Agent skill

Debug With File

by catlog22 in catlog22/Claude-Code-Workflow

Interactive hypothesis-driven debugging with documented exploration, understanding evolution, and analysis-assisted correction.

MITAuto-check passedDevelopment

Install Debug With File

skills CLI
$ npx skills add catlog22/Claude-Code-Workflow --skill debug-with-file -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install catlog22/Claude-Code-Workflow debug-with-file --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/catlog22/Claude-Code-Workflow.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/debug-with-file .claude/skills/debug-with-file && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
debug-with-file
GitHub stars
2.1k
Token cost
~4k tokens
SKILL.md length
515 words
Files
1
Skills in repo
82
Repo updated
First seen
Licence
MIT

At a glance

Interactive hypothesis-driven debugging with documented exploration, understanding evolution, and analysis-assisted correction.

  • Tasks that involve Debugging
  • SKILL.md covers Overview, Target Bug, Project Context and Execution Process, plus 9 more sections
  • Calls git

What it does

Debug With File is an agent skill from catlog22/Claude-Code-Workflow. Interactive hypothesis-driven debugging with documented exploration, understanding evolution, and analysis-assisted correction.

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Debugging. The repository describes itself as: JSON-driven multi-agent cadence-team development framework with intelligent CLI orchestration (Gemini/Qwen/Codex), context-first architecture, and automated workflow execution. The licence is MIT.

When your agent uses it

  • Tasks that involve Debugging

Example prompts

  • “/debug-with-file”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 07491b0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Debug With File loads about 4k tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 515 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from catlog22/Claude-Code-Workflow at commit 07491b0, republished under its MIT licence (© catlog22). 515 words, ~3,977 tokens.

Download SKILL.mdSave it as .claude/skills/debug-with-file/SKILL.md (or your agent's skills folder).
name
debug-with-file
description
Interactive hypothesis-driven debugging with documented exploration, understanding evolution, and analysis-assisted correction.
argument-hint
BUG="<bug description or error message>"

Codex Debug-With-File Prompt

Overview

Enhanced evidence-based debugging with documented exploration process. Records understanding evolution, consolidates insights, and uses analysis to correct misunderstandings.

Core workflow: Explore → Document → Log → Analyze → Correct Understanding → Fix → Verify

Key enhancements over /prompts:debug:

  • understanding.md: Timeline of exploration and learning
  • Analysis-assisted correction: Validates and corrects hypotheses
  • Consolidation: Simplifies proven-wrong understanding to avoid clutter
  • Learning retention: Preserves what was learned, even from failed attempts

Target Bug

$BUG

Project Context

Run ccw spec load --category debug for known issues, workarounds, and root-cause notes.

Execution Process

Session Detection:
   ├─ Check if debug session exists for this bug
   ├─ EXISTS + understanding.md exists → Continue mode
   └─ NOT_FOUND → Explore mode

Explore Mode:
   ├─ Locate error source in codebase
   ├─ Document initial understanding in understanding.md
   ├─ Generate testable hypotheses with analysis validation
   ├─ Add NDJSON logging instrumentation
   └─ Output: Hypothesis list + await user reproduction

Analyze Mode:
   ├─ Parse debug.log, validate each hypothesis
   ├─ Use analysis to evaluate hypotheses and correct understanding
   ├─ Update understanding.md with:
   │   ├─ New evidence
   │   ├─ Corrected misunderstandings (strikethrough + correction)
   │   └─ Consolidated current understanding
   └─ Decision:
       ├─ Confirmed → Fix root cause
       ├─ Inconclusive → Add more logging, iterate
       └─ All rejected → Assisted new hypotheses

Fix & Cleanup:
   ├─ Apply fix based on confirmed hypothesis
   ├─ User verifies
   ├─ Document final understanding + lessons learned
   ├─ Remove debug instrumentation
   └─ If not fixed → Return to Analyze mode

Implementation Details

Session Setup & Mode Detection
Step 0: Determine Project Root

检测项目根目录,确保 .workflow/ 产物位置正确:

bash
PROJECT_ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd)

