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Agent skill

Bug Hunt Swarm

by sickn33 in sickn33/agentic-awesome-skills

Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures.

MITAuto-check passedAgent Workflows

Install Bug Hunt Swarm

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill bug-hunt-swarm -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills bug-hunt-swarm --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/bug-hunt-swarm .claude/skills/bug-hunt-swarm && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bug-hunt-swarm
GitHub stars
47k
Used in
1 other repo
Token cost
~1.9k tokens
SKILL.md length
1,051 words
Files
2
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures.

  • Works in 5 steps: Build the Bug Packet → Bound the Investigation → Launch Four Read-Only Investigators in… → …
  • Tasks that involve Root cause analysis
  • SKILL.md covers When to Use, Step 1: Build the Bug Packet, Step 2: Bound the Investigation and Step 3: Launch Four Read-Only…, plus 4 more sections
  • Calls git

What it does

Bug Hunt Swarm is an agent skill from sickn33/agentic-awesome-skills. Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Agent Workflows, covering Root cause analysis and Subagents. It works with Git. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Root cause analysis
  • Tasks that involve Subagents

Example prompts

  • “/bug-hunt-swarm”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Build the Bug Packet
  2. Bound the Investigation
  3. Launch Four Read-Only Investigators in Parallel
  4. Synthesize Ranked Hypotheses
  5. Output a Clear Diagnosis Path

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bug Hunt Swarm loads about 1.9k tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 1,051 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit b84d35a, republished under its MIT licence (© sickn33). 1,051 words, ~1,855 tokens.

Download SKILL.mdSave it as .claude/skills/bug-hunt-swarm/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
bug-hunt-swarm
description
Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures.
risk
safe
source
https://github.com/Dimillian/Skills/tree/main/bug-hunt-swarm
source_repo
Dimillian/Skills
source_type
community
date_added
2026-07-01
license
MIT
license_source
https://github.com/Dimillian/Skills/blob/main/LICENSE

Bug Hunt Swarm

When to Use

Use this skill when you need 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...

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

Show full SKILL.md (442 more words)Show less
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.

Example

User request:

Investigate this failure with @bug-hunt-swarm, prove the root cause, implement the smallest safe fix, and verify it.

Limitations

  • Use this skill only when the task clearly matches its upstream source and local project context.
  • Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
  • Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/bug-hunt-swarm of sickn33/agentic-awesome-skills.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit b84d35a

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Bug Hunt Swarm next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Bug Hunt Swarm compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Bug Hunt Swarm this skillsickn33/agentic-awesome-skills47k1 repos~1.9kAutomated safety check: PassMIT
Bug Hunt SwarmDimillian/Skills4k—~1.6kAutomated safety check: PassMIT
Analyze Trajectoryyologdev/yoyo-evolve1.9k—~3.6kAutomated safety check: PassMIT
O2 Review Loopopenobserve/openobserve22k—~3.7kAutomated safety check: PassAGPL-3.0
Dynamic Workflowszai-org/ZCode7.6k—~23kAutomated safety check: PassApache-2.0
Vspawnvlinx-io/VelaTerm281—~2.5kAutomated safety check: PassMIT

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Works with

Categories

Questions about Bug Hunt Swarm

What does Bug Hunt Swarm do?

Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures. Bug Hunt Swarm is an agent skill from sickn33/agentic-awesome-skills. Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures.

When should I use Bug Hunt Swarm?

Bug Hunt Swarm fits situations like: tasks that involve Root cause analysis; tasks that involve Subagents.

How do I install Bug Hunt Swarm in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill bug-hunt-swarm -a claude-code`. Or copy the skill folder (skills/bug-hunt-swarm in sickn33/agentic-awesome-skills) into .claude/skills/bug-hunt-swarm in your project. Claude Code loads it when a task matches its description.

How do I install Bug Hunt Swarm in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill bug-hunt-swarm -a codex`. Or copy the skill folder (skills/bug-hunt-swarm in sickn33/agentic-awesome-skills) into .agents/skills/bug-hunt-swarm in your project. Codex loads it when a task matches its description.

Can I use Bug Hunt Swarm in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill bug-hunt-swarm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bug-hunt-swarm, .gemini/skills/bug-hunt-swarm, .github/skills/bug-hunt-swarm and .opencode/skills/bug-hunt-swarm in your project.

What does Bug Hunt Swarm need to run?

Going by SKILL.md and its folder, Bug Hunt Swarm needs the command-line tools its instructions call (git).

Does Bug Hunt Swarm access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bug Hunt Swarm safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bug Hunt Swarm use?

Bug Hunt Swarm is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bug Hunt Swarm use?

About 1.9k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bug Hunt Swarm?

Skills that share tags, products or a category with Bug Hunt Swarm: Bug Hunt Swarm (Dimillian/Skills, 4k stars), Analyze Trajectory (yologdev/yoyo-evolve, 1.9k stars), O2 Review Loop (openobserve/openobserve, 22k stars) and Dynamic Workflows (zai-org/ZCode, 7.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bug Hunt Swarm?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.