acceptance-orchestrato…
Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance…
Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
Claude Code
$ npx -y skills add sinhoneyy/master-skills --skill ai-native-cli --agent claude-code
Codex
$ npx skills add sinhoneyy/master-skills --skill ai-native-cli -a codex -y
Cursor
$ npx skills add sinhoneyy/master-skills --skill ai-native-cli -a cursor -y
Gemini CLI
$ npx skills add sinhoneyy/master-skills --skill ai-native-cli -a gemini-cli -y
GitHub Copilot
$ npx skills add sinhoneyy/master-skills --skill ai-native-cli -a github-copilot -y
OpenCode
$ npx skills add sinhoneyy/master-skills --skill ai-native-cli -a opencode -y
Windsurf
$ npx skills add sinhoneyy/master-skills --skill ai-native-cli -a windsurf -y
name: ai-native-cli description: "Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description." risk: safe source: https://github.com/ChaosRealmsAI/agent-cli-spec date_added: "2026-03-15"
When building or modifying CLI tools, follow these rules to make them safe and reliable for AI agents to use.
A comprehensive design specification for building AI-native CLI tools. It defines 98 rules across three certification levels (Agent-Friendly, Agent-Ready, Agent-Native) with prioritized requirements (P0/P1/P2). The spec covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, self-description, and a feedback loop via a built-in issue system.
1. **Agent-first** -- default output is JSON; human-friendly is opt-in via `--human` 2. **Agent is untrusted** -- validate all input at the same level as a public API 3. **Fail-Closed** -- when validation logic itself errors, deny by default 4. **Verifiable** -- every rule is written so it can be automatically checked
This spec uses two orthogonal axes:
Use layers for migration and certification:
Certification maps to layers:
Default is agent mode (JSON). Explicit flags to switch:
$ mycli list # default = JSON output (agent mode) $ mycli list --human # human-friendly: colored, tables, formatted $ mycli list --agent # explicit agent mode (override config if needed)
Every CLI tool MUST have an `agent/` directory at its project root. This is the tool's identity and behavior contract for AI agents.
agent/
brief.md # One paragraph: who am I, what can I do
rules/ # Behavior constraints (auto-registered)
trigger.md # When should an agent use this tool
workflow.md # Step-by-step usage flow
writeback.md # How to write feedback back
skills/ # Extended capabilities (auto-registered)
getting-started.md1. **--brief** (business card, injected into agent config) 2. **Every Command Response** (always-on context: data + rules + skills + issue) 3. **--help** (full self-description: brief + commands + rules + skills + issue) 4. **skills \<name\>** (on-demand deep dive into a specific skill)
Each level includes all rules from the previous level. Priority tag `[P0]`=agent breaks without it, `[P1]`=agent works but poorly, `[P2]`=nice to have.
Goal: CLI is a stable, callable API. Agent can invoke, parse, and handle errors.
**Output** -- default is JSON, stable schema
**Error** -- structured, to stderr, never interactive
**Exit Code** -- predictable failure signals
**Composability** -- clean pipe semantics
**Input** -- fail fast on bad input
**Safety** -- protect against agent mistakes
**Guardrails** -- runtime input protection
Goal: CLI is self-describing, well-named, and pipe-friendly. Agent discovers capabilities and chains commands without trial and error.
**Self-Description** -- agent discovers what CLI can do
Unified skill library for Claude, Codex, Cursor, Antigravity & AI agents — 2,660 skills across 15 domains
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