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.
npx agentmods add agents/ccdawn/vibelution/project-operation-cataloggit clone --depth 1 https://github.com/CCDawn/VibelutionWrote 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/agents/ccdawn/vibelution/project-operation-catalog)<a href="https://agentmods.dev/agents/ccdawn/vibelution/project-operation-catalog"><img src="https://agentmods.dev/badge/agents/ccdawn/vibelution/project-operation-catalog.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 | $0.00000 | $0.04406 |
| Opus 5 | $0.00000 | $0.02203 |
| Sonnet 5 | $0.00000 | $0.00881 |
| Haiku 4.5 | $0.00000 | $0.00441 |
Grade A, and why
project-operation-catalog 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 6d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
项目操作目录(Project Operation Catalog · Phase 1 baseline)
读者:仓库内 / 外部 coding Agent。
定位:现行 Agent 指南(Phase 1 baseline),不是计划,也不是执行面真源。
它登记项目级可操作对象(Agent / Session / 后台 API)、治理访问类、检索卡片与安全生命周期语义。
真源是代码: 路由注册表 + 路由模块、canonical 工具注册表。本指南与其 --inventory 输出都是派生投影。
全局红线见根 AGENTS.md,开发标准见 docs/standards/,工具注册见 tools/README.md。
1. SSOT 与派生投影(Layer Boundaries)
只有两类真源(SSOT);其余都是派生投影。
| 层 | 角色 | 权威 |
|---|---|---|
| 路由注册表 + 路由模块 | SSOT:core/web/router_registry.py(include 顺序与 /api 前缀)+ core/web/app.py + core/web/routes/**/*.py |
是后端 API 面的唯一事实来源 |
| Canonical 工具注册表 | SSOT:tools/Key_Tools.py(create_key_tools())+ core/web/services/tool_catalog.py(TOOL_CATALOG) |
是 Agent 可见工具 canonical 名的唯一事实来源 |
| 业务与状态 | core/web/services/<domain>/ |
一切状态与写入的权威;projection 不得成为第二写入者 |
scripts/api_contract_audit.py --inventory |
派生投影:静态路由定义盘点(扫描 core/web/routes/**/*.py 与 app.py 中的装饰器) |
只读;不改变运行时行为;静态定义 ≠ 运行时可调用证明 |
| 本指南的表格 | 派生投影:索引锚点 | 精确端点以 --inventory 为准;禁止手工维护第二份全量端点表 |
规则:
- 新增/变更路由或工具后,重跑
--inventory;先改 SSOT(代码/注册表),再刷新投影。 --inventory与指南永远不参与 joint SSOT;出现不一致时以代码/注册表为准并修正投影。--inventory是静态路由定义盘点:只扫描路由源文件中的装饰器,可能包含尚未注册(未进router_registry.py)的模块;运行时可执行性必须与 router 注册 / runtime OpenAPI 对账,不能把静态定义直接当作可调用证明。- 本指南第 5 节矩阵是锚点索引,不是注册表。
2. 治理访问类(Governance Access Classes)
治理访问类是声明式契约,与当前审计脚本的传输/漂移分类不同层、不混用:
scripts/api_contract_audit.py的_classify_backend_without_frontend只是传输/漂移分类(direct_fetch_*、binary_or_url_resource、agent_inbox_api等),不机器执行治理访问类,也不代表授权生效。- 治理访问类由本指南与后续治理实现共同持有;当前只登记为声明,不得声称已由机器强制。
| 治理类 | 含义 | 典型对象 |
|---|---|---|
AUTO_READ |
只读、无副作用;非无条件读取,仍受 ToolPolicy、ACL 与 owner/team scope 约束 | 列表、详情、状态端点 |
GOVERNED_WRITE |
有状态写入,须经 governed tool 或已授权调用方 | session 创建、消息发送、子会话创建 |
APPROVAL_REQUIRED |
高风险写入,需用户/操作者显式审批 | agent reset、批量操作、删除类操作 |
OPERATOR_ONLY |
仅操作者可执行;普通 Agent 不开放 | agent purge(不可逆) |
INTERNAL_ONLY |
仅供系统服务内部使用 | 内部辅助 / 无外部面的端点 |
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.
- 6d ago First seen · 186 lines · 0 tokens per session scan A df32333ccaab
project-operation-catalog is an agent published in the GitHub repository CCDawn/Vibelution (21 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,406 tokens. 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.
Other agents, from other repositories
memory-consolidator
Use this agent ONLY when a human has just run /memory-seed and the new L1 atoms need folding into scenes and persona. Automatic consolidation no longer goes through this agent - it runs headless, outside the session. Do not invoke this agent on your own initiative.
Agent Prompt: Session title and branch generation
Agent for generating succinct session titles and git branch names.
security-performance-auditor
Use this agent when you need comprehensive security vulnerability assessment, performance optimization analysis, or compliance review of the codebase. Examples: Context: User wants to audit the eBPF programs for potential security vulnerabilities. user: 'Can you check our eBPF programs for any security issues?'…
reference-builder
Creates exhaustive technical references and API documentation. Generates comprehensive parameter listings, configuration guides, and searchable reference materials. Use PROACTIVELY for API docs, configuration references, or complete technical specifications.
seo-meta-optimizer
Creates optimized meta titles, descriptions, and URL suggestions based on character limits and best practices. Generates compelling, keyword-rich metadata. Use PROACTIVELY for new content.
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.