version: "1.0.1" name: debug description: Guide systematic debugging through hypothesis generation, evidence collection, and verification. Uses scientific method for root cause analysis. license: MIT compatibility:

• runtime:any

allowed-tools:

• Read
• Glob
• Grep
• Bash(git:log)
• Bash(git:diff)
• Bash(git:blame)
• Bash(git:show)

metadata: author: thoreinstein version: 1.0.0

Debug

Systematic debugging through hypothesis generation, evidence collection, and verification.

When to Use This Skill

• When facing a bug with unclear root cause
• When initial debugging attempts haven't resolved the issue
• When you need structured investigation rather than guesswork
• When documenting a debugging session for future reference

Input

Provide as much of the following as available:

1. Problem Statement

• What's the expected behavior?
• What's the actual behavior?
• Steps to reproduce

1. Context

• Recent changes that might be related
• Error messages or logs
• Environment details (OS, versions, etc.)

1. Initial Hypotheses (if any)

• What do you think might be causing it?
• What have you already ruled out?

Debugging Framework

Step 1: Problem Definition

Clearly define:

• Expected vs actual behavior
• Reproduction steps (minimal if possible)
• Scope: when does it happen, when doesn't it?
• Impact: severity and affected users/systems

Step 2: Hypothesis Generation

Generate ranked hypotheses:

• List possible causes
• Assign probability (likely, possible, unlikely)
• Note evidence for and against each
• Identify what would confirm or rule out each

Step 3: Investigation Plan

For each hypothesis, plan:

• What evidence to look for
• Where to look (files, logs, git history)
• What tests would confirm/deny
• Order of investigation (highest probability first)

Step 4: Evidence Collection

Investigate systematically:

• Search codebase for relevant patterns
• Review git history for recent changes
• Examine logs and error messages
• Document findings with timestamps

Step 5: Root Cause Identification

Synthesize findings:

• Identify the root cause (not just symptoms)
• Note contributing factors
• Document code locations involved
• Explain the causal chain

Investigation Tracks

Run these investigation tracks in parallel where possible:

Output

Produce a debug session report following the template in references/debug-session-template.md.

The report should include:

• Problem statement with reproduction steps
• Hypotheses with evidence assessment
• Investigation log with timestamps
• Root cause and contributing factors
• Recommended fix and prevention measures

Constraints

• Hypotheses first - generate hypotheses before diving into code
• Evidence-based - support conclusions with specific evidence
• Document as you go - keep an investigation log
• Root cause, not symptoms - dig until you find the actual cause
• Prevention - recommend how to prevent recurrence

Example

Example: Intermittent API Timeout

Problem: /api/users endpoint times out ~10% of requests

Hypotheses:

H1: Database query is slow (70% likely)
    Evidence for: Timeout correlates with high DB load
    Evidence against: None yet
    Test: Check query execution time
 
H2: Connection pool exhaustion (20% likely)
    Evidence for: Happens under load
    Evidence against: Pool metrics look normal
    Test: Monitor pool during timeout
 
H3: External service dependency (10% likely)
    Evidence for: None
    Evidence against: No external calls in this endpoint
    Test: Trace request path

Investigation:

10:15 - Checked slow query log → found users query taking 2-5s
10:22 - git blame on users.go → query changed 3 days ago
10:28 - Compared old vs new query → missing index on new column
10:35 - Confirmed: new filter column not indexed

Root Cause: Query added filter on last_login column which lacks an index. Under load, full table scan causes timeouts.

Fix: Add index on users.last_login

Prevention: Add query plan review to PR checklist for database changes.

Begin by clearly defining the problem and generating hypotheses before investigating.