311 lines
12 KiB
Markdown
311 lines
12 KiB
Markdown
---
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name: ai-native-cli
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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."
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risk: safe
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source: https://github.com/ChaosRealmsAI/agent-cli-spec
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date_added: "2026-03-15"
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---
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# Agent-Friendly CLI Spec v0.1
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When building or modifying CLI tools, follow these rules to make them safe and
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reliable for AI agents to use.
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## Overview
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A comprehensive design specification for building AI-native CLI tools. It defines
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98 rules across three certification levels (Agent-Friendly, Agent-Ready, Agent-Native)
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with prioritized requirements (P0/P1/P2). The spec covers structured JSON output,
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error handling, input contracts, safety guardrails, exit codes, self-description,
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and a feedback loop via a built-in issue system.
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## When to Use This Skill
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- Use when building a new CLI tool that AI agents will invoke
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- Use when retrofitting an existing CLI to be agent-friendly
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- Use when designing command-line interfaces for automation pipelines
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- Use when auditing a CLI tool's compliance with agent-safety standards
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## Core Philosophy
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1. **Agent-first** -- default output is JSON; human-friendly is opt-in via `--human`
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2. **Agent is untrusted** -- validate all input at the same level as a public API
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3. **Fail-Closed** -- when validation logic itself errors, deny by default
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4. **Verifiable** -- every rule is written so it can be automatically checked
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## Layer Model
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This spec uses two orthogonal axes:
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- **Layer** answers rollout scope: `core`, `recommended`, `ecosystem`
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- **Priority** answers severity: `P0`, `P1`, `P2`
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Use layers for migration and certification:
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- **core** -- execution contract: JSON, errors, exit codes, stdout/stderr, safety
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- **recommended** -- better machine UX: self-description, explicit modes, richer schemas
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- **ecosystem** -- agent-native integration: `agent/`, `skills`, `issue`, inline context
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Certification maps to layers:
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- **Agent-Friendly** -- all `core` rules pass
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- **Agent-Ready** -- all `core` + `recommended` rules pass
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- **Agent-Native** -- all layers pass
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## How It Works
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### Step 1: Output Mode
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Default is agent mode (JSON). Explicit flags to switch:
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```bash
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$ mycli list # default = JSON output (agent mode)
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$ mycli list --human # human-friendly: colored, tables, formatted
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$ mycli list --agent # explicit agent mode (override config if needed)
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```
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- **Default (no flag)** -- JSON to stdout. Agent never needs to add a flag.
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- **--human** -- human-friendly format (colors, tables, progress bars)
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- **--agent** -- explicit JSON mode (useful when env/config overrides default)
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### Step 2: agent/ Directory Convention
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Every CLI tool MUST have an `agent/` directory at its project root. This is the
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tool's identity and behavior contract for AI agents.
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```
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agent/
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brief.md # One paragraph: who am I, what can I do
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rules/ # Behavior constraints (auto-registered)
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trigger.md # When should an agent use this tool
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workflow.md # Step-by-step usage flow
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writeback.md # How to write feedback back
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skills/ # Extended capabilities (auto-registered)
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getting-started.md
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```
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### Step 3: Four Levels of Self-Description
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1. **--brief** (business card, injected into agent config)
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2. **Every Command Response** (always-on context: data + rules + skills + issue)
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3. **--help** (full self-description: brief + commands + rules + skills + issue)
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4. **skills \<name\>** (on-demand deep dive into a specific skill)
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## Certification Requirements
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Each level includes all rules from the previous level.
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Priority tag `[P0]`=agent breaks without it, `[P1]`=agent works but poorly, `[P2]`=nice to have.
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### Level 1: Agent-Friendly (core -- 20 rules)
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Goal: CLI is a stable, callable API. Agent can invoke, parse, and handle errors.
