With Scripts

With-Scripts: Bundled Gmail CLI Example

This example shows how to ship a real working tool inside a Logion course bundle. The course owns its codebase — there is no pip install, no npm install, no transitive dependency tree. Everything the agent needs to call the Gmail API lives in this bundle and was reviewed once when the course was published.

This is the canonical pattern for replacing "tell the user to install my CLI from npm" with "the course bundles the CLI."

Structure

with-scripts/
├── SKILL.md                  # this file
├── course/
│   └── capabilities.yaml     # file + terminal + web; gmail.googleapis.com; OAuth env
├── scripts/                  # thin entrypoints the agent invokes
│   ├── search.sh             # search messages
│   └── list-labels.sh        # list user labels
└── src/
    └── gmailcli/             # actual implementation
        ├── __init__.py
        ├── __main__.py       # python -m gmailcli {search,labels}
        ├── api.py            # Gmail REST client (stdlib urllib only)
        └── auth.py           # reads OAuth token from env

scripts/ holds short entrypoints — bash wrappers that set PYTHONPATH and invoke the Python module. src/ holds the real implementation. The separation is deliberate:

• The agent only invokes scripts/search.sh "query". Simple, discoverable surface.
• The bulk of the logic — API calls, error handling, JSON parsing — lives in src/gmailcli/. Reviewable, testable, organized.
• The bundle stays self-contained: stdlib only (urllib.request, json), no pip install anywhere.

When to use this template

Copy this structure when your course:

• Ships a non-trivial amount of code (more than one short script).
• Wants scripts/ to remain a clean surface for the agent to discover commands.
• Has real implementation worth organizing into modules.

If your course is a single 50-line script, the simpler with-references-and-scripts/ example is a better starting point.

How to use

1. Obtain a Gmail OAuth token

The course does not authenticate. The user obtains a token externally — via the Google OAuth playground, their own auth flow, or another tool — and exports it.

This is the trust-clean pattern: OAuth is the user's separate, informed consent decision, outside Logion's trust loop. The course only reads the token from GMAIL_OAUTH_TOKEN.

2. Export the token and invoke

export GMAIL_OAUTH_TOKEN="ya29...."
bash scripts/search.sh "from:alice subject:invoice"
bash scripts/list-labels.sh

Both print JSON to stdout. The agent should redirect output if persistence is needed.

Capability declarations

• tools: [file, terminal, web] — reads its own bundle, runs Python, makes HTTPS calls.
• network.allow_domains: [gmail.googleapis.com] — exactly the one host the bundled code contacts.
• secrets.env: [GMAIL_OAUTH_TOKEN] — the only env var the bundle reads.
• No filesystem.write — output goes to stdout.
• No human_approval — the user already consented by exporting the token.

What an author should change when copying

1. name: in frontmatter (must match the directory name).
2. description: (specific about what, when, why).
3. Replace src/gmailcli/ with your own package directory.
4. Update scripts/*.sh entrypoints to dispatch to your package.
5. Update course/capabilities.yaml to declare only what your code actually does — drop the network domain if you don't need it, etc.
6. Keep the stdlib-only constraint. If you find yourself wanting pip install, see the "Self-contained bundle rule" in examples/courses/README.md.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: nicolasmelo1
• Source: nicolasmelo1/logion
• License: MIT
• Homepage: https://logion.sh

Install and usage instructions live in the source repository linked above.