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Bug Hunt Swarm

Free

Conduct parallel investigations for software bugs.

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Free · Opens the source repo

What Bug Hunt Swarm does

Bug Hunt Swarm is a specialized skill designed for multi-agent investigations into software bugs, regressions, crashes, and other unexplained failures. This skill employs a structured approach to diagnose issues by utilizing four read-only sub-agents that work in parallel. Each sub-agent is tasked with a specific investigative role, ensuring a thorough examination of the problem without making any changes to the codebase. This allows for a focused and efficient diagnosis process, which is critical in complex software environments.

The investigation begins by building a comprehensive bug packet that includes symptoms, expected and actual behaviors, reproduction steps, and relevant evidence such as logs and stack traces. This packet serves as the foundation for the investigation, guiding the sub-agents as they explore the problem. The main agent synthesizes the findings from the sub-agents, ranking the hypotheses based on evidence and confidence levels, ultimately leading to a clear diagnosis path.

This skill is particularly useful for developers and teams facing ambiguous or complex issues that require a systematic approach to root-cause analysis. By leveraging the power of parallel investigations, Bug Hunt Swarm helps teams identify and understand the underlying causes of bugs more quickly and accurately. It is ideal for situations where multiple perspectives on a problem can yield better insights, especially in large codebases or when dealing with flaky behavior.

However, it is important to note that Bug Hunt Swarm is not a tool for implementing fixes or making changes to the code. Its focus is strictly on diagnosis and evidence gathering, making it unsuitable for direct debugging or code modification tasks. Teams looking for a tool that combines diagnosis with automated fixing may need to consider additional solutions alongside this skill.

When to use it

Use this skill when you encounter a bug that requires detailed investigation and multiple perspectives to identify its root cause.

When not to use it

Avoid this skill for straightforward bugs that can be resolved quickly, or when you need to implement fixes rather than just diagnose issues.

What you can build with it

Investigating a Flaky Test

When a test intermittently fails without clear reasons, use Bug Hunt Swarm to analyze the test's behavior and gather evidence from multiple angles.

Understanding a Recent Regression

After a recent code change, a feature breaks. Use this skill to trace back through recent changes and identify the root cause of the regression.

Diagnosing a Crash in Production

When an application crashes in production, deploy Bug Hunt Swarm to gather logs and evidence, helping to pinpoint the failure's source efficiently.

How to install Bug Hunt Swarm

View source

1. Install with the skills CLI

npx skills add sickn33/agentic-awesome-skills/bug-hunt-swarm --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by sickn33

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

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.

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.

Frequently asked questions about Bug Hunt Swarm

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