Borrowing it
Nothing to install: this file belongs to mnemox-ai/AgentRelay. 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/mnemox-ai/AgentRelay/main/CLAUDE.mdgit clone --depth 1 https://github.com/mnemox-ai/AgentRelayWrote 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/mnemox-ai/agentrelay/claude-md)<a href="https://agentmods.dev/instructions/mnemox-ai/agentrelay/claude-md"><img src="https://agentmods.dev/badge/instructions/mnemox-ai/agentrelay/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/mnemox-ai/agentrelay/claude-md"><img src="https://agentmods.dev/badge/instructions/mnemox-ai/agentrelay/claude-md.svg" alt="Reviewed on agentmods" width="80" 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 | $0.01618 | $0.01618 |
| Opus 5.5 | $0.00647 | $0.00647 |
| Sonnet 5.5 | $0.00324 | $0.00324 |
| Haiku 4.5 | $0.00162 | $0.00162 |
Grade A, and why
AgentRelay 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentRelay — Claude Code 指令
專案概述
AgentRelay = Verifiable Microtask Protocol for AI Agents。多 agent 協作平台,讓 AI agent 發布、認領、執行微任務,並透過驗證引擎確保產出品質。
- GitHub: mnemox-ai/AgentRelay
- 版本: v0.6.0
- License: Apache-2.0
- Default branch:
main
架構(分層)
src/agentrelay/
├── api/ # FastAPI routes (app.py, routes/)
│ └── routes/ # health, agents, tasks, validation
├── schemas/ # Pydantic request/response models
├── services/ # Business logic (task_service, validation_service, quota_service, reputation_service)
├── repositories/ # DB access layer (task_repo, agent_repo, submission_repo, reputation_repo)
├── models/ # SQLAlchemy ORM models (task, agent, submission, ledger, reputation, validation_run)
├── domain/ # Core business objects (task_spec, validation_result, quota_profile, reputation_score)
├── validation/ # Validation engine (schema_validator, rule_validator)
├── security/ # Input/output sanitizers, token limiter
├── config.py # pydantic-settings (.env)
└── db.py # Async SQLAlchemy engine + session
分層規則:API → Service → Repository → Models,禁止跨層呼叫。
開發規範
- Python 3.11+
- 測試:
python -m pytest tests/ -v(每個任務完成後必跑) - Linting:
ruff check src/ tests/ - Commit message 格式:
type: description(feat/fix/docs/chore/test) - DB:PostgreSQL + asyncpg,Redis for quota/cache
- 所有 timestamps 用 UTC
ToS 紅線
- 不碰 auth / API key 管理 — agent 身分驗證由外部 IdP 負責
- 不代理 API call — AgentRelay 只轉發任務規格和結果,絕不代替 agent 呼叫第三方 API
- 不存放模型權重或推論結果 — 只存結構化驗證結果
重要檔案位置
- FastAPI app:
src/agentrelay/api/app.py - Routes:
src/agentrelay/api/routes/(tasks, agents, validation, health) - Domain objects:
src/agentrelay/domain/(TaskSpec, TaskType, TaskStatus, TaskDifficulty) - Validation engine:
src/agentrelay/validation/ - Security:
src/agentrelay/security/(auth.py, rate_limiter.py, task_sanitizer, output_sanitizer, token_limiter) - DB models:
src/agentrelay/models/ - Alembic migrations:
alembic/ - Tests:
tests/ - Integration tests:
tests/test_integration.py(full lifecycle)、tests/test_security_integration.py(attack simulation) - Seed script:
scripts/seed_tasks.py - CI:
.github/workflows/ci.yml - Phase 1 services:
src/agentrelay/services/(task_service, validation_service, ledger_service, reputation_service, expiration_service, quota_service) - Phase 1 domain:
src/agentrelay/domain/task_lifecycle.py(TaskStateMachine) - API deps:
src/agentrelay/api/deps.py(auth, rate limiting dependency injection) - Phase 4 capability:
src/agentrelay/domain/capability.py(AgentCapability matching) - Phase 4 notification:
src/agentrelay/services/notification_service.py(WebSocket broadcast) - Phase 4 queue:
src/agentrelay/services/queue_service.py(Redis priority queue) - Phase 4 WebSocket:
src/agentrelay/api/routes/ws.py(/ws endpoint) - Phase 5 MCP server:
src/agentrelay/mcp_server.py(7 MCP tools + 1 resource) - Phase 5 MCP E2E tests:
tests/test_mcp_e2e.py(full MCP lifecycle tests) - Phase 5 MCP unit tests:
tests/test_mcp_server.py(per-tool unit tests) - Phase 5 SKILL.md:
.skills/agentrelay/SKILL.md(universal agent onboarding)
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.
- 2mo ago First seen · 102 lines · 1,618 tokens per session scan A 453067aae0f4
AgentRelay CLAUDE.md is an instructions file published in the GitHub repository mnemox-ai/AgentRelay (67 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 1,618 tokens to every session, about $0.0065 per session on Opus 5.5. 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.
AgentRelay CLAUDE.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| AgentRelay CLAUDE.mdmnemox-ai/AgentRelay | Instructions file | 67 | 7mo ago | Apache-2.0 | A | 1,618 | Claude Code |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | yesterday | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |