agentsquire 0.4.0

Let a Python package carry its own agent integrations and install them into whatever agent harness is present.

AgentSquire

A reusable Python library + CLI that lets a Python package carry its own agent integrations (Agent Skills) and install them into whatever agent harness is present - the executable is the framework.

Your CLI ships its skills as package data inside its own wheel, adds agentsquire as a plain pip dependency, and mounts a ready-made subcommand group. Your users only ever see your tool:

$ awiki skills install
installed awiki-search -> /home/you/.claude/skills/awiki-search

Supported harnesses at launch: Claude Code, pi, Hermes, and opencode. Detection is by marker directory; every operation is local (no network in any verb). Each harness's directories, scopes, and behaviour are recorded in docs/harnesses.md.

Installation

pip install agentsquire

This installs the library and its own CLI, squire (aliased agentsquire). As a consumer you normally add agentsquire to your package's dependencies rather than having users install it directly - it rides in your wheel, and your users only ever see your tool.

agentsquire dogfoods its own contract: it carries the developing-with-agentsquire skill and its reference docs as package data. When you build an app on agentsquire, install that skill into your own agent harness so the harness has the integration know-how on hand while it works:

squire skills install

That copies developing-with-agentsquire (how to ship skills, mount the group, and wire the staleness hook) into every detected harness. The same reference docs are served at the terminal from the installed wheel - no checkout needed:

squire guide              # topics: api, harnesses, integration
squire guide api          # the Python API reference
squire guide integration  # this consumer integration guide

From source

Clone the repository and install it into your environment:

git clone https://github.com/TacoTakumi/AgentSquire.git
cd AgentSquire
pip install .

Add -e (pip install -e .) for an editable install if you plan to work on agentsquire itself. The repo uses uv: uv sync provisions a dev environment and uv run pytest runs the test suite.

Consumer integration guide

1. Ship skills as package data

Lay each skill out as a directory containing a SKILL.md (the agentskills.io format) under a skills/ resource inside your importable package:

your_pkg/
    __init__.py
    skills/
        my-skill/
            SKILL.md
            reference.md

The skills ride inside your wheel; make sure your build backend includes package data (hatchling includes it by default, setuptools needs include-package-data). No source checkout is needed at run time - skills are enumerated straight from the installed wheel.

2. Mount the subcommand group

One call returns a click group with install, status, update, and uninstall subcommands, parameterized by your package name, the resource path (default "skills"), and the default scope.

For a click CLI:

import click

from agentsquire.cli import skills_command_group


@click.group()
def cli():
    """Your CLI."""


cli.add_command(skills_command_group("your_pkg", default_scope="user"))

For a typer CLI, mount onto the underlying click command:

import typer
import typer.main

from agentsquire.cli import skills_command_group

app = typer.Typer()


@app.callback()
def main():
    """Your CLI."""


cli = typer.main.get_command(app)
cli.add_command(skills_command_group("your_pkg", default_scope="user"))

Your users now run your-cli skills install and friends. Every subcommand takes --scope user|project (overriding your declared default) and --harness NAME (default: all detected harnesses).

Choosing the default scope: user installs into the harness's per-user skills directory and follows the user everywhere - right for general-purpose tools. project installs into the current project's directory - right for skills that only make sense inside a repository that uses your tool.

3. Surface updates proactively (optional)

Place the one-call staleness hook at your CLI entry point. When installed skills have updates available it prints a single advisory line on stderr, for example:

your-cli: a skills update is available for 1 skill (alpha); run `your-cli skills update`

The hook is notice-only: it never prompts, never reads stdin, and never updates anything itself - the explicit skills update verb stays the sole updater. It writes nothing to stdout, never changes your exit code, and swallows its own errors, so it can never break the command it runs inside:

from agentsquire import BundledPackageDataSource, check_stale


def main():
    check_stale(
        BundledPackageDataSource("your_pkg"),
        prog_name="your-cli",
        update_command="your-cli skills update",
    )
    # ... the rest of your entry point

The notice shows unless a suppression gate holds: CI set to a non-empty value, or AGENTSQUIRE_NO_UPDATE_CHECK set to a non-empty value. It is not gated on an interactive terminal - it fires on non-TTY stderr too, so an agent harness that runs your CLI with captured stderr still sees that an update is available. Suppression is presence-disables, the NO_COLOR convention: any non-empty value disables the notice (CI=false and AGENTSQUIRE_NO_UPDATE_CHECK=0 both suppress), while an empty string is treated as unset.

4. Mark your package as skill-carrying (optional)

One pyproject line registers your package under the agentsquire.skills entry-point group. Nothing reads it today - it is reserved for a future environment-wide listing of skill-carrying packages and changes no behaviour:

[project.entry-points."agentsquire.skills"]
your_pkg = "your_pkg"

The provenance and update model

Installs are plain copies - no symlinks, no lockfile, no references back into site-packages - so an installed skill survives upgrade or removal of your package. Each installed SKILL.md carries a provenance stamp in its frontmatter metadata.agentsquire map: installer, installer_version, source_package, source_version, and content_hash. The skill body is byte-identical to what you shipped.

status classifies every skill by local hash compares only (no network, ever): not-installed, up-to-date, update-available (your shipped copy moved on), or locally-modified (the user edited the install, the directory carries no stamp, or a symlink sits at the target - none of them ours to touch). update refreshes update-available skills and skips locally modified ones unless --force is given; uninstall removes only directories whose stamp names your package. User content is never silently overwritten or deleted - a pre-existing symlink at a target (a common hand-wired setup) is reported and skipped, never followed or clobbered.

Python API

Everything the CLI group does is available as plain Python - enumerate, detect, install, status, update, uninstall, and the staleness check - with no CLI involved. See docs/api.md for the reference.

License

MIT.