Bug Hunt logo

Bug Hunt

Organization
escoffier-labs
bug-hunt

Use when asked to find bugs, hunt for correctness issues, sweep a codebase for defects, or verify a repo behaves as intended. Not for style or architecture review; this is defect-finding only.

Overview

Publisherescoffier-labs
Repositorybrigade
Skill namebug-hunt
Stars
82
Forks
9
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by escoffier-labs on GitHub. Read the source before you install it.

Installation

Install the Bug Hunt AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/escoffier-labs/brigade/tree/main/registry/skills/bug-hunt
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/escoffier-labs/brigade.git /tmp/brigade
mkdir -p .claude/skills
cp -r /tmp/brigade/registry/skills/bug-hunt .claude/skills/bug-hunt
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bug Hunt in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bug Hunt on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bug Hunt is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

bug-hunt

A correctness sweep that only reports bugs it failed to refute. Finders generate candidates; verifiers try to kill them; survivors make the report. The single biggest failure mode of agent bug-hunting is plausible-but-wrong findings, so verification is not optional.

Read-only. Finding bugs and fixing them are separate engagements.

Lenses

Sweep with each lens. With parallel subagents available, one finder per lens; otherwise sequential passes.

LensHunting for
LogicInverted conditions, off-by-one, wrong operator, unreachable branches, broken invariants
Error handlingSwallowed exceptions, missing error paths, errors that corrupt state before propagating, misleading messages
Edge casesEmpty/nil/zero inputs, unicode, huge inputs, boundary values, first/last iteration
ConcurrencyRaces, missing locks, shared mutable state, TOCTOU, async ordering assumptions
API misuseContract violations against libraries and the project's own interfaces, ignored return values, resource leaks, lifecycle errors

Focus finders on code that is reachable and load-bearing: entry points, hot paths, recently changed files (git log --since is a good prior). A bug in dead code is info, not a finding.

Two deep lenses live as their own skills: latent-premises for unguarded assumptions that hold today with no trigger yet, and retry-safety for side effects that double-apply on a re-run. Reach for them when the sweep keeps surfacing "could break later" candidates that fail the trigger rule.

Verification (mandatory)

Every candidate gets an adversarial pass before it may appear in the report. The verifier's job is to REFUTE the finding, default skeptical:

  1. Read the actual code path end to end, including callers and guards the finder may have missed.
  2. Trace a concrete input that triggers the bug. No trigger, no bug.
  3. Check whether a test, type system, or runtime check already prevents it.
  4. Verdict: confirmed (with the triggering scenario), refuted (drop silently), or unverifiable (report downgraded one severity, marked (unverified)).

When tests can be run safely (no external dependencies, sandboxed), a failing reproduction test is the gold standard for confirmation and should be included in the finding as a sketch, not committed.

Report contract

Same spine as line-check so findings compose. Severity: critical (data loss, corruption, security-adjacent) / high (wrong results on common inputs, crashes) / medium (wrong on edge cases) / low (latent, needs unlikely conditions) / info. Effort is the fix cost: S / M / L.

markdown
# bug-hunt report: <repo> (<date>)

## Verdict
Paragraph: overall correctness posture, the scariest confirmed bug.

## Scorecard
| Lens | Score (0-5) | Summary |

## Findings
### [SEVERITY] Short imperative title
- **Lens:** which lens found it
- **Where:** file:line
- **What:** the defect, concretely
- **Trigger:** the concrete input or sequence that hits it
- **Why it matters:** consequence
- **Fix:** specific action
- **Effort:** S / M / L

## Backlog
Numbered, leverage-sorted: `N. [SEVERITY/EFFORT] title (lens)`

## Not checked
Lenses or areas skipped and why; candidates that were refuted (count only).

Common mistakes

  • Reporting finder output without verification. Half of plausible candidates die under a skeptical read.
  • "This could be a problem if..." findings with no trigger. A bug without a triggering input is a hypothesis.
  • Treating style issues as bugs. Wrong formatting never corrupted data.
  • Stopping at the first confirmed bug in a file. Bugs cluster; finish the file.

Frequently asked questions

What does the Bug Hunt AI skill do?

Use when asked to find bugs, hunt for correctness issues, sweep a codebase for defects, or verify a repo behaves as intended. Not for style or architecture review; this is defect-finding only.

Why use Bug Hunt on TypingMind?

Because you install it once and use it with any model. Bug Hunt is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bug Hunt in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/escoffier-labs/brigade/tree/main/registry/skills/bug-hunt. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bug Hunt?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bug Hunt?

As many as you like. As long as a model supports skills, you can use Bug Hunt with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bug Hunt AI skill free?

Yes. It is published on GitHub by escoffier-labs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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