Bug Hunt Swarm logo

Bug Hunt Swarm

CommunityPopular
Dimillian
bug-hunt-swarm

Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures. Use when the user asks to investigate a bug, find the root cause, trace a regression, understand why something broke, or wants a ranked diagnosis with the fastest proof path without making code edits.

Overview

PublisherDimillian
RepositorySkills
Skill namebug-hunt-swarm
Stars
4K
Forks
206
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Dimillian on GitHub. Read the source before you install it.

Installation

Install the Bug Hunt Swarm 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.
  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/Dimillian/Skills.git /tmp/Skills
mkdir -p .claude/skills
cp -r /tmp/Skills/bug-hunt-swarm .claude/skills/bug-hunt-swarm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bug Hunt Swarm 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 Swarm 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 Swarm 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 Swarm

Investigate a bug with four read-only sub-agents in parallel, then have the main agent rank the likely causes and recommend the fastest path to prove or fix the issue. This skill is diagnosis-first: do not edit files or implement fixes as part of this workflow.

Step 1: Build the Bug Packet

Start by collecting the smallest useful investigation packet:

  1. Symptom
  2. Expected behavior
  3. Actual behavior
  4. Reproduction steps, if known
  5. Scope of impact
  6. Relevant evidence, such as logs, stack traces, failing tests, screenshots, recent diffs, or environment details

Prefer this source order:

  1. Direct user description
  2. Explicit files, stack traces, logs, tests, or screenshots provided by the user
  3. Current git changes or recent repo history when the bug appears regression-like
  4. The smallest relevant code path or subsystem surrounding the failure

If the bug report is underspecified, infer a minimal problem statement and say what is still unknown.

Before launching sub-agents, read the closest project instructions and relevant docs for the touched area, such as:

  • AGENTS.md
  • repo workflow docs
  • architecture, state, routing, schema, or runtime docs for the affected subsystem

Step 2: Bound the Investigation

Write a short investigation brief for the swarm:

  1. What appears broken
  2. What is not yet proven
  3. What part of the system is most likely involved
  4. What evidence already exists
  5. What kind of proof would count as confirmation

Use read-only evidence gathering where useful:

  • rg, git diff, git log, git show
  • reading logs, crash traces, and config
  • existing test runs or the smallest safe reproduction command

Do not edit files, inject new instrumentation, or implement fixes as part of this skill.

Step 3: Launch Four Read-Only Investigators in Parallel

Launch four sub-agents when the problem is large or ambiguous enough that parallel investigation helps. For a tiny and obvious issue, it is acceptable to investigate locally instead.

For every sub-agent:

  • give the same bug packet and investigation brief
  • state that the sub-agent is read-only
  • do not let the sub-agent edit files, run apply_patch, stage changes, commit, or perform any other state-mutating action
  • ask for concise investigation output only
  • ask for: hypothesis, supporting evidence, missing evidence, smallest proof step, and confidence
  • tell the sub-agent to avoid generic code quality feedback, nits, or speculative guesses without evidence
  • tell the sub-agent to send findings back to the main agent only

Use these four investigation roles.

Sub-Agent 1: Reproduction and Scope Investigation

Clarify the exact failure shape and its boundaries.

Check for:

  1. The narrowest reliable trigger
  2. Conditions that make the bug appear or disappear
  3. Expected versus actual behavior at the failure boundary
  4. Whether the impact is local, cross-cutting, deterministic, or flaky

This sub-agent is read-only. It must not edit files, apply patches, or make any other workspace changes.

Recommended sub-agent role: reviewer

Sub-Agent 2: Code Path and Failure Seam Investigation

Trace the most likely execution path and identify the seam where behavior diverges.

Check for:

  1. State transitions, lifecycle edges, or ordering problems
  2. Mismatched assumptions between caller and callee
  3. Data-flow or control-flow breaks
  4. The smallest code region most likely responsible for the failure

This sub-agent is read-only. It must not edit files, apply patches, or make any other workspace changes.

Recommended sub-agent role: explorer for broad tracing, or reviewer when a stronger local reasoning pass is more useful

Sub-Agent 3: Recent Change and Regression Investigation

Look for likely regressors in nearby history or changed contracts.

Check for:

  1. Recent diffs that correlate with the symptom
  2. Config, flag, dependency, schema, or migration drift
  3. Partial updates where several entry points should have changed together
  4. Behavior changes that fit the timing of the bug report

This sub-agent is read-only. It must not edit files, apply patches, or make any other workspace changes.

Recommended sub-agent role: reviewer

Sub-Agent 4: Proof Plan and Observability Investigation

Determine the fastest way to confirm or reject the leading hypotheses.

Check for:

  1. The smallest existing test or reproduction that should fail
  2. The most useful current logs, traces, metrics, or assertions
  3. A minimal non-mutating command that could raise confidence quickly
  4. What evidence is missing and how to collect it without broad churn

This sub-agent is read-only. It must not edit files, apply patches, or make any other workspace changes.

Recommended sub-agent role: reviewer

Report only hypotheses that materially improve the odds of finding the real cause. It is better to return two evidence-backed theories than six vague guesses.

Step 4: Synthesize Ranked Hypotheses

The main agent owns synthesis. Treat sub-agent output as raw investigation input, not final output.

Merge and rank the hypotheses:

  • combine duplicates
  • discard weak speculation
  • prefer evidence over elegance
  • separate likely root causes from mere contributing factors
  • keep alternate theories only when they remain plausible

Normalize the surviving hypotheses into this shape:

  1. Hypothesis
  2. Supporting evidence
  3. Missing or conflicting evidence
  4. Smallest proof step
  5. Confidence: high, medium, or low

If the evidence is too weak for a real ranking, say so directly and present the leading open questions instead.

Step 5: Output a Clear Diagnosis Path

Present the result in this order:

  1. Most likely root cause
  2. Plausible alternate causes, if any
  3. Fastest proof step
  4. Recommended fix path
  5. Open questions or blockers

When the fix is not yet clear, recommend the next proving step instead of pretending the diagnosis is complete.

When helpful, group actions into:

  • prove now
  • fix next
  • follow up later

Do not implement fixes as part of this skill. The output is a read-only diagnosis with a prioritized path forward.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Bug Hunt Swarm AI skill do?

Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures. Use when the user asks to investigate a bug, find the root cause, trace a regression, understand why something broke, or wants a ranked diagnosis with the fastest proof path without making code edits.

Why use Bug Hunt Swarm on TypingMind?

Because you install it once and use it with any model. Bug Hunt Swarm 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 Swarm in TypingMind?

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

Which AI models can use Bug Hunt Swarm?

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 Swarm?

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

Is the Bug Hunt Swarm AI skill free?

Yes. It is published on GitHub by Dimillian 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