Shannon is a framework for building and running production AI agents that coordinate multiple agents through different execution strategies. It is used by teams that need agent collaboration, token budgets, human approvals, sandboxed code execution, and visibility into agent runs. The catalogue instruction supports working with Shannon.
Borrowing it
Nothing to install: this file belongs to Kocoro-lab/Shannon. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Kocoro-lab/Shannon/main/CLAUDE.mdgit clone --depth 1 https://github.com/Kocoro-lab/ShannonWrote 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.
[](https://agentmods.dev/instructions/kocoro-lab/shannon/claude-md)<a href="https://agentmods.dev/instructions/kocoro-lab/shannon/claude-md"><img src="https://agentmods.dev/badge/instructions/kocoro-lab/shannon/claude-md.svg" alt="Measured on agentmods" height="20"></a>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 5.1 | $0.01717 | $0.01717 |
| Opus 5 | $0.00859 | $0.00859 |
| Sonnet 5 | $0.00343 | $0.00343 |
| Haiku 4.5 | $0.00172 | $0.00172 |
Grade A, and why
Shannon CLAUDE.md scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sS -X POST http://localhost:8080/api/v1/tasks \ How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
Shannon is an open-source, enterprise-grade multi-agent AI platform that combines Rust (agent core), Go (orchestration with Temporal), and Python (LLM services) to create a token-efficient, distributed AI system.
Essential Commands
# Setup & Run
make setup && vim .env && make dev # Setup, configure API keys, start all
make smoke # E2E smoke tests
make proto # Regenerate proto files after .proto changes
make ci # Run CI checks
# Local Docker Builds
# Pattern: docker build --no-cache -f <Dockerfile> -t <image> .
# Then: docker compose -f deploy/compose/docker-compose.yml up -d <service> --no-build
# Dockerfiles: go/orchestrator/Dockerfile, go/orchestrator/cmd/gateway/Dockerfile,
# python/llm-service/Dockerfile, rust/agent-core/Dockerfile
# Unit Tests
cd rust/agent-core && cargo test
cd go/orchestrator && go test -race ./...
cd python/llm-service && python3 -m pytest
Project Structure
rust/agent-core/: Enforcement gateway, WASI sandbox, gRPC servergo/orchestrator/: Temporal workflows, budget manager, complexity analyzerpython/llm-service/: LLM providers, MCP tools, agent loopprotos/: Shared protocol buffer definitionsdeploy/compose/: Docker Compose configurationconfig/: Hot-reload configuration filesdocs/: Architecture and API documentationscripts/: Automation and helper scripts
API Endpoints (4 Entry Points)
| Endpoint | Orchestrator? | Format | Use Case |
|---|---|---|---|
POST /v1/chat/completions |
Yes | OpenAI-compatible | Apps using OpenAI SDK -- auto tool selection, deep research, swarm, strategies |
POST /v1/completions |
No (Proxy) | OpenAI-compatible | Thin LLM proxy -- single call, no orchestration, caller-supplied tools only |
POST /api/v1/tasks |
Yes | Shannon native (sync) | Full orchestrator pipeline, sync response |
POST /api/v1/tasks/stream |
Yes | Shannon native (SSE) | Full orchestrator pipeline, streaming SSE events |
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.
- 8d ago First seen · 166 lines · 1,717 tokens per session scan A 36f0b66a8d18
Shannon CLAUDE.md is an instructions file published in the GitHub repository Kocoro-lab/Shannon (2,230 stars, last pushed 2d ago), licensed MIT. It adds 1,717 tokens to every session, about $0.0086 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
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