parallel-debugging
Allows the agent to debug complex issues with parallel investigations and root cause analysis.
What it does
Helps find the cause of complex software problems by investigating multiple explanations simultaneously. It organizes the search for clues and decides which explanation is most likely.
When to use it
- ✓When a bug has multiple possible causes.
- ✓When initial attempts to fix the bug have failed.
- ✓When the issue affects different parts of the program.
Example of what to ask your AI
“I'm getting a strange error on my website where buttons sometimes don't work. Can you help me investigate the possible causes?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI will brainstorm several reasons for the problem, such as logic errors, bad data, or connection issues. It will ask you to look for 'clues' (evidence) that confirm or deny each reason and finally state the most likely cause.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add wshobson/agents --skill parallel-debuggingOr download just the file:
curl -L https://raw.githubusercontent.com/wshobson/agents/main/plugins/agent-teams/skills/parallel-debugging/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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
FAQ
What are 'hypotheses'?
These are the possible explanations or causes for the problem that the AI will investigate.
What is 'evidence'?
These are the clues or concrete information (like code snippets or error messages) that help prove or disprove a hypothesis.
How does the AI decide the root cause?
It compares the hypotheses against the evidence found. The hypothesis with the best and strongest evidence, and which explains the problem without contradictions, is considered the root cause.
Details
Original description by the author
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.
repo: wshobson/agents
Popularity
- GitHub stars
- 41k
- Ranking position
- #4795
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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