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**Output** -- default is JSON, stable schema
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- `[P0]` O1: Default output is JSON. No `--json` flag needed
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- `[P0]` O2: JSON MUST pass `jq .` validation
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- `[P0]` O3: JSON schema MUST NOT change within same version
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**Error** -- structured, to stderr, never interactive
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- `[P0]` E1: Errors -> `{"error":true, "code":"...", "message":"...", "suggestion":"..."}` to stderr
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- `[P0]` E4: Error has machine-readable `code` (e.g. `MISSING_REQUIRED`)
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- `[P0]` E5: Error has human-readable `message`
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- `[P0]` E7: On error, NEVER enter interactive mode -- exit immediately
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- `[P0]` E8: Error codes are API contracts -- MUST NOT rename across versions
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**Exit Code** -- predictable failure signals
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- `[P0]` X3: Parameter/usage errors MUST exit 2
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- `[P0]` X9: Failures MUST exit non-zero -- never exit 0 then report error in stdout
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**Composability** -- clean pipe semantics
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- `[P0]` C1: stdout is for data ONLY
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- `[P0]` C2: logs, progress, warnings go to stderr ONLY
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**Input** -- fail fast on bad input
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- `[P1]` I4: Missing required param -> structured error, never interactive prompt
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- `[P1]` I5: Type mismatch -> exit 2 + structured error
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**Safety** -- protect against agent mistakes
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- `[P1]` S1: Destructive ops require `--yes` confirmation
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- `[P1]` S4: Reject `../../` path traversal, control chars
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**Guardrails** -- runtime input protection
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- `[P1]` G1: Unknown flags rejected with exit 2
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- `[P1]` G2: Detect API key / token patterns in args, reject execution
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- `[P1]` G3: Reject sensitive file paths (*.env, *.key, *.pem)
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- `[P1]` G8: Reject shell metacharacters in arguments (; | && $())
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### Level 2: Agent-Ready (+ recommended -- 59 rules)
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Goal: CLI is self-describing, well-named, and pipe-friendly. Agent discovers capabilities and chains commands without trial and error.
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**Self-Description** -- agent discovers what CLI can do
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- `[P1]` D1: `--help` outputs structured JSON with `commands[]`
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- `[P1]` D3: Schema has required fields (help, commands)
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- `[P1]` D4: All parameters have type declarations
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- `[P1]` D7: Parameters annotated as required/optional
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- `[P1]` D9: Every command has a description
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- `[P1]` D11: `--help` outputs JSON with help, rules, skills, commands
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- `[P1]` D15: `--brief` outputs `agent/brief.md` content
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- `[P1]` D16: Default JSON (agent mode), `--human` for human-friendly
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- `[P2]` D2/D5/D6/D8/D10: per-command help, enums, defaults, output schema, version
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**Input** -- unambiguous calling convention
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- `[P1]` I1: All flags use `--long-name` format
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- `[P1]` I2: No positional argument ambiguity
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- `[P2]` I3/I6/I7: --json-input, boolean --no-X, array params
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**Error**
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- `[P1]` E6: Error includes `suggestion` field
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- `[P2]` E2/E3: errors to stderr, error JSON valid
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**Safety**
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- `[P1]` S8: `--sanitize` flag for external input
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- `[P2]` S2/S3/S5/S6/S7: default deny, --dry-run, no auto-update, destructive marking
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**Exit Code**
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- `[P1]` X1: 0 = success
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- `[P2]` X2/X4-X8: 1=general, 10=auth, 11=permission, 20=not-found, 30=conflict
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**Composability**
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- `[P1]` C6: No interactive prompts in pipe mode
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- `[P2]` C3/C4/C5/C7: pipe-friendly, --quiet, pipe chain, idempotency
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**Naming** -- predictable flag conventions
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- `[P1]` N4: Reserved flags (--agent, --human, --brief, --help, --version, --yes, --dry-run, --quiet, --fields)
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- `[P2]` N1/N2/N3/N5/N6: consistent naming, kebab-case, max 3 levels, --version semver
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**Guardrails**
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- `[P1]` I8/I9: no implicit state, non-interactive auth
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- `[P1]` G6/G9: precondition checks, fail-closed
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- `[P2]` G4/G5/G7: permission levels, PII redaction, batch limits
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#### Reserved Flags
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| Flag | Semantics | Notes |
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|------|-----------|-------|
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| `--agent` | JSON output (default) | Explicit override |
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| `--human` | Human-friendly output | Colors, tables, formatted |
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| `--brief` | One-paragraph identity | For sync into agent config |
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| `--help` | Full self-description JSON | Brief + commands + rules + skills + issue |
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| `--version` | Semver version string | |
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| `--yes` | Confirm destructive ops | Required for delete/destroy |
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| `--dry-run` | Preview without executing | |
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| `--quiet` | Suppress stderr output | |
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| `--fields` | Filter output fields | Save tokens |
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### Level 3: Agent-Native (+ ecosystem -- 19 rules)
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Goal: CLI has identity, behavior contract, skill system, and feedback loop. Agent can learn the tool, extend its use, and report problems -- full closed-loop collaboration.
