A workflow for coordinating several specialised AI agents to take a GitHub issue from investigation through implementation and a merged pull request.

Use it to start work on GitHub issues, track ready tasks and project status, and run the listed agents through the issue-to-pull-request process.

It breaks a large issue into reviewed stages so research, planning, coding, security, design, and quality checks are coordinated instead of handled as one unchecked task.

start — Skill for Claude Code and Codex by dsifry: A workflow for coordinating several specialised AI agents to…

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/dsifry/metaswarm/start

Any agent

npx skills add dsifry/metaswarm --skill start

Clone the repo

git clone --depth 1 https://github.com/dsifry/metaswarm

Made for: Claude Code, Codex.

Or install metaswarm, the plugin that ships this one along with the rest of its 14 skills, 16 commands, 19 agents, 2 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/dsifry/metaswarm/start.svg)](https://agentmods.dev/skills/dsifry/metaswarm/start)

Your own site

<a href="https://agentmods.dev/skills/dsifry/metaswarm/start"><img src="https://agentmods.dev/badge/skills/dsifry/metaswarm/start.svg" alt="Measured on agentmods" height="20"></a>

Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 7,169 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00027	$0.07169
Opus 5	$0.00014	$0.03585
Sonnet 5	$0.00005	$0.01434
Haiku 4.5	$0.00003	$0.00717

Measured 3d ago against content hash 2a31dd3e1702, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

start scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/start/SKILL.md · 872 lines

How it starts

The opening of the file, as written. The whole thing — 872 lines — stays where its author put it; the contents beside it link to each section on GitHub.

BEADS Multi-Agent Orchestration Skill

This skill coordinates a swarm of specialized AI agents to autonomously handle GitHub Issues from creation to merged PR.

Quick Start

Start Work on a GitHub Issue

# User triggers via any of:
@beads start #123
bd start 123
/beads-start 123

Check BEADS Status

bd ready          # Show tasks ready to work
bd list           # Show all tasks
bd stats          # Show project statistics
bd doctor         # Check system health

Agent Roster

Agent	Role	Spawned When
Issue Orchestrator	Main coordinator per Issue	Issue receives `agent-ready` label
Researcher Agent	Codebase exploration	Orchestrator creates research task
Architect Agent	Implementation planning	Research complete
Product Manager Agent	Use case & user benefit review	Design review gate (parallel)
Designer Agent	UX/API design review	Design review gate (parallel)
Security Design Agent	Security threat modeling	Design review gate (parallel)
CTO Agent	TDD readiness & plan review	Design review gate (parallel)
Coder Agent	TDD implementation	Design review gate approved
Code Review Agent	Internal code review	Implementation complete
Security Auditor	Security review (code)	Implementation complete
Release Engineer Agent	Safe delivery from merge through production	QA approves PR, PR reaches merge readiness
PR Shepherd	PR lifecycle management	PR created

See ./agents/ directory for detailed agent definitions.

Read the full file on GitHub · 872 lines

Files

What ships with it

29 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

3d ago First seen · 872 lines · 27 tokens per session scan A 2a31dd3e1702

Subscribe to this mod's changes

start is a skill published in the GitHub repository dsifry/metaswarm (407 stars, last pushed 2mo ago), licensed MIT. It adds 27 tokens to every session and 7,169 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Building agents Planning

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brainstorming

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auto-perf-optimize

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chat-perf

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chat-pet-sprite-creation

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Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

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