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 daxaur/openpaw --skill c-aigit clone --depth 1 https://github.com/daxaur/openpawWrote 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/daxaur/openpaw/c-ai)<a href="https://agentmods.dev/skills/daxaur/openpaw/c-ai"><img src="https://agentmods.dev/badge/skills/daxaur/openpaw/c-ai.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 | $0.00034 | $0.00451 |
| Opus 5 | $0.00017 | $0.00226 |
| Sonnet 5 | $0.00007 | $0.00090 |
| Haiku 4.5 | $0.00003 | $0.00045 |
Grade A, and why
c-ai 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 7d 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.
What it actually says
AI / LLM Tools
llm (Simon Willison)
# Quick prompt
llm "What is the capital of France?"
# Pipe text for processing
cat article.txt | llm "Summarize this in 3 bullet points"
git diff | llm "Write a commit message for these changes"
pbpaste | llm "Fix the grammar in this text"
# Interactive chat
llm chat
# Use specific model
llm -m claude-3.5-sonnet "Explain quantum computing"
llm -m gpt-4o "Review this code"
# List available models
llm models
# Install model plugins
llm install llm-claude-3
llm install llm-ollama # local models
# View prompt/response history
llm logs list
llm logs last
aichat
# Quick prompt
aichat "Explain Docker in simple terms"
# Pipe input
cat code.py | aichat "Find bugs in this code"
# Interactive REPL
aichat
# Shell assistant (generates and runs commands)
aichat -e "find all files larger than 100MB"
# Specific model
aichat -m claude-3.5-sonnet "Hello"
# List models
aichat --list-models
Guidelines
- Use
llmfor piping text through LLMs (summarize, translate, analyze) - Use
aichat -efor generating shell commands from natural language - Both tools store API keys locally — set up once with auth commands
llmhas the richest plugin ecosystem (100+ model providers)aichatis faster (Rust) and has built-in RAG support- These tools use separate API keys from Claude Code — user pays per token
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.
- 7d ago First seen · 69 lines · 34 tokens per session scan A 425b6fd9b647
c-ai is a skill published in the GitHub repository daxaur/openpaw (167 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 451 once invoked, about $0.0002 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
openai-patterns
Production OpenAI API patterns — model selection, prompt engineering, function calling, streaming, error handling, cost control, and structured outputs.
prompt-optimizer
Analyze and improve LLM prompts for clarity, precision, and output quality. Use when a prompt produces inconsistent results, the model ignores instructions, outputs are too long/short, or quality is below expectations.
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
outlines
Outlines: structured JSON/regex/Pydantic LLM generation.
guidance
Constrain LLM output with grammars; guarantee valid JSON.
instructor
Structured LLM outputs validated with Pydantic.