Copilot Skills

Agent skills for GitHub Copilot CLI that implement the CodeAct pattern — collapse multi-step tool chains into a single sandboxed Python execution.

Install

# Install a specific skill
gh skill install jsturtevant/copilot-skills monty-codeact
gh skill install jsturtevant/copilot-skills hyperlight-codeact

# Or browse and choose interactively
gh skill install jsturtevant/copilot-skills

Then /skills reload in Copilot CLI to pick them up.

Available Skills

monty-codeact

CodeAct with Pydantic Monty — a minimal, secure Python interpreter written in Rust.

• Sub-microsecond startup (<1μs)
• Tools called as natural Python functions: view(path="README.md")
• Lightweight: pip install pydantic-monty (~4.5MB)
• Auto-installs dependencies via uv if missing

Best for: fast, lightweight tool chaining where full Python isn't needed.

hyperlight-codeact

CodeAct with Hyperlight — micro-VM sandbox using WebAssembly.

• Full CPython runtime inside a Wasm sandbox
• Tools called via call_tool("view", path="README.md")
• Stronger isolation (separate micro-VM per execution)
• Auto-installs dependencies via uv if missing

Best for: when you need full Python support or stronger sandbox isolation.

Shared features

Both skills discover and register tools that match Copilot CLI built-in tool names:

Copilot CLI tool	Sandbox function	What it does
view	view() / call_tool("view")	Read files / list directories
create	create() / call_tool("create")	Create new files
edit	edit() / call_tool("edit")	Surgical string replacement
glob	glob() / call_tool("glob")	Find files by pattern
grep	grep() / call_tool("grep")	Search file contents (needs rg)
bash	bash() / call_tool("bash")	Run shell commands
sql	sql() / call_tool("sql")	SQLite queries
web_fetch	web_fetch() / call_tool("web_fetch")	Fetch URLs (needs curl)
github_api	github_api() / call_tool("github_api")	GitHub REST API (needs gh)

Why CodeAct?

Instead of N individual tool calls (model → tool → model → tool …), the agent writes one Python program that chains all the tools together and runs it in a single turn. This cuts latency by ~50% and token usage by 85%+ for multi-step tasks.

For more on the pattern, see CodeAct with Hyperlight from Microsoft.

License

MIT