skillager 0.1.2

A Python environment skill registry and activation layer for coding agents.

Skillager

Agent skills are useful. Loading all of them into every chat is not.

Skillager is a local CLI that lets projects, Python libraries, tools, and personal skill repos ship useful agent skills without turning every session into a wall of instructions. It discovers skills, scans them, asks for human approval, and gives agents a small, fast way to find the right skill only when the task needs it.

install package -> discover skills -> approve safety -> agent uses approved metadata -> expose only what matters

Quickstart

uv tool install skillager
cd my-project
skillager status
skillager setup

No uv:

pipx install skillager
# or
python -m pip install --user skillager
python -m skillager setup

setup is the approval gate. It discovers skills in the current project and environment, scans them, asks what audience you care about, and never trusts a skill unless you approve it.

After setup, restart Codex or Claude in the same directory and tell it what you are doing. Skillager installs a tiny project handoff so the agent knows to run skillager status once, use approved metadata, and avoid loading unapproved skill bodies.

The Problem

Skills want to live everywhere:

• inside libraries, next to the APIs they explain
• inside projects, next to team workflows
• inside global agent directories
• inside personal or community skill repos

But agents should not see every skill all the time. Irrelevant skills burn context. Unreviewed skills are a safety risk. Similar skills compete. Package-installed skills are hard for agents to discover.

Skillager gives that ecosystem a local registry and approval gate.

The Mental Model

Skillager keeps two decisions separate:

• Approval: the user reviewed a skill at its current content hash.
• Exposure: the skill is available to an agent in the current project.

An approved skill does not have to be loaded into the agent. It can stay in Skillager's index until a task needs it. When it should be available, Skillager writes one of three project-level representations:

• native: the full reviewed skill directory copied into the agent's project skill directory
• stub: a tiny native handle that activates the full skill through Skillager on demand
• router: one compact native skill for a curated tag like gis, workflows, or release

This keeps the default context small while still giving agents a deterministic path to approved skills.

For Library Authors

If you maintain a Python library, Skillager gives you a way to ship agent-facing guidance with the package itself. Users can discover those skills after install, review them locally, and expose all, or just the ones relevant to their project.

Example:

your_package/
  __init__.py
  .skills/
    data-cleaning/
      SKILL.md
      skillager.yaml
      references/
      scripts/

When a user installs your package, Skillager can discover those skills without importing your library. The user still reviews and approves them before any agent can activate them.

This lets a library ship:

• user-facing skills for using the API well
• maintainer skills for internal development workflows
• domain skills that explain correctness rules and edge cases
• migration skills for version upgrades

See the library author guide for metadata and packaging details.

What Skillager Does

• Discovers skills from projects, .venv, installed packages, global agent dirs, and skill repos.
• Scans full skill directories before approval.
• Tracks trust by skill ID and content hash.
• Keeps search/list/show metadata safe and compact for agents.
• Materializes only reviewed skills into Codex or Claude native skill directories.
• Supports stubs and routers for large skill collections.
• Records compact local usage signals for lookback, without storing transcripts or skill bodies.
• Treats manually installed native skills as user-installed while still warning on risk.

Safety Shape

The built-in scanner is deterministic and local. It looks for common agent-risk patterns like instruction override attempts, hidden prompt requests, secret exfiltration language, credential paths, download-and-execute flows, network callbacks involving secrets, unattended approval language, shell execution requests, hidden control characters, encoded blobs, and oversized content.

It is not a proof of safety. It is a review aid.

The hard rule is simpler: agents should not activate, materialize, or rely on skills that have not been approved by the user or project trust store.

Skill Repos Without Context Flooding

Skill repositories are collections. Collections are inventory; tags are curation; project attachment is intent.

skillager collection add ~/skills/workflows --name workflows
skillager collection enable workflows
skillager setup
skillager materialize --tag workflows --mode index --agent codex --scope project

collection enable creates or updates a catalog tag for the collection and attaches that tag to the current project. The agent sees one router skill, not the whole repo. It activates a specific reviewed skill only when the task calls for it.

Lookback

Skillager learns from usage as a local feedback loop. It records compact events such as search result IDs, activations, materialization status, and explicit feedback. It does not store chat transcripts or skill bodies.

The next skillager status can tell the agent that lookback is pending. Then the user can decide whether to promote a repeatedly useful skill, keep a broad skill route-only, block an unwanted one, or resolve overlapping skills.

More Docs

• User guide
• Agent CLI guide
• Skill repositories
• Library author guide
• Safety model
• Security policy

External contributions are not being accepted yet while the 0.1 API and workflow settle.

Development

uv run python -m unittest discover -s tests
uv build

Skillager is released under the MIT License.