ai-factory.fix — ai-factory.fix install ai-factory.fix, community, ai-factory.fix install, ide skills, ai-factory.fix workflow, bug fixing with ai-factory.fix, ai-factory.fix for developers, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Perfect for Code Analysis Agents needing efficient bug-fixing workflows with direct codebase analysis. ai-factory.fix is a workflow-based skill that analyzes codebases to fix bugs, using project context and past experience from files like .ai-factory/DESCRIPTION.md and .ai-factory/patches/

Features

Loads project context from .ai-factory/DESCRIPTION.md to understand tech stack, architecture, and coding conventions
Reads all patches from .ai-factory/patches/ directory using Glob to find *.md files
Analyzes codebase directly to fix specific bugs or problems
Uses past experience from patch files to inform bug-fixing decisions
Streamlines bug-fixing workflow by eliminating the need for plans and reports

# Core Topics

spraby spraby
[0]
[0]
Updated: 3/8/2026

Agent Capability Analysis

The ai-factory.fix skill by spraby is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for ai-factory.fix install, ai-factory.fix workflow, bug fixing with ai-factory.fix.

Ideal Agent Persona

Perfect for Code Analysis Agents needing efficient bug-fixing workflows with direct codebase analysis.

Core Value

Empowers agents to directly fix specific bugs or problems by analyzing the codebase, utilizing project context from `.ai-factory/DESCRIPTION.md` and patch history from `.ai-factory/patches/` directory, streamlining the debugging process with Glob for patch file discovery.

Capabilities Granted for ai-factory.fix

Debugging code issues with direct patch application
Analyzing project architecture for bug fixing
Applying coding conventions for consistent fixes

! Prerequisites & Limits

  • Requires `.ai-factory/DESCRIPTION.md` and `.ai-factory/patches/` directory for context
  • Limited to projects with accessible codebase and patch history
Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

ai-factory.fix

Install ai-factory.fix, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly

Fix - Quick Bug Fix Workflow

Fix a specific bug or problem by analyzing the codebase directly. No plans, no reports.

Workflow

Step 0: Load Project Context & Past Experience

Read .ai-factory/DESCRIPTION.md if it exists to understand:

  • Tech stack (language, framework, database)
  • Project architecture
  • Coding conventions

Read all patches from .ai-factory/patches/ if the directory exists:

  • Use Glob to find all *.md files in .ai-factory/patches/
  • Read each patch file to learn from past fixes
  • Pay attention to recurring patterns, root causes, and solutions
  • If the current problem resembles a past patch — apply the same approach or avoid the same mistakes
  • This is your accumulated experience. Use it.

Step 1: Understand the Problem

From $ARGUMENTS, identify:

  • Error message or unexpected behavior
  • Where it occurs (file, function, endpoint)
  • Steps to reproduce (if provided)

If unclear, ask:

To fix this effectively, I need more context:

1. What is the expected behavior?
2. What actually happens?
3. Can you share the error message/stack trace?
4. When did this start happening?

Step 2: Investigate the Codebase

Search for the problem:

  • Find relevant files using Glob/Grep
  • Read the code around the issue
  • Trace the data flow
  • Check for similar patterns elsewhere

Look for:

  • The root cause (not just symptoms)
  • Related code that might be affected
  • Existing error handling

Step 3: Implement the Fix

Apply the fix with logging:

typescript
1// ✅ REQUIRED: Add logging around the fix 2console.log('[FIX] Processing user input', { userId, input }); 3 4try { 5 // The actual fix 6 const result = fixedLogic(input); 7 console.log('[FIX] Success', { userId, result }); 8 return result; 9} catch (error) { 10 console.error('[FIX] Error in fixedLogic', { 11 userId, 12 input, 13 error: error.message, 14 stack: error.stack 15 }); 16 throw error; 17}

Logging is MANDATORY because:

  • User needs to verify the fix works
  • If it doesn't work, logs help debug further
  • Feedback loop: user provides logs → we iterate

Step 4: Verify the Fix

  • Check the code compiles/runs
  • Verify the logic is correct
  • Ensure no regressions introduced

Step 5: Suggest Test Coverage

ALWAYS suggest covering this case with a test:

## Fix Applied ✅

The issue was: [brief explanation]
Fixed by: [what was changed]

### Logging Added
The fix includes logging with prefix `[FIX]`.
Please test and share any logs if issues persist.

### Recommended: Add a Test

This bug should be covered by a test to prevent regression:

\`\`\`typescript
describe('functionName', () => {
  it('should handle [the edge case that caused the bug]', () => {
    // Arrange
    const input = /* the problematic input */;

    // Act
    const result = functionName(input);

    // Assert
    expect(result).toBe(/* expected */);
  });
});
\`\`\`

Would you like me to create this test?
- [ ] Yes, create the test
- [ ] No, skip for now

Logging Requirements

All fixes MUST include logging:

  1. Log prefix: Use [FIX] or [FIX:<issue-id>] for easy filtering
  2. Log inputs: What data was being processed
  3. Log success: Confirm the fix worked
  4. Log errors: Full context if something fails
  5. Configurable: Use LOG_LEVEL if available
typescript
1// Pattern for fixes 2const LOG_FIX = process.env.LOG_LEVEL === 'debug' || process.env.DEBUG_FIX; 3 4function fixedFunction(input) { 5 if (LOG_FIX) console.log('[FIX] Input:', input); 6 7 // ... fix logic ... 8 9 if (LOG_FIX) console.log('[FIX] Output:', result); 10 return result; 11}

Examples

Example 1: Null Reference Error

User: /fix TypeError: Cannot read property 'name' of undefined in UserProfile

Actions:

  1. Search for UserProfile component/function
  2. Find where .name is accessed
  3. Add null check with logging
  4. Suggest test for null user case

Example 2: API Returns Wrong Data

User: /fix /api/orders returns empty array for authenticated users

Actions:

  1. Find orders API endpoint
  2. Trace the query logic
  3. Find the bug (e.g., wrong filter)
  4. Fix with logging
  5. Suggest integration test

Example 3: Form Validation Not Working

User: /fix email validation accepts invalid emails

Actions:

  1. Find email validation logic
  2. Check regex or validation library usage
  3. Fix the validation
  4. Add logging for validation failures
  5. Suggest unit test with edge cases

Important Rules

  1. NO plans - This is a direct fix, not planned work
  2. NO reports - Don't create summary documents
  3. ALWAYS log - Every fix must have logging for feedback
  4. ALWAYS suggest tests - Help prevent regressions
  5. Root cause - Fix the actual problem, not symptoms
  6. Minimal changes - Don't refactor unrelated code
  7. One fix at a time - Don't scope creep

After Fixing

## Fix Applied ✅

**Issue:** [what was broken]
**Cause:** [why it was broken]
**Fix:** [what was changed]

**Files modified:**
- path/to/file.ts (line X)

**Logging added:** Yes, prefix `[FIX]`
**Test suggested:** Yes

Please test the fix and share logs if any issues.

To add the suggested test:
- [ ] Yes, create test
- [ ] No, skip

Step 6: Create Self-Improvement Patch

ALWAYS create a patch after every fix. This builds a knowledge base for future fixes.

Create the patch:

  1. Create directory if it doesn't exist:

    bash
    1mkdir -p .ai-factory/patches
  2. Create a patch file with the current timestamp as filename. Format: YYYY-MM-DD-HH.mm.md (e.g., 2026-02-07-14.30.md)

  3. Use this template:

markdown
1# [Brief title describing the fix] 2 3**Date:** YYYY-MM-DD HH:mm 4**Files:** list of modified files 5**Severity:** low | medium | high | critical 6 7## Problem 8 9What was broken. How it manifested (error message, wrong behavior). 10Be specific — include the actual error or symptom. 11 12## Root Cause 13 14WHY the problem occurred. This is the most valuable part. 15Not "what was wrong" but "why it was wrong": 16- Logic error? Why was the logic incorrect? 17- Missing check? Why was it missing? 18- Wrong assumption? What was assumed? 19- Race condition? What sequence caused it? 20 21## Solution 22 23How the fix was implemented. Key code changes and reasoning. 24Include the approach, not just "changed line X". 25 26## Prevention 27 28How to prevent this class of problems in the future: 29- What pattern/practice should be followed? 30- What should be checked during code review? 31- What test would catch this? 32 33## Tags 34 35Space-separated tags for categorization, e.g.: 36`#null-check` `#async` `#validation` `#typescript` `#api` `#database`

Example patch:

markdown
1# Null reference in UserProfile when user has no avatar 2 3**Date:** 2026-02-07 14:30 4**Files:** src/components/UserProfile.tsx 5**Severity:** medium 6 7## Problem 8 9TypeError: Cannot read property 'url' of undefined when rendering 10UserProfile for users without an uploaded avatar. 11 12## Root Cause 13 14The `user.avatar` field is optional in the database schema but the 15component accessed `user.avatar.url` without a null check. This was 16introduced in commit abc123 when avatar display was added — the 17developer tested only with users that had avatars. 18 19## Solution 20 21Added optional chaining: `user.avatar?.url` with a fallback to a 22default avatar URL. Also added a null check in the Avatar sub-component. 23 24## Prevention 25 26- Always check if database fields marked as `nullable` / `optional` 27 are handled with null checks in the UI layer 28- Add test cases for "empty state" — user with minimal data 29- Consider a lint rule for accessing nested optional properties 30 31## Tags 32 33`#null-check` `#react` `#optional-field` `#typescript`

This is NOT optional. Every fix generates a patch. The patch is your learning.


DO NOT:

  • ❌ Create PLAN.md or any plan files
  • ❌ Generate reports or summaries (patches are NOT reports — they are learning artifacts)
  • ❌ Refactor unrelated code
  • ❌ Add features while fixing
  • ❌ Skip logging
  • ❌ Skip test suggestion
  • ❌ Skip patch creation

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is ai-factory.fix?

Perfect for Code Analysis Agents needing efficient bug-fixing workflows with direct codebase analysis. ai-factory.fix is a workflow-based skill that analyzes codebases to fix bugs, using project context and past experience from files like .ai-factory/DESCRIPTION.md and .ai-factory/patches/

How do I install ai-factory.fix?

Run the command: npx killer-skills add spraby/api/ai-factory.fix. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for ai-factory.fix?

Key use cases include: Debugging code issues with direct patch application, Analyzing project architecture for bug fixing, Applying coding conventions for consistent fixes.

Which IDEs are compatible with ai-factory.fix?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for ai-factory.fix?

Requires `.ai-factory/DESCRIPTION.md` and `.ai-factory/patches/` directory for context. Limited to projects with accessible codebase and patch history.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add spraby/api/ai-factory.fix. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use ai-factory.fix immediately in the current project.

Related Skills

Looking for an alternative to ai-factory.fix or another community skill for your workflow? Explore these related open-source skills.

View All

widget-generator

Logo of f
f

f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.

149.6k
0
AI

flags

Logo of vercel
vercel

flags is a Next.js feature management skill that enables developers to efficiently add or modify framework feature flags, streamlining React application development.

138.4k
0
Browser

zustand

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

72.8k
0
AI

data-fetching

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

72.8k
0
AI