优先通过 git 获取仓库根目录;非 git 项目回退到 pwd 取当前绝对路径。 存储为 {projectRoot},后续所有 .workflow/ 路径必须以此为前缀。

javascript
const getUtc8ISOString = () => new Date(Date.now() + 8 * 60 * 60 * 1000).toISOString()
const projectRoot = bash('git rev-parse --show-toplevel 2>/dev/null || pwd').trim()

const bugSlug = "$BUG".toLowerCase().replace(/[^a-z0-9]+/g, '-').substring(0, 30)
const dateStr = getUtc8ISOString().substring(0, 10)

const sessionId = `DBG-${dateStr}-${bugSlug}`
const sessionFolder = `${projectRoot}/.workflow/.debug/${sessionId}`
const debugLogPath = `${sessionFolder}/debug.log`
const understandingPath = `${sessionFolder}/understanding.md`
const hypothesesPath = `${sessionFolder}/hypotheses.json`

// Auto-detect mode
const sessionExists = fs.existsSync(sessionFolder)
const hasUnderstanding = sessionExists && fs.existsSync(understandingPath)
const logHasContent = sessionExists && fs.existsSync(debugLogPath) && fs.statSync(debugLogPath).size > 0

const mode = logHasContent ? 'analyze' : (hasUnderstanding ? 'continue' : 'explore')

if (!sessionExists) {
  bash(`mkdir -p ${sessionFolder}`)
}
Explore Mode
Step 1.1: Locate Error Source
javascript
// Extract keywords from bug description
const keywords = extractErrorKeywords("$BUG")

// Search codebase for error locations
const searchResults = []
for (const keyword of keywords) {
  const results = Grep({ pattern: keyword, path: ".", output_mode: "content", "-C": 3 })
  searchResults.push({ keyword, results })
}

// Identify affected files and functions
const affectedLocations = analyzeSearchResults(searchResults)
Step 1.2: Document Initial Understanding

Create understanding.md:

markdown
# Understanding Document

**Session ID**: ${sessionId}
**Bug Description**: $BUG
**Started**: ${getUtc8ISOString()}

---

## Exploration Timeline

### Iteration 1 - Initial Exploration (${timestamp})

#### Current Understanding

Based on bug description and initial code search:

- Error pattern: ${errorPattern}
- Affected areas: ${affectedLocations.map(l => l.file).join(', ')}
- Initial hypothesis: ${initialThoughts}

#### Evidence from Code Search

${searchResults.map(r => `
**Keyword: "${r.keyword}"**
- Found in: ${r.results.files.join(', ')}
- Key findings: ${r.insights}
`).join('\n')}

#### Next Steps

- Generate testable hypotheses
- Add instrumentation
- Await reproduction

---

## Current Consolidated Understanding

${initialConsolidatedUnderstanding}
Step 1.3: Generate Hypotheses

Analyze the bug and generate 3-5 testable hypotheses:

javascript
// Hypothesis generation based on error pattern
const HYPOTHESIS_PATTERNS = {
  "not found|missing|undefined|未找到": "data_mismatch",
  "0|empty|zero|registered": "logic_error",
  "timeout|connection|sync": "integration_issue",
  "type|format|parse": "type_mismatch"
}

function generateHypotheses(bugDescription, affectedLocations) {
  // Generate targeted hypotheses based on error analysis
  // Each hypothesis includes:
  // - id: H1, H2, ...
  // - description: What might be wrong
  // - testable_condition: What to log
  // - logging_point: Where to add instrumentation
  // - evidence_criteria: What confirms/rejects it
  return hypotheses
}

Save to hypotheses.json:

json
{
  "iteration": 1,
  "timestamp": "2025-01-21T10:00:00+08:00",
  "hypotheses": [
    {
      "id": "H1",
      "description": "Data structure mismatch - expected key not present",
      "testable_condition": "Check if target key exists in dict",
      "logging_point": "file.py:func:42",
      "evidence_criteria": {
        "confirm": "data shows missing key",
        "reject": "key exists with valid value"
      },
      "likelihood": 1,
      "status": "pending"
    }
  ]
}
Step 1.4: Add NDJSON Instrumentation

For each hypothesis, add logging at the specified location:

Python template:

python
# region debug [H{n}]
try:
    import json, time
    _dbg = {
        "sid": "{sessionId}",
        "hid": "H{n}",
        "loc": "{file}:{line}",
        "msg": "{testable_condition}",
        "data": {
            # Capture relevant values here
        },
        "ts": int(time.time() * 1000)
    }
    with open(r"{debugLogPath}", "a", encoding="utf-8") as _f:
        _f.write(json.dumps(_dbg, ensure_ascii=False) + "\n")
except: pass
# endregion

