Agent skill
docs-guide
LLM guide for creating, publishing, and running Enact tools
Install this agent skill to your Project
npx add-skill https://github.com/EnactProtocol/enact/tree/main/tools/docs-guide
SKILL.md
Enact LLM Guide
Enact: Containerized tools with structured I/O for AI agents.
Commands
enact run ./tool --input "key=value" # Run local tool
enact run ./tool --args '{"key":"value"}' # Run with JSON
enact run author/tool --input "x=y" # Run installed tool
enact install author/tool # Install to project
enact install author/tool -g # Install globally
enact search "query" # Find tools
enact sign ./tool && enact publish ./tool # Publish
Tool Structure
Tools use a two-file model:
my-tool/
├── skill.package.yml # Technical manifest (execution config)
├── SKILL.md # Agent-facing documentation
└── main.py # Your code (any language)
skill.package.yml Template
enact: "2.0.0"
name: "namespace/category/tool-name"
version: "1.0.0"
description: "What it does"
from: "python:3.12-slim"
build: "pip install requests pandas"
timeout: "30s"
scripts:
run:
command: "python /work/main.py {{input}}"
inputSchema:
type: object
properties:
input:
type: string
description: "Input description"
required: [input]
outputSchema:
type: object
properties:
result:
type: string
env:
API_KEY:
description: "API key"
secret: true
LOG_LEVEL:
description: "Log level"
default: "info"
tags: [category, keywords]
Field Reference
| Field | Required | Description |
|---|---|---|
name |
Yes | namespace/category/tool |
description |
Yes | What it does |
scripts |
No* | Named scripts with {{param}} substitution |
from |
No | Docker image (default: alpine:latest) |
build |
No | Build commands (string or array), cached |
inputSchema |
No | JSON Schema for inputs (under each script) |
outputSchema |
No | JSON Schema for outputs |
env |
No | Environment vars (secret: true for keyring) |
timeout |
No | Max runtime (default: 30s) |
version |
No | Semver version |
tags |
No | Discovery keywords |
*Tools without scripts are LLM instruction tools (markdown interpreted by AI).
Examples by Language
Python
from: "python:3.12-slim"
build: "pip install pandas"
scripts:
run:
command: "python /work/main.py {{input}}"
Node.js
from: "node:20-alpine"
build: "npm install"
scripts:
run:
command: "node /work/index.js {{input}}"
Rust
from: "rust:1.83-slim"
build: "rustc /work/main.rs -o /work/app"
scripts:
run:
command: "/work/app {{input}}"
Go
from: "golang:1.22-alpine"
build: "go build -o /work/app /work/main.go"
scripts:
run:
command: "/work/app {{input}}"
Shell (no build)
scripts:
run: "echo {{name}}"
Source Code Pattern
Always output JSON matching outputSchema:
#!/usr/bin/env python3
import sys, json
input_val = sys.argv[1]
result = {"result": input_val.upper()}
print(json.dumps(result))
Secrets
env:
API_KEY:
description: "API key"
secret: true # Stored in OS keyring, not .env
User sets: enact env set API_KEY --secret --namespace myorg/tools
Access in code via environment variable: os.environ['API_KEY']
Two Tool Types
- Container tools (has
scripts): Runs in Docker, deterministic - Instruction tools (no
scripts): Markdown body interpreted by LLM
Workflow
# 1. Create
mkdir my-tool && cd my-tool
# Create skill.package.yml, SKILL.md, and source files
# 2. Test
enact run . --input "test=value"
# 3. Publish
enact auth login
enact sign .
enact publish .
Checklist
-
name: namespace/category/tool format -
description: clear, searchable -
scripts: named scripts with{{param}}substitution -
inputSchema: validates inputs (under each script) -
outputSchema: documents output -
from: pinned image version (notlatest) -
build: installs dependencies - Source outputs valid JSON
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