Agent Skills: systematic-debugging

Structured debugging methodology using hypothesis-driven investigation, log analysis, and bisection to isolate and resolve defects.

UncategorizedID: a5c-ai/babysitter/systematic-debugging

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pnpm dlx add-skill https://github.com/a5c-ai/babysitter/tree/HEAD/library/methodologies/rpikit/skills/systematic-debugging

Skill Files

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library/methodologies/rpikit/skills/systematic-debugging/SKILL.md

Skill Metadata

Name
systematic-debugging
Description
Structured debugging methodology using hypothesis-driven investigation, log analysis, and bisection to isolate and resolve defects.
  • Unexpected behavior discovered during testing
  • Bug reports require investigation
  • Performance issues need root cause analysis

Process

  1. Reproduce - Confirm the defect with a minimal reproduction
  2. Hypothesize - Form theories about the root cause
  3. Investigate - Systematically test hypotheses (logs, breakpoints, bisection)
  4. Isolate - Narrow to the specific component/line
  5. Fix - Apply targeted fix addressing root cause
  6. Verify - Confirm fix resolves the issue without regression

Key Rules

  • Never apply fixes without understanding the root cause
  • For Strike-3/post-instrumentation handoffs, do not apply a source-code fix until you enumerate at least 3 candidate root-cause hypotheses, give each hypothesis a falsifying log line or observation, and cite concrete log evidence for the selected fix. Use seq number when present; otherwise cite timestamp, log-id, or artifact path plus the exact log line. If no proposed fix cites a specific log line or log record, mark needs-more-data.
  • Use web-researcher agent for unfamiliar error patterns
  • Document the investigation path for future reference
  • Verify that the fix does not introduce regressions

Tool Use

Integrated into methodologies/rpikit/rpikit-implement (failure handling)