JavaScript/TypeScript template:

javascript
// region debug [H{n}]
try {
  require('fs').appendFileSync("{debugLogPath}", JSON.stringify({
    sid: "{sessionId}",
    hid: "H{n}",
    loc: "{file}:{line}",
    msg: "{testable_condition}",
    data: { /* Capture relevant values */ },
    ts: Date.now()
  }) + "\n");
} catch(_) {}
// endregion
Step 1.5: Output to User
## Hypotheses Generated

Based on error "$BUG", generated {n} hypotheses:

{hypotheses.map(h => `
### ${h.id}: ${h.description}
- Logging at: ${h.logging_point}
- Testing: ${h.testable_condition}
- Evidence to confirm: ${h.evidence_criteria.confirm}
- Evidence to reject: ${h.evidence_criteria.reject}
`).join('')}

**Debug log**: ${debugLogPath}

**Next**: Run reproduction steps, then come back for analysis.
Analyze Mode
Step 2.1: Parse Debug Log
javascript
// Parse NDJSON log
const entries = Read(debugLogPath).split('\n')
  .filter(l => l.trim())
  .map(l => JSON.parse(l))

// Group by hypothesis
const byHypothesis = groupBy(entries, 'hid')

// Validate each hypothesis
for (const [hid, logs] of Object.entries(byHypothesis)) {
  const hypothesis = hypotheses.find(h => h.id === hid)
  const latestLog = logs[logs.length - 1]

  // Check if evidence confirms or rejects hypothesis
  const verdict = evaluateEvidence(hypothesis, latestLog.data)
  // Returns: 'confirmed' | 'rejected' | 'inconclusive'
}
Step 2.2: Analyze Evidence and Correct Understanding

Review the debug log and evaluate each hypothesis:

  1. Parse all log entries
  2. Group by hypothesis ID
  3. Compare evidence against expected criteria
  4. Determine verdict: confirmed | rejected | inconclusive
  5. Identify incorrect assumptions from previous understanding
  6. Generate corrections
Step 2.3: Update Understanding with Corrections

Append new iteration to understanding.md:

markdown
### Iteration ${n} - Evidence Analysis (${timestamp})

#### Log Analysis Results

${results.map(r => `
**${r.id}**: ${r.verdict.toUpperCase()}
- Evidence: ${JSON.stringify(r.evidence)}
- Reasoning: ${r.reason}
`).join('\n')}

#### Corrected Understanding

Previous misunderstandings identified and corrected:

${corrections.map(c => `
- ~~${c.wrong}~~ → ${c.corrected}
  - Why wrong: ${c.reason}
  - Evidence: ${c.evidence}
`).join('\n')}

#### New Insights

${newInsights.join('\n- ')}

${confirmedHypothesis ? `
#### Root Cause Identified

**${confirmedHypothesis.id}**: ${confirmedHypothesis.description}

Evidence supporting this conclusion:
${confirmedHypothesis.supportingEvidence}
` : `
#### Next Steps

${nextSteps}
`}

---

## Current Consolidated Understanding (Updated)

${consolidatedUnderstanding}
Step 2.4: Update hypotheses.json
json
{
  "iteration": 2,
  "timestamp": "2025-01-21T10:15:00+08:00",
  "hypotheses": [
    {
      "id": "H1",
      "status": "rejected",
      "verdict_reason": "Evidence shows key exists with valid value",
      "evidence": {...}
    },
    {
      "id": "H2",
      "status": "confirmed",
      "verdict_reason": "Log data confirms timing issue",
      "evidence": {...}
    }
  ],
  "corrections": [
    {
      "wrong_assumption": "...",
      "corrected_to": "...",
      "reason": "..."
    }
  ]
}
Fix & Verification
Step 3.1: Apply Fix

Based on confirmed hypothesis, implement the fix in the affected files.

