CowAgent is an open-source AI assistant and agent framework that plans tasks, uses tools, manages skills, and maintains long-term memory and a personal knowledge base. People run it on a computer or server and connect it to language models, external services, and messaging channels. Its catalogue skills extend the assistant's abilities.
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 zhayujie/CowAgent --skill skill-creatorgit clone --depth 1 https://github.com/zhayujie/CowAgentWrote 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/zhayujie/cowagent/skill-creator)<a href="https://agentmods.dev/skills/zhayujie/cowagent/skill-creator"><img src="https://agentmods.dev/badge/skills/zhayujie/cowagent/skill-creator.svg" alt="Measured on agentmods" height="20"></a>- Snyk fail
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 3 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
- high Rogue Agent · line 269 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00061 | $0.04195 |
| Opus 5 | $0.00030 | $0.02098 |
| Sonnet 5 | $0.00012 | $0.00839 |
| Haiku 4.5 | $0.00006 | $0.00419 |
Grade A, and why
skill-creator 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 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.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- skill-creator — 97% identical, 16 lines differ
How it starts
The opening of the file, as written. The whole thing — 430 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Creator
This skill provides guidance for creating effective skills using the existing tool system.
About Skills
Skills are modular, self-contained packages that extend the agent's capabilities by providing specialized knowledge, workflows, and tools. They transform a general-purpose agent into a specialized agent equipped with procedural knowledge.
What Skills Provide
- Specialized workflows - Multi-step procedures for specific domains
- Tool integrations - Instructions for working with specific file formats or APIs
- Domain expertise - Company-specific knowledge, schemas, business logic
- Bundled resources - Scripts, references, and assets for complex tasks
Core Principle
Concise is Key: Only add context the agent doesn't already have. Challenge each piece of information: "Does this justify its token cost?" Prefer concise examples over verbose explanations.
Skill Structure
Every skill consists of a required SKILL.md file and optional bundled resources:
skill-name/
├── SKILL.md (required)
│ ├── YAML frontmatter metadata (required)
│ │ ├── name: (required)
│ │ └── description: (required)
│ └── Markdown instructions (required)
└── Bundled Resources (optional)
├── scripts/ - Executable code (Python/Bash/etc.)
├── references/ - Documentation intended to be loaded into context as needed
└── assets/ - Files used in output (templates, icons, fonts, etc.)
SKILL.md Components
Frontmatter (YAML) - Required fields:
- name: Skill name in hyphen-case (e.g.,
weather-api,pdf-editor) - description: CRITICAL - Primary triggering mechanism
- Must clearly describe what the skill does
- Must explicitly state when to use it
- Include specific trigger scenarios and keywords
- All "when to use" info goes here, NOT in body
- Example:
"PDF document processing with rotation, merging, splitting, and text extraction. Use when user needs to: (1) Rotate PDF pages, (2) Merge multiple PDFs, (3) Split PDF files, (4) Extract text from PDFs."
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.
- 8d ago First seen · 430 lines · 61 tokens per session scan A 640be6c97808
skill-creator is a skill published in the GitHub repository zhayujie/CowAgent (46,796 stars, last pushed yesterday), licensed MIT. It adds 61 tokens to every session and 4,195 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
fastgpt-docker-deploy
FastGPT Docker Compose self-hosting deployment workflow. Use when a user asks an AI agent to deploy FastGPT with Docker by referencing /deploy/SKILL.md, including creating an empty fastgpt directory, running the install script in non-interactive mode, starting Docker Compose, monitoring service health, troubleshooting…
byok-custom-model
Register a custom LLM endpoint with your own API key for chat in Starchild. Use when adding a personal Anthropic, OpenAI, Grok, Qwen, DeepSeek, Meta (Muse Spark), NEAR AI, or Venice key as a chat model (e.g. add my Claude key, register DeepSeek, use Muse Spark 1.1).
playwright
Use when the task requires capturing or automating a real browser from the terminal.
pydantic-ai
Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support.
html-artifacts
Author the HTML for a plan artifact, dashboard iframe, or Slack attachment — structure, design plan, available runtime, theming, and craft. Read this before writing HTML for saveplan, outputiframe, or slackattachhtml.
langbot-plugin-dev
Develop, debug, and test LangBot plugins. Use when creating new LangBot plugins, fixing plugin bugs, setting up a LangBot test environment, or testing plugins via WebSocket. Covers plugin component architecture (EventListener, Command, Tool), the plugin SDK API (invokellm, getllmmodels, sendmessage, plugin storage)…