llm-debugger
Diagnoses LLM output failures including hallucinations, constraint violations, format errors, and reasoning issues. Provides root cause classification, prompt fixes, tool improvements, and new test cases. Use for "debugging AI", "fixing prompts", "quality issues", or "output errors".
npx skills add majiayu000/claude-skill-registry --skill llm-debugger --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LLM Debugger Systematically diagnose and fix LLM output issues. ## Failure Taxonomy ```python class FailureType(Enum): HALLUCINATION = "hallucination" FORMAT_VIOLATION = "format_violation" CONSTRAINT_BREAK = "constraint_break" REASONING_ERROR = "reasoning_error" TOOL_MISUSE = "tool_misuse" REFUSAL = "unexpected_refusal" INCOMPLETE = "incomplete_output" ``` ## Root Cause Analysis ```python def diagnose_failure(input: str, output: str, expected: dict) -> dict: """Identify why LLM output failed""" issues = [] # Check format if expected.get("format") == "json": try: json.loads(output) except: issues.append({ "type": FailureType.FORMAT_VIOLATION, "details": "Invalid JSON output" }) # Check required fields if expected.get("required_fields"): for field in expected["required_fields"]: if field not in output: issues.append({ "type": FailureType.INCOMPLETE, "details": f"Missing required field: {field}" }) # Check constraints if expected.get("max_length"): if len(output) > expected["max_length"]: issues.append({ "type": FailureType.CONSTRAINT_BREAK, "details": f"Output too long: {len(output)} > {expected['max_length']}" }) # Check for hallucination indicators if contains_hallucination_marke
- Failure Taxonomy
- Root Cause Analysis
- Prompt Fixes
- Tool Improvements
- Test Case Generation
- Debugging Workflow
- Interactive Debugging
- Best Practices
- Output Checklist
What does the llm-debugger skill do?
Diagnoses LLM output failures including hallucinations, constraint violations, format errors, and reasoning issues. Provides root cause classification, prompt fixes, tool improvements, and new test cases. Use for "debugging AI", "fixing prompts", "quality issues", or "output errors".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill llm-debugger --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.