bug-hunt-swarm
$
npx mdskill add TerminalSkills/skills/bug-hunt-swarmInvestigates bugs in parallel with multi-agent swarm analysis
- Analyzes root causes of bugs, regressions, and failures
- Uses git history, logs, tests, and code diffs as evidence
- Ranks likely causes by probability and diagnostic value
- Returns prioritized findings in JSON with actionable next steps
SKILL.md
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---
name: bug-hunt-swarm
description: >-
Parallel read-only multi-agent root-cause investigation for bugs and regressions. Use when: investigating bugs, finding root causes, tracing regressions, or diagnosing failures with multi-agent swarm.
license: MIT
compatibility: "Any codebase with git history"
metadata:
author: terminal-skills
version: "1.0.0"
category: development
tags: [debugging, swarm, multi-agent, bugs, testing]
use-cases:
- "Investigate a bug with parallel agent analysis"
- "Find root cause of regressions or crashes"
agents: [claude-code, openai-codex, gemini-cli, cursor]
---
# 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.
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