | 11,318 | -13% | 1 | 1 | 0% | 2,077 | 2,397 | +15% | 0 | 0 | — |
case-01 | pass→pass | 24,238 | 11,910 | -51% | 1 | 1 | 0% | 2,114 | 2,255 | +7% | 0 | 0 | — |
case-02 | fail→fail | 9,428 | 11,770 | +25% | 1 | 1 | 0% | 1,465 | 1,678 | +15% | 0 | 0 | — |
case-03 | pass→pass | 12,359 | 10,228 | -17% | 1 | 1 | 0% | 1,935 | 2,256 | +17% | 0 | 0 | — |
case-04 | pass→pass | 5,209 | 3,552 | -32% | 1 | 1 | 0% | 857 | 1,148 | +34% | 0 | 0 | — |
case-05 | pass→pass | 8,121 | 5,631 | -31% | 1 | 1 | 0% | 1,346 | 1,517 | +13% | 0 | 0 | — |
case-06 | pass→pass | 16,442 | 11,488 | -30% | 1 | 1 | 0% | 2,782 | 2,576 | -7% | 0 | 0 | — |
case-07 | pass→pass | 13,071 | 13,858 | +6% | 1 | 1 | 0% | 1,922 | 2,962 | +54% | 0 | 0 | — |
case-08 | pass→pass | 13,321 | 10,212 | -23% | 1 | 1 | 0% | 1,958 | 2,207 | +13% | 0 | 0 | — |
case-09 | pass→pass | 13,431 | 6,852 | -49% | 1 | 1 | 0% | 2,155 | 1,702 | -21% | 0 | 0 | — |
case-10 | pass→pass | 5,041 | 4,781 | -5% | 1 | 1 | 0% | 844 | 1,511 | +79% | 0 | 0 | — |
case-11 | pass→pass | 12,770 | 9,747 | -24% | 1 | 1 | 0% | 2,069 | 2,256 | +9% | 0 | 0 | — |
case-12 | pass→pass | 7,737 | 6,536 | -16% | 1 | 1 | 0% | 1,381 | 1,672 | +21% | 0 | 0 | — |
case-13 | pass→pass | 10,874 | 6,862 | -37% | 1 | 1 | 0% | 1,770 | 1,742 | -2% | 0 | 0 | — |
case-14 | pass→pass | 14,171 | 6,848 | -52% | 1 | 1 | 0% | 1,816 | 1,933 | +6% | 0 | 0 | — |
case-16 | fail→pass | 5,992 | 1,801 | -70% | 1 | 1 | 0% | 1,047 | 871 | -17% | 0 | 0 | — |
case-17 | pass→pass | 9,178 | 4,918 | -46% | 1 | 1 | 0% | 1,049 | 1,506 | +44% | 0 | 0 | — |
case-18 | fail→fail | 10,381 | 5,250 | -49% | 1 | 1 | 0% | 1,917 | 1,609 | -16% | 0 | 0 | — |
case-19 | pass→pass | 15,635 | 13,639 | -13% | 1 | 1 | 0% | 2,432 | 2,756 | +13% | 0 | 0 | — |
case-20 | pass→pass | 12,656 | 14,014 | +11% | 1 | 1 | 0% | 2,745 | 3,823 | +39% | 0 | 0 | — |
case-21 | pass→pass | 10,920 | 8,557 | -22% | 1 | 1 | 0% | 1,743 | 2,373 | +36% | 0 | 0 | — |
case-22 | pass→pass | 11,040 | 11,243 | +2% | 1 | 1 | 0% | 1,876 | 2,592 | +38% | 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. The headline lift of +5 percentage points is the difference between those two pass rates over the 22 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.