screen-reader-testing
Test web applications with screen readers including VoiceOver, NVDA, and JAWS. Use when validating screen reader compatibility, debugging accessibility issues,…
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 -y skills add wshobson/agents --skill parallel-debugging --agent claude-codeHow it fires
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/parallel-debuggingContext preview
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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.
name: parallel-debugging description: 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. version: 1.0.2
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:
1. Confidence level 2. Number of supporting evidence pieces 3. Strength of causal chain 4. Absence of contradicting evidence
Before declaring the bug fixed:
Production-ready agentic workflow building blocks: 94 plugins, 202 agents, 183 skills, 105 commands — built for Claude Code and consumed natively by OpenAI Codex CLI, Cursor, OpenCode, the Antigravity CLI, GitHub Copilot, and Pi from a single Markdown source.
Repo: wshobson/agents
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