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Interactive debugging via DAP-MCP for multiple languages with natural language commands
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Based on SOC occupation classification
Orchestrate large-scale changes across a codebase in parallel
Review changed code for reuse, quality, and efficiency, then fix any issues found
Consolidated Claude Code sync and parity tracking for RustyClawd. Fetches latest Claude Code features from official sources, compares against the local feature inventory, identifies gaps, and optionally creates GitHub issues. Also supports deep analysis by deminifying the installed Claude Code cli.js.
Track and report agent invocation metrics including usage counts, success/failure rates, and completion times. Use for understanding which agents are utilized, identifying underused agents, and optimizing agent delegation patterns.
Automated dependency conflict detection and resolution. Detects local vs CI environment mismatches, compares versions, and generates pinning recommendations. Run as pre-push check to catch issues early.
Auto-discovers and configures Language Server Protocol (LSP) servers for your project's languages
| name | dynamic-debugger |
| description | Interactive debugging via DAP-MCP for multiple languages with natural language commands |
| version | 1.0.0 |
| disableModelInvocation | true |
| activation_conditions | [{"intent_keywords":["debug","breakpoint","step through","step into","step over","inspect","trace execution","call stack"]},{"confidence_threshold":0.8},{"ask_confirmation_below":0.8}] |
| supported_languages | ["python","c","cpp","rust"] |
| planned_languages | ["javascript","typescript","go","java","csharp"] |
| dependencies | {"external":["dap-mcp"],"internal":["claude-code-sdk"]} |
| resource_requirements | {"memory_mb":4096,"timeout_minutes":30} |
| token_budget | {"skill_md":2000,"reference_md":4000,"examples_md":3000,"patterns_md":2000} |
Interactive debugging capability fer Claude Code via DAP-MCP integration. Debug yer code in natural language without leavin' the conversation.
This skill enables interactive debuggin' through the Debug Adapter Protocol (DAP) via MCP server integration. Set breakpoints, step through code, inspect variables, and control execution flow across multiple programming languages using natural language commands.
What ye get:
This skill is DISABLED by default (disableModelInvocation: true).
To enable:
Explicit invocation (recommended):
"Use the dynamic-debugger skill to debug this function"
Auto-activation (edit SKILL.md frontmatter):
disableModelInvocation: false # or remove this line
Why opt-in?
Required:
pip install dap-mcp or uv pip install dap-mcp)pip install debugpy)Verification:
# Check dap-mcp installation
python3 -m dap_mcp --help
# Check language debuggers
python -c "import debugpy; print('debugpy ready')"
which gdb
dlv version
User: "This async function isn't awaiting properly. Debug it."
Skill activates automatically:
User: "Getting segfault in malloc. Set a breakpoint."
Skill response:
User: "Why is this Promise chain not resolving?"
Skill response:
Explicit triggers (auto-start):
Implicit triggers (may ask confirmation):
Breakpoint management:
Execution control:
Variable inspection:
Session management:
Load these files on demand based on context:
Trigger: User needs specific API details, configuration syntax, or error codes Contains: Complete API reference, language configurations, session management API, error handling details, resource limits Size: 3,000-4,000 tokens Example queries: "How do I configure the Go debugger?", "What are the resource limits?", "Show me all error codes"
Trigger: User wants working code examples or specific debugging scenarios Contains: Production-ready debugging examples for all 6 languages with complete workflows Size: 2,000-3,000 tokens Example queries: "Show me a Python async debugging example", "How do I debug a Rust panic?", "Example of goroutine deadlock debugging"
Trigger: User asks about best practices, architectural patterns, or debugging strategies Contains: Production debugging patterns, performance techniques, security best practices, common pitfalls Size: 1,500-2,000 tokens Example queries: "What are best practices for debugging?", "How do I debug performance issues?", "Common security mistakes?"
Default behavior: Use only SKILL.md for basic debugging commands. Load supporting files only when explicitly needed.
Single concurrent session: Only one debugging session per user at a time Timeouts:
Resource limits:
Automatic detection via:
Confidence thresholds:
Manual override: "Debug this as Python code" (bypasses auto-detection)
Symptom: "dap-mcp server not available" Solution:
npm install -g dap-mcp
npx dap-mcp --version
Symptom: "debugpy not found" or "gdb not available" Solution: Install language-specific debugger (see Prerequisites)
Symptom: "Session timed out after 30 minutes" Solution: Start new session with "debug this"
Symptom: "Another debugging session is active" Solution: Stop existing session with "stop debugging" or wait for timeout
Symptom: "Debugged process exceeded 4GB memory limit" Solution: Reduce data structures or use sampling for large datasets
All errors provide:
Graceful degradation: If dap-mcp unavailable, skill suggests manual debugger commands.
Design philosophy: Keep skill orchestration minimal. Most tokens spent on actual debugging interaction, not overhead.
⚠️ IMPORTANT SECURITY CONSIDERATIONS:
Best Practices:
Process Isolation:
Philosophy: Ruthlessly simple orchestration. All complexity lives in dap-mcp server, not in this skill. We're just the friendly pirate captain givin' orders to the debugger crew.