agentmods.dev

multiagent CLAUDE.md

A set of instructions for a tool that uses six AI agents to take software tasks from a backlog through implementation, review, testing, and a commit.

In plain words
What is it for?
It helps automate prioritized coding tasks, coordinate specialist agents, enforce protected paths, resume interrupted work, and create feature branches.
Why use it?
It gives the agents a shared process for dividing work, checking quality, and saving progress for human review.

Instructions file

5 5mo ago A scan 1,492 tokens original MIT
View the source file mopkobka57/multiagent
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 instructions/mopkobka57/multiagent/claude-md
Clone the repo
git clone --depth 1 https://github.com/mopkobka57/multiagent
Per session 1,492 This file is loaded in full into every session.
When invoked 1,492 The same file — it is already loaded in full.
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.

ModelPer sessionOnce invoked
Fable 5 $0.01492 $0.01492
Opus 5 $0.00746 $0.00746
Sonnet 5 $0.00298 $0.00298
Haiku 4.5 $0.00149 $0.00149

Measured 2d ago against content hash 5158be53dad7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

multiagent CLAUDE.md 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 2d 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.

CLAUDE.md · 122 lines

How it starts

The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Multiagent — Instructions for Claude

What is this

Autonomous task execution system powered by Claude. Reads a backlog, picks tasks by priority, coordinates 6 AI agents (Orchestrator → Product → Analyst → Implementor → Reviewer → Visual Tester), runs quality gates, and commits to feature branches for human review.

Designed to be installed into any project via python -m multiagent init.

Language

Code, comments, commits — English.

Architecture

multiagent/
  __main__.py          CLI entry point (init, spec, --next, --task, --list, --batch)
  config.py            All settings (populated from multiagent.toml at import)
  project_config.py    TOML loader, auto-detection fallback
  core/                Engine
    pipeline.py        Standard task pipeline (branch → enrich → implement → review → commit)
    orchestrator.py    High-level commands (run_next, run_batch, etc.)
    agents.py          Agent definitions and Claude API calls
    prompt_builder.py  Prompt construction, spec discovery
    task_loader.py     Backlog parser (Markdown tables → Task objects)
    state.py           State persistence for resume support
    guardrails.py      Protected path enforcement
    quality_gates.py   Build/lint gates, screenshots
    git.py             Git operations (branch, commit, checkout)
    retry.py           Rate limit handling with exponential backoff
    spec_manager.py    Spec CRUD, versioning
    spec_creator.py    AI-powered spec generation from descriptions
    init.py            Project initialization logic
  analyzer/            Project detection + AI analysis (used by init)
  prompts/             Agent system prompts (Markdown templates)
  templates/           Scaffold templates for init (toml, backlog, contexts)
  server/              FastAPI web dashboard + process manager
  docs/                Full documentation (11 files)

Key concepts

  • Specs — Markdown files describing tasks. Statuses: stub → partial → full. Agents enrich stubs before implementation. See docs/backlog-format.md.
  • Foundational specs — files prefixed with _ in specs/, loaded into every agent's context (conventions, architecture).
  • Quality gates — shell commands (tsc, build) run after each implementation step. Configured in multiagent.toml.
  • Autonomy modes — supervised (confirm each step), batch (confirm per-task), autonomous (hands-off). See docs/autonomy-modes.md.
  • Web dashboard — FastAPI + Alpine.js SPA for managing tasks, specs, groups, and runs. Real-time logs via WebSocket. See docs/dashboard.md.
  • Spec groups — bundle related tasks to run sequentially on a shared branch. Created via dashboard UI.
  • Task types — feature (full pipeline), tech-debt/refactor/bugfix (skip Product agent), audit (read-only analysis).

Read the full file on GitHub · 122 lines

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.

  1. 2d ago First seen · 122 lines · 1,492 tokens per session scan A 5158be53dad7

Subscribe to this mod's changes

multiagent CLAUDE.md is an instructions file published in the GitHub repository mopkobka57/multiagent (5 stars, last pushed 5mo ago), licensed MIT. It adds 1,492 tokens to every session, about $0.0075 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 2026-08-31.

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