bug-hunt-swarm
Has your agent investigate software failures using multiple agents at once. It seeks the root cause of problems to fix them.
What it does
This tool helps find the root cause of problems in computer programs. It uses multiple 'investigators' to analyze the issue from different angles simultaneously and then suggests the most likely causes.
When to use it
- ✓When a program shows an unexpected error.
- ✓To find out why a feature stopped working after an update.
- ✓When a program fails intermittently (flaky behavior).
Example of what to ask your AI
“Investigate the error that is causing the program to crash when I try to save a file.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
You provide information about the problem, like what happened and what should have happened. The tool then sends multiple 'investigators' to analyze the code and logs, without making changes, to find the cause.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill bug-hunt-swarmOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/bug-hunt-swarm/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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:
- Symptom
- Expected behavior
- Actual behavior
- Reproduction steps, if known
- Scope of impact
- Relevant evidence, such as logs, stack traces, failing tests, screenshots, recent diffs, or environment details
Prefer this source order:
- Direct user description
- Explicit files, stack traces, logs, tests, or screenshots provided by the user
- Current git changes or recent repo history when the bug appears regression-like
- 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:
- What appears broken
- What is not yet proven
- What part of the system is most likely involved
- What evidence already exists
- 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:
- The narrowest reliable trigger
- Conditions that make the bug appear or disappear
- Expected versus actual behavior at the failure boundary
- 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:
- State transitions, lifecycle edges, or ordering problems
- Mismatched assumptions between caller and callee
- Data-flow or control-flow breaks
- 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:
- Recent diffs that correlate with the symptom
- Config, flag, dependency, schema, or migration drift
- Partial updates where several entry points should have changed together
- Behavior changes that fit the timing of the bug report
…
FAQ
Will the tool fix the problem?
No, this tool focuses on finding the cause. It does not make changes to the code or implement fixes.
What are 'investigators'?
They are like mini AI programs that analyze the problem in specific ways, such as checking the change history or the code path where the error might have occurred.
Do I need to know how to code to use it?
No, you just need to describe the problem you are experiencing with the program.
Details
Original description by the author
Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2462
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
00-andruia-consultant
Helps your agent start or improve AI projects. It analyzes code, suggests tools, and creates action plans in Spanish.
sickn33/agentic-awesome-skills
007
Lets your agent perform security audits and threat modeling. It's useful for protecting projects against attacks and vulnerabilities.
sickn33/agentic-awesome-skills
10-andruia-skill-smith
Helps your agent create or improve AI tools. It defines how tools work, writes documentation, and validates the structure.
sickn33/agentic-awesome-skills
20-andruia-niche-intelligence
Lets your agent create a summary about a specific industry. It's useful for understanding the context, rules, usage patterns, and risks of a project, using reliable sources.
sickn33/agentic-awesome-skills
Similar skills
agent-architecture-audit
Makes your agent check if everything is working correctly. It finds and fixes problems in AI systems, ensuring they perform as expected.
affaan-m/ECC
agent-eval
Helps your agent compare different AI coding tools. It measures which one is faster, cheaper, and more reliable for specific tasks.
affaan-m/ECC
agent-harness-construction
Makes your agent better understand how to perform tasks. It optimizes the tools and instructions the agent uses to increase its chances of success.
affaan-m/ECC