Step 3.2: Document Resolution

Append to understanding.md:

markdown
### Iteration ${n} - Resolution (${timestamp})

#### Fix Applied

- Modified files: ${modifiedFiles.join(', ')}
- Fix description: ${fixDescription}
- Root cause addressed: ${rootCause}

#### Verification Results

${verificationResults}

#### Lessons Learned

What we learned from this debugging session:

1. ${lesson1}
2. ${lesson2}
3. ${lesson3}

#### Key Insights for Future

- ${insight1}
- ${insight2}
Step 3.3: Cleanup

Remove debug instrumentation by searching for region markers:

javascript
const instrumentedFiles = Grep({
  pattern: "# region debug|// region debug",
  output_mode: "files_with_matches"
})

for (const file of instrumentedFiles) {
  // Remove content between region markers
  removeDebugRegions(file)
}

Session Folder Structure

{projectRoot}/.workflow/.debug/DBG-{date}-{slug}/
├── debug.log           # NDJSON log (execution evidence)
├── understanding.md    # Exploration timeline + consolidated understanding
└── hypotheses.json     # Hypothesis history with verdicts

Understanding Document Template

markdown
# Understanding Document

**Session ID**: DBG-xxx-2025-01-21
**Bug Description**: [original description]
**Started**: 2025-01-21T10:00:00+08:00

---

## Exploration Timeline

### Iteration 1 - Initial Exploration (2025-01-21 10:00)

#### Current Understanding
...

#### Evidence from Code Search
...

#### Hypotheses Generated
...

### Iteration 2 - Evidence Analysis (2025-01-21 10:15)

#### Log Analysis Results
...

#### Corrected Understanding
- ~~[wrong]~~ → [corrected]

#### Analysis Results
...

---

## Current Consolidated Understanding

### What We Know
- [valid understanding points]

### What Was Disproven
- ~~[disproven assumptions]~~

### Current Investigation Focus
[current focus]

### Remaining Questions
- [open questions]

Debug Log Format (NDJSON)

Each line is a JSON object:

json
{"sid":"DBG-xxx-2025-01-21","hid":"H1","loc":"file.py:func:42","msg":"Check dict keys","data":{"keys":["a","b"],"target":"c","found":false},"ts":1734567890123}
FieldDescription
sidSession ID
hidHypothesis ID (H1, H2, ...)
locCode location
msgWhat's being tested
dataCaptured values
tsTimestamp (ms)

Iteration Flow

First Call (BUG="error"):
   ├─ No session exists → Explore mode
   ├─ Extract error keywords, search codebase
   ├─ Document initial understanding in understanding.md
   ├─ Generate hypotheses
   ├─ Add logging instrumentation
   └─ Await user reproduction

After Reproduction (BUG="error"):
   ├─ Session exists + debug.log has content → Analyze mode
   ├─ Parse log, evaluate hypotheses
   ├─ Update understanding.md with:
   │   ├─ Evidence analysis results
   │   ├─ Corrected misunderstandings (strikethrough)
   │   ├─ New insights
   │   └─ Updated consolidated understanding
   ├─ Update hypotheses.json with verdicts
   └─ Decision:
       ├─ Confirmed → Fix → Document resolution
       ├─ Inconclusive → Add logging, document next steps
       └─ All rejected → Assisted new hypotheses

Output:
   ├─ {projectRoot}/.workflow/.debug/DBG-{date}-{slug}/debug.log
   ├─ {projectRoot}/.workflow/.debug/DBG-{date}-{slug}/understanding.md (evolving document)
   └─ {projectRoot}/.workflow/.debug/DBG-{date}-{slug}/hypotheses.json (history)
Show full SKILL.md (208 more words)Show less

Error Handling

SituationAction
Empty debug.logVerify reproduction triggered the code path
All hypotheses rejectedGenerate new hypotheses based on disproven assumptions
Fix doesn't workDocument failed fix attempt, iterate with refined understanding
>5 iterationsReview consolidated understanding, escalate with full context
Understanding too longConsolidate aggressively, archive old iterations to separate file

Consolidation Rules

When updating "Current Consolidated Understanding":

  1. Simplify disproven items: Move to "What Was Disproven" with single-line summary
  2. Keep valid insights: Promote confirmed findings to "What We Know"
  3. Avoid duplication: Don't repeat timeline details in consolidated section
  4. Focus on current state: What do we know NOW, not the journey
  5. Preserve key corrections: Keep important wrong→right transformations for learning

Bad (cluttered):

markdown
## Current Consolidated Understanding

In iteration 1 we thought X, but in iteration 2 we found Y, then in iteration 3...
Also we checked A and found B, and then we checked C...

Good (consolidated):

markdown
## Current Consolidated Understanding

### What We Know
- Error occurs during runtime update, not initialization
- Config value is None (not missing key)

### What Was Disproven
- ~~Initialization error~~ (Timing evidence)
- ~~Missing key hypothesis~~ (Key exists)

### Current Investigation Focus
Why is config value None during update?

Key Features

FeatureDescription
NDJSON loggingStructured debug log with hypothesis tracking
Hypothesis generationAnalysis-assisted hypothesis creation
Exploration documentationunderstanding.md with timeline
Understanding evolutionTimeline + corrections tracking
Error correctionStrikethrough + reasoning for wrong assumptions
Consolidated learningCurrent understanding section
Hypothesis historyhypotheses.json with verdicts
Analysis validationAt key decision points

When to Use

Best suited for:

  • Complex bugs requiring multiple investigation rounds
  • Learning from debugging process is valuable
  • Team needs to understand debugging rationale
  • Bug might recur, documentation helps prevention

Now execute the debug-with-file workflow for bug: $BUG

© catlog22, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .codex/skills/debug-with-file of catlog22/Claude-Code-Workflow.

Open the folder on GitHubat commit 07491b0

Compare with similar skills

Debug With File next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Debug With File compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Debug With File this skillcatlog22/Claude-Code-Workflow2.1k—~4kAutomated safety check: PassMIT
Trellis Session Insightmindfold-ai/Trellis15k4 repos~1.7kAutomated safety check: PassAGPL-3.0
Native Data FetchingCherryHQ/cherry-studio-app4k6 repos~2.9kAutomated safety check: NotesMIT
Debugging Executionsn8n-io/n8n207k—~2.6kAutomated safety check: PassCustom licence
Aoti Debugpytorch/pytorch104k1 repos~1.7kAutomated safety check: PassCustom licence
Herdr Throwaway Reproductionherdrdev/herdr43k—~2.4kAutomated safety check: PassApache-2.0

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Categories

Questions about Debug With File

What does Debug With File do?

Interactive hypothesis-driven debugging with documented exploration, understanding evolution, and analysis-assisted correction. Debug With File is an agent skill from catlog22/Claude-Code-Workflow. Interactive hypothesis-driven debugging with documented exploration, understanding evolution, and analysis-assisted correction.

When should I use Debug With File?

Debug With File fits situations like: tasks that involve Debugging.

How do I install Debug With File in Claude Code?

Run `npx skills add catlog22/Claude-Code-Workflow --skill debug-with-file -a claude-code`. Or copy the skill folder (.codex/skills/debug-with-file in catlog22/Claude-Code-Workflow) into .claude/skills/debug-with-file in your project. Claude Code loads it when a task matches its description.

How do I install Debug With File in Codex?

Run `npx skills add catlog22/Claude-Code-Workflow --skill debug-with-file -a codex`. Or copy the skill folder (.codex/skills/debug-with-file in catlog22/Claude-Code-Workflow) into .agents/skills/debug-with-file in your project. Codex loads it when a task matches its description.

Can I use Debug With File in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add catlog22/Claude-Code-Workflow --skill debug-with-file -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/debug-with-file, .gemini/skills/debug-with-file, .github/skills/debug-with-file and .opencode/skills/debug-with-file in your project.

What does Debug With File need to run?

Going by SKILL.md and its folder, Debug With File needs the command-line tools its instructions call (git). Our summary lists: Python 3.

Does Debug With File access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Debug With File safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Debug With File use?

Debug With File is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Debug With File use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Debug With File?

Skills that share tags, products or a category with Debug With File: Trellis Session Insight (mindfold-ai/Trellis, 15k stars), Native Data Fetching (CherryHQ/cherry-studio-app, 4k stars), Debugging Executions (n8n-io/n8n, 207k stars) and Aoti Debug (pytorch/pytorch, 104k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Debug With File?

catlog22 (a GitHub user) maintains it in catlog22/Claude-Code-Workflow, which has 2,130 GitHub stars. The repository holds 82 skills in this directory. The repository was last updated on June 18, 2026.

Source: catlog22/Claude-Code-Workflow on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.