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audit-swarm

Fan out 4–5 role-specialised audit agents (regression / security / performance / coverage / style) over a code change, then merge findings with a…

Skill by Lesile-Yin

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/lesile-yin-audit-swarm

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Fan out 4–5 role-specialised audit agents (regression / security / performance / coverage / style) over a code change, then merge findings with a noise-filtered severity model. Use as a wider-net alternative to a single review pass, or as a complement to prism-review when a change needs breadth.

Use cases: testing, security

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/audit-swarm

What's inside

Skill name
audit-swarm
Description
Fan out 4–5 role-specialised audit agents (regression / security / performance / coverage / style) over a code change, then merge findings with a noise-filtered severity model. Use as a wider-net alternative to a single review pass, or as a complement to prism-review when a change needs breadth.
License
MIT
Instructions
46 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • lanes.md · references/

SKILL.md (first lines)

# Audit Swarm

One reviewer sees one lens. audit-swarm sees many at once by spawning a small
swarm, each constrained to its lane, then merging without duplicating noise.

## When to use

- After a non-trivial change, before merge.
- When prism-review's four lenses need more coverage (e.g. a dedicated coverage
  or style lane).
- When a single model's review keeps missing a category of defect.

## Workflow

### Step 1 — Scope the swarm
Given the diff + intent brief, pick 4–5 lanes. Default set: regression,
security, performance, coverage, style. Drop a lane if the change can't touch
it; add one if the domain demands (e.g. i18n, accessibility).

### Step 2 — Dispatch with contracts
Each agent gets: the diff, the brief, and *exactly one* lane checklist (see
`references/lanes.md`, adapted from prism-review's lens model). Agents report
findings **only** in their lane; cross-lane observations go to merge as "notes".

### Step 3 — Merge with a noise filter
Dedupe by `file:line`. Collapse "looks fine" noise. Rank survivors by severity
(critical / major / minor / nit). A finding is *critical* only if it can cause
data loss, a security break, or a regression in shipped behavior.

### Step 4 — Emit the merged report
Table form: `file:line · lane · severity · one-line finding · suggested fix`.
Flag escalations (critical/major) for human review; nits may be auto-batched.

## Anti-patterns (never do these)

- Overlapping lanes — two agents auditing the same thing just doubles noise.
- Agents without a contract — they drift into generic "looks okay" spam.
- Running a swarm on a one-line typo diff — cost exceeds value.
- Severity inflation — not everything is "critical".

## Output contract

Return a merged findings table + an escalation list + a one-line coverage note
("N lanes, M findings, K critical"). No swarm runs without a diff and a brief.

From Lesile-Yin/agent-skills, MIT.

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Last commit (this folder)
Aug 26, 2026 (2 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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audit-swarm skill. Agent Mart. https://agentmart.dev/skills/lesile-yin-audit-swarm (last updated 2026-10-24)