SkillAgentSearch skills...

parallel-debugging

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.

Install / Use

npx skills add wshobson/agents --skill parallel-debugging

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

92/100

Category

Automation

Supported Platforms

Universal

Tags

Our assessment of parallel-debugging

parallel-debugging scores 92/100 on our quality scale, 163rd of 1,111 Automation skills we index (top 15%).

Its SKILL.md is 4.6 KB long, well organised into 18 sections with 1 code example: a solid amount of guidance for an agent.

With 39,920 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
17/20
Description
15/15
Adoption
20/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 4 days ago, so parallel-debugging is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

parallel-debugging compared with similar skills

All 4 of these similar skills score higher than parallel-debugging; compare them before choosing.

SkillScoreStarsUpdatedFormat
parallel-debugging (this skill)by wshobson9239.9k4d agoSKILL.md
Agent-Reachby Panniantong10085.4k9d agoCLAUDE.md
rufloby ruvnet10073.2ktodayCLAUDE.md
Scraplingby D4Vinci10083.5ktodayMCP Server
algorithmic-artby anthropics100177.9k2d agoSKILL.md

Frequently asked questions

How do I install parallel-debugging?
Run npx skills add wshobson/agents --skill parallel-debugging. The install tabs above show the steps for each supported agent.
Which AI agents does parallel-debugging work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is parallel-debugging safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is parallel-debugging still maintained?
The repository was last updated 4 days ago, so parallel-debugging is actively maintained.

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

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:

  1. Confidence level
  2. Number of supporting evidence pieces
  3. Strength of causal chain
  4. 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

Related Skills

View on GitHub
GitHub Stars39.9k
CategoryAutomation
Updated4d ago
Forks4.3k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions