agentcli

Shared conventions for command-line tools whose primary callers are agents. It owns no food domain: it owns predictable errors, JSON output, skills, in-binary guides, and the candidate record used for composition.

Use it in a package

Install the distribution (the import name remains agentcli):

uv add click-agentcli

Register the shared output, guide, and skill commands on a Click CLI:

import click

from agentcli import (
    JsonAwareGroup,
    emit,
    guide_command,
    json_option,
    skill_group,
)

GUIDE = """# acme guide

Use `acme hello` to print a greeting.
"""


@click.group(cls=JsonAwareGroup)
def cli() -> None:
    """Acme's agent-facing CLI."""


@cli.command()
@json_option
def hello(json_output: bool) -> None:
    """Print a greeting."""
    emit(
        {"message": "hello"},
        json_output=json_output,
        human=lambda result: [result["message"]],
    )


cli.add_command(guide_command(GUIDE))
cli.add_command(skill_group(name="acme", package="acme"))

The command has readable output for people and one stable document for agents:

$ acme hello
hello
$ acme hello --json
{"ok":true,"data":{"message":"hello"}}
$ acme guide
# acme guide
...
$ acme skill install
copied  /home/me/.agents/skills/acme

Declare the entry point and ship the skill inside the import package. For Hatchling, a project-root SKILL.md can be mapped into the required wheel location like this:

[project.scripts]
acme = "acme.cli:cli"

[tool.hatch.build.targets.wheel.force-include]
"SKILL.md" = "acme/skills/acme/SKILL.md"

skill_group(name="acme", package="acme") expects an installed wheel to contain acme/skills/acme/SKILL.md. The same file may stay at the repository root for source-checkout use. Its frontmatter name must match the skill name:

---
name: acme
description: Use Acme from an agent.
---

Run `acme guide` for the complete manual.

Add @json_option to each command that supports structured output and call emit once with both the data and its human renderer. JsonAwareGroup then keeps parse failures structured when --json was requested.

Develop this package

uv sync --project .
uv run --project . pytest -q

CLI contract

Every consuming tool uses click, declares --json per command with json_option, and makes its top-level group JsonAwareGroup. The group scans raw arguments so even parse failures that happen before a subcommand exists honour a --json request. Importing agentcli.exits also changes Click's own usage-error code from 2 to 1; consumers must not repeat that correction.

An exhausted request budget is code 1, because the caller can lift it. A proportional recipe fit with no solution is code 3.

--json emits exactly one JSON object on stdout and nothing else. Success and failure are symmetric:

{"ok":true,"data":{}}
{"ok":false,"error":{"message":"..."}}

A search with no matches is successful with an empty list. Under --json, errors go to stdout so a caller never has to merge streams to recover the one promised document. Human errors go to stderr.

The stable public surface is:

• UsageError, RemoteError, AssertionFailure, and StrictFailure.
• dumps, emit, emit_error, json_option, and limit_option.
• JsonAwareGroup for every consuming tool's top-level group.
• skill_group(name=..., package=...) for skill install, uninstall, and status. Installation refuses an unrelated destination, recognises owned broken symlinks, copies by default, and supports --link, --to, and --dry-run. With no options it installs everywhere the skill is wanted and refreshes its own earlier copies, so plain install is the whole job; a directory holding somebody else's skill is still refused.
• guide_command(text) for a complete manual available without a network.
• candidate, macro_options, matches, rank, and unverifiable for the shared composition record and filters below.

Candidate contract

Candidate sources answer the same question: filter things someone could eat by per-serving macros, then rank them with provenance. Recipes and restaurant meals therefore emit the same record:

{
  "kind":"recipe",
  "id":"sourdough-pizza",
  "name":"Sourdough Pizza",
  "per_serving":{"kcal":384.2,"protein":31.5,"fat":12.1,"carbs":38.4},
  "complete":true,
  "detail":{}
}

kind is recipe or meal. id is accepted back by the emitting tool; display-only slugs are not identifiers. Source-specific fields live under detail, which shared code never reads.

Sources accept macro_options (--max-kcal, --min-protein) and use rank. The rank key is unrounded protein per 100 kcal, then absolute protein, then name. --max-kcal 0 is valid because zero-calorie records exist.

per_serving contains only macros actually known by the source. Missing is never filled with zero. complete exposes whether the full shape is present; a candidate missing a requested filter macro is excluded and returned in the source's unverifiable or equivalent bucket. Every source emits that bucket, even when its loader makes it structurally empty.

This contract is the reason the tools can be independent packages: an orchestrator can merge and rank results without knowing which source answered.