parallel-debugging
π―SkillA method for finding the cause of hard bugs by testing several possible explanations at once. Each idea is checked against evidence from the code, and the best-supported one is chosen as the real cause.
Installation
npx skills add wshobson/agents --skill parallel-debuggingRun it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).
Uses skills.sh Β· Node.js 22.20+
After installing
Ask for related work and the agent uses it on its own, or call it yourself.
/parallel-debuggingInstall by hand
Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.
Open the skill folder β- This project
.claude/skills/parallel-debugging/ - All projects
~/.claude/skills/parallel-debugging/
These paths are for Claude Code. For other tools, the command above puts it in the right place.
What this skill does
Overview
Some bugs have many possible causes, and it is easy to fix the wrong one. This skill lists likely explanations across six areas, such as logic mistakes, bad data or setup problems. Several agents (AI helpers working side by side) can check them at the same time, and the results are compared by their evidence.
Key Features
- Come up with possible causes from six common types of failure.
- Rate each piece of evidence as strong, medium or weak.
- Point to exact code lines, like file.ts:42, to back up each claim.
- Sort ideas into confirmed, plausible, ruled out or unclear.
- Check a fix against a short list before calling the bug solved.
Who is this for?
It suits developers stuck on a bug that spans several parts of a system. It also helps teams avoid assuming their first guess is right.
Summarized by AI from this skill's SKILL.md. Suggest a better one
SKILL.md
View on GitHub βDebug 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.
Parallel Debugging
Framework for debugging complex issues using the Analysis of Competing Hypotheses (ACH) methodology with parallel agent investigation.
When to Use This Skill
- Bug has multiple plausible root causes
- Initial debugging attempts haven't identified the issue
- Issue spans multiple modules or components
- Need systematic root cause analysis with evidence
- Want to avoid confirmation bias in debugging
Hypothesis Generation Framework
Generate hypotheses across 6 failure mode categories:
1. Logic Error
- Incorrect conditional logic (wrong operator, missing case)
- Off-by-one errors in loops or array access
- Missing edge case handling
- Incorrect algorithm implementation
2. Data Issue
- Invalid or unexpected input data
- Type mismatch or coercion error
- Null/undefined/None where value expected
- Encoding or serialization problem
- Data truncation or overflow
3. State Problem
- Race condition between concurrent operations
- Stale cache returning outdated data
- Incorrect initialization or default values
- Unintended mutation of shared state
- State machine transition error
4. Integration Failure
- API contract violation (request/response mismatch)
- Version incompatibility between components
- Configuration mismatch between environments
- Missing or incorrect environment variables
- Network timeout or connection failure
5. Resource Issue
- Memory leak causing gradual degradation
- Connection pool exhaustion
- File descriptor or handle leak
- Disk space or quota exceeded
- CPU saturation from inefficient processing
6. Environment
- Missing runtime dependency
- Wrong library or framework version
- Platform-specific behavior difference
- Permission or access control issue
- Timezone or locale-related behavior
Evidence Collection Standards
What Constitutes Evidence
| 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 |
Citation Format
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.
Confidence Levels
| 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 |
Result Arbitration Protocol
After all investigators report:
Step 1: Categorize Results
- Confirmed: High confidence, strong evidence, clear causal chain
- Plausible: Medium confidence, some evidence, reasonable causal chain
- Falsified: Evidence contradicts the hypothesis
- Inconclusive: Insufficient evidence to confirm or falsify
Step 2: Compare Confirmed Hypotheses
If multiple hypotheses are confirmed, rank by:
- Confidence level
- Number of supporting evidence pieces
- Strength of causal chain
- Absence of contradicting evidence
Step 3: Determine Root Cause
- If one hypothesis clearly dominates: declare as root cause
- If multiple hypotheses are equally likely: may be compound issue (multiple contributing causes)
- If no hypotheses confirmed: generate new hypotheses based on evidence gathered
Step 4: Validate Fix
Before declaring the bug fixed:
- Fix addresses the identified root cause
- Fix doesn't introduce new issues
- Original reproduction case no longer fails
- Related edge cases are covered
- Relevant tests are added or updated
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