openclaw-skills/parallel-debugging/SKILL.md
Debug complex issues using competing hypotheses with parallel investigation, evidence collection, and root cause arbitration. Use this skill when debugging bugs with multiple potential causes, performing root cause analysis, or organizing parallel investigation workflows.
npx skillsauth add seaworld008/commonly-used-high-value-skills parallel-debuggingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Framework for debugging complex issues using the Analysis of Competing Hypotheses (ACH) methodology with parallel agent investigation.
Generate hypotheses across 6 failure mode categories:
| Evidence Type | Strength | Example |
| ----------------- | -------- | --------------------------------------------------------------- |
| Direct | Strong | Code at file.ts:42 shows if (x > 0) should be if (x >= 0) |
| Correlational | Medium | Error rate increased after commit abc123 |
| Testimonial | Weak | "It works on my machine" |
| Absence | Variable | No null check found in the code path |
Always cite evidence with file:line references:
**Evidence**: The validation function at `src/validators/user.ts:87`
does not check for empty strings, only null/undefined. This allows
empty email addresses to pass validation.
| Level | Criteria | | ------------------- | ----------------------------------------------------------------------------------- | | High (>80%) | Multiple direct evidence pieces, clear causal chain, no contradicting evidence | | Medium (50-80%) | Some direct evidence, plausible causal chain, minor ambiguities | | Low (<50%) | Mostly correlational evidence, incomplete causal chain, some contradicting evidence |
After all investigators report:
If multiple hypotheses are confirmed, rank by:
Before declaring the bug fixed:
development
Enumerating failure modes via pre-mortem analysis. Systematically identifies failure scenarios for plans, designs, and features, scoring them with RPN/AP. Does not write code.
testing
Orchestrating specialist AI agent teams as a meta-coordinator. Decomposes requests into minimum viable chains, spawns each as an independent session in AUTORUN modes, and drives to final output. Use when a task spans multiple specialist domains, requires parallel agent execution, or needs hub-and-spoke routing across the skill ecosystem.
development
Converting document formats (Markdown/Word/Excel/PDF/HTML). Converts specs from Scribe and reports from Harvest into distributable formats; generates reusable conversion scripts. Use when converting documents, building accessibility-compliant PDFs, or creating Pandoc/LibreOffice pipelines.
testing
Curating cross-agent knowledge and guarding institutional memory. Extracts patterns from agent journals into METAPATTERNS.md, detects knowledge decay, propagates best practices, prevents organizational forgetting. Use when consolidating cross-agent insights, curating memory, or auditing knowledge decay.