A workflow for diagnosing and fixing a software bug from a description, error trace, or GitHub issue. A GitHub issue is a tracked report or task in a repository.

Use it to investigate broken behavior, inspect related code and tests, reproduce the problem, patch the cause, and leave the changes ready for review.

It provides a path from reproducing the failure to finding its cause and preparing a fix that can be reviewed.

fix — Skill for Claude Code and Codex by 0xrafasec: A workflow for diagnosing and fixing a software bug…

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/0xrafasec/ai-workflow/fix

Any agent

npx skills add 0xrafasec/ai-workflow --skill fix

Clone the repo

git clone --depth 1 https://github.com/0xrafasec/ai-workflow

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/0xrafasec/ai-workflow/fix.svg)](https://agentmods.dev/skills/0xrafasec/ai-workflow/fix)

Your own site

<a href="https://agentmods.dev/skills/0xrafasec/ai-workflow/fix"><img src="https://agentmods.dev/badge/skills/0xrafasec/ai-workflow/fix.svg" alt="Measured on agentmods" height="20"></a>

Per session 89 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,240 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00089	$0.01240
Opus 5	$0.00044	$0.00620
Sonnet 5	$0.00018	$0.00248
Haiku 4.5	$0.00009	$0.00124

Measured 4d ago against content hash 682de44d349a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fix scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/fix/SKILL.md · 75 lines

How it starts

The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Fix the bug described in $ARGUMENTS.

Parse Arguments

The argument can be:

Bug description: /fix users can't login when password contains special chars
Issue link: /fix https://github.com/org/repo/issues/42

Branch

Before writing code, ensure you are on a short-lived branch named fix/<slug>. If currently on main/master, create the branch now. See the global Trunk-Based Workflow in root CLAUDE.md for branch/worktree conventions.

Steps

Understand the bug
- If given an issue link: fetch it with gh issue view and read the full description, comments, and labels.
- If given a description: use it directly.
- Check for existing docs (CLAUDE.md, README.md, docs/) to understand the project context.
Reproduce and locate
- Search the codebase for the relevant code paths (use Grep, Glob, read key files).
- Identify the component, module, or layer where the bug lives.
- If there are existing tests, run them to see the current failure state.
- If reproduction requires specific steps, tell the user what you're doing.
Diagnose root cause
- Read the relevant code carefully. Trace the data flow from input to failure point.
- Check git log on the affected files for recent changes that may have introduced the bug.
- Identify the root cause — not just the symptom. Explain it to the user in 1-2 sentences before proceeding.
Discover test strategy — Before writing the fix, understand the project's test approach:

a. Check for TDD: Read docs/TECHNICAL_DESIGN_DOCUMENT.md — if it has a Testing Strategy section, follow it. b. If no TDD: Infer from the codebase — look for existing test directories, frameworks, patterns, and naming conventions (same discovery as /feature step 3b). c. Determine which test layers the bug touches — a bug in a pure function needs a unit test; a bug in an API endpoint needs an integration test; a bug in a user flow may need an e2e test.

Read the full file on GitHub · 75 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

4d ago First seen · 75 lines · 89 tokens per session scan A 682de44d349a

Subscribe to this mod's changes

fix is a skill published in the GitHub repository 0xrafasec/ai-workflow (9 stars, last pushed 2d ago), licensed MIT. It adds 89 tokens to every session and 1,240 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Code review Debugging

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens