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 skills add shitianfang/coware-skills --skill cowaregit clone --depth 1 https://github.com/shitianfang/coware-skillsWrote 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/skills/shitianfang/coware-skills/coware)<a href="https://agentmods.dev/skills/shitianfang/coware-skills/coware"><img src="https://agentmods.dev/badge/skills/shitianfang/coware-skills/coware/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/skills/shitianfang/coware-skills/coware"><img src="https://agentmods.dev/badge/skills/shitianfang/coware-skills/coware.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 | $0.00100 | $0.02372 |
| Opus 5 | $0.00050 | $0.01186 |
| Sonnet 5 | $0.00020 | $0.00474 |
| Haiku 4.5 | $0.00010 | $0.00237 |
Grade A, and why
coware 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 12d 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Coware — Shared Living Specs
Coware keeps AI agents aligned by syncing shared interface specs across your team. Every agent reads the same spec before writing code — no more merge conflicts from mismatched APIs, field names, or return types.
FIRST: Check initialization status
Check if .coware/config.json exists in the project root.
IF .coware/config.json EXISTS → Project is initialized
This project already uses Coware. Follow this workflow:
Before coding
- Run:
node .coware/pull.mjs- This fetches the latest specs from the Coware server.
- If the command fails (network error, server down), proceed with whatever
local
.coware/specs/*.mdfiles exist.
- Read ALL
.mdfiles in.coware/specs/.- These specs are the source of truth for all interfaces, types, API contracts, and data shapes in this project.
- You are ready to code. Follow the specs exactly.
While coding
- Follow specs exactly. Match field names, types, return formats, status codes.
- If a spec needs to change (new endpoint, renamed field, etc.):
- Update the relevant
.coware/specs/<slug>.mdfile FIRST. - Run:
node .coware/sync.mjs - THEN update the code to match.
- Never let code drift from specs.
- Update the relevant
After coding
- If you created or modified any spec files, run:
node .coware/sync.mjs- This pushes updated specs to the server so teammates get them.
- If sync fails, the local spec files are still the source of truth.
- If you did NOT change any specs, no action needed.
IF .coware/config.json DOES NOT EXIST → New project setup
This project has not been initialized with Coware yet.
Setup instructions
- Fetch the setup guide:
https://coware.team/llms.txt - Read the response carefully.
- Follow ALL instructions in the guide from start to finish, in order.
- The guide will walk you through: login, project creation/joining, file setup, and spec generation.
- Execute ALL steps in ONE go. Do NOT stop to ask the user questions mid-flow.
- The ONLY acceptable way to ask the user something is via a tool-based selection dialog. NEVER stop your turn to ask via text.
- After setup completes, follow the "Project is initialized" workflow above.
What ships with it
3 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.
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.
- 12d ago First seen · 274 lines · 100 tokens per session scan A 86a8308346e6
coware is a skill published in the GitHub repository shitianfang/coware-skills (1 stars, last pushed 5mo ago), licensed MIT. It adds 100 tokens to every session and 2,372 once invoked, about $0.0005 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…