debug-with-file

5
0
Source

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

Install

mkdir -p .claude/skills/debug-with-file && curl -L -o skill.zip "https://mcp.directory/api/skills/download/2368" && unzip -o skill.zip -d .claude/skills/debug-with-file && rm skill.zip

Installs to .claude/skills/debug-with-file

About this skill

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:

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。

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-${bugSlug}-${dateStr}`
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

// 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:

# 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:

// 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:

{
  "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:

# 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:

// 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

// 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:

### 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

{
  "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:

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

#### Fix Applied

- Modified files: ${mo

---

*Content truncated.*

You might also like

flutter-development

aj-geddes

Build beautiful cross-platform mobile apps with Flutter and Dart. Covers widgets, state management with Provider/BLoC, navigation, API integration, and material design.

643969

drawio-diagrams-enhanced

jgtolentino

Create professional draw.io (diagrams.net) diagrams in XML format (.drawio files) with integrated PMP/PMBOK methodologies, extensive visual asset libraries, and industry-standard professional templates. Use this skill when users ask to create flowcharts, swimlane diagrams, cross-functional flowcharts, org charts, network diagrams, UML diagrams, BPMN, project management diagrams (WBS, Gantt, PERT, RACI), risk matrices, stakeholder maps, or any other visual diagram in draw.io format. This skill includes access to custom shape libraries for icons, clipart, and professional symbols.

591705

ui-ux-pro-max

nextlevelbuilder

"UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 8 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, mobile app, .html, .tsx, .vue, .svelte. Elements: button, modal, navbar, sidebar, card, table, form, chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, flat design. Topics: color palette, accessibility, animation, layout, typography, font pairing, spacing, hover, shadow, gradient."

318398

godot

bfollington

This skill should be used when working on Godot Engine projects. It provides specialized knowledge of Godot's file formats (.gd, .tscn, .tres), architecture patterns (component-based, signal-driven, resource-based), common pitfalls, validation tools, code templates, and CLI workflows. The `godot` command is available for running the game, validating scripts, importing resources, and exporting builds. Use this skill for tasks involving Godot game development, debugging scene/resource files, implementing game systems, or creating new Godot components.

339397

nano-banana-pro

garg-aayush

Generate and edit images using Google's Nano Banana Pro (Gemini 3 Pro Image) API. Use when the user asks to generate, create, edit, modify, change, alter, or update images. Also use when user references an existing image file and asks to modify it in any way (e.g., "modify this image", "change the background", "replace X with Y"). Supports both text-to-image generation and image-to-image editing with configurable resolution (1K default, 2K, or 4K for high resolution). DO NOT read the image file first - use this skill directly with the --input-image parameter.

451339

fastapi-templates

wshobson

Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.

304231

Stay ahead of the MCP ecosystem

Get weekly updates on new skills and servers.