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**Agent Directory** -- tool identity and behavior contract
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- `[P1]` D12: `agent/brief.md` exists
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- `[P1]` D13: `agent/rules/` has trigger.md, workflow.md, writeback.md
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- `[P1]` D17: agent/rules/*.md have YAML frontmatter (name, description)
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- `[P1]` D18: agent/skills/*.md have YAML frontmatter (name, description)
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- `[P2]` D14: `agent/skills/` directory + `skills` subcommand
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**Response Structure** -- inline context on every call
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- `[P1]` R1: Every response includes `rules[]` (full content from agent/rules/)
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- `[P1]` R2: Every response includes `skills[]` (name + description + command)
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- `[P1]` R3: Every response includes `issue` (feedback guide)
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**Meta** -- project-level integration
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- `[P2]` M1: AGENTS.md at project root
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- `[P2]` M2: Optional MCP tool schema export
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- `[P2]` M3: CHANGELOG.md marks breaking changes
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**Feedback** -- built-in issue system
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- `[P2]` F1: `issue` subcommand (create/list/show)
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- `[P2]` F2: Structured submission with version/context/exit_code
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- `[P2]` F3: Categories: bug / requirement / suggestion / bad-output
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- `[P2]` F4: Issues stored locally, no external service dependency
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- `[P2]` F5: `issue list` / `issue show <id>` queryable
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- `[P2]` F6: Issues have status tracking (open/in-progress/resolved/closed)
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- `[P2]` F7: Issue JSON has all required fields (id, type, status, message, created_at, updated_at)
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- `[P2]` F8: All issues have status field
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## Examples
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### Example 1: JSON Output (Agent Mode)
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```bash
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$ mycli list
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{"result": [{"id": 1, "title": "Buy milk", "status": "todo"}], "rules": [...], "skills": [...], "issue": "..."}
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```
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### Example 2: Structured Error
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```json
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{
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"error": true,
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"code": "AUTH_EXPIRED",
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"message": "Access token expired 2 hours ago",
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"suggestion": "Run 'mycli auth refresh' to get a new token"
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}
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```
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### Example 3: Exit Code Table
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```
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0 success 10 auth failed 20 resource not found
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1 general error 11 permission denied 30 conflict/precondition
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2 param/usage error
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```
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## Quick Implementation Checklist
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Implement by layer -- each phase gets you the next certification level.
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**Phase 1: Agent-Friendly (core)**
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1. Default output is JSON -- no `--json` flag needed
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2. Error handler: `{ error, code, message, suggestion }` to stderr
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3. Exit codes: 0 success, 2 param error, 1 general
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4. stdout = data only, stderr = logs only
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5. Missing param -> structured error (never interactive)
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6. `--yes` guard on destructive operations
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7. Guardrails: reject secrets, path traversal, shell metacharacters
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**Phase 2: Agent-Ready (+ recommended)**
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8. `--help` returns structured JSON (help, commands[], rules[], skills[])
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9. `--brief` reads and outputs `agent/brief.md` content
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10. `--human` flag switches to human-friendly format
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11. Reserved flags: --agent, --version, --dry-run, --quiet, --fields
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12. Exit codes: 20 not found, 30 conflict, 10 auth, 11 permission
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**Phase 3: Agent-Native (+ ecosystem)**
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13. Create `agent/` directory: `brief.md`, `rules/trigger.md`, `rules/workflow.md`, `rules/writeback.md`
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14. Every command response appends: rules[] + skills[] + issue
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15. `skills` subcommand: list all / show one with full content
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16. `issue` subcommand for feedback (create/list/show/close/transition)
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17. AGENTS.md at project root
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## Best Practices
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- Do: Default to JSON output so agents never need to add flags
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- Do: Include `suggestion` field in every error response
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- Do: Use the three-level certification model for incremental adoption
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- Do: Keep `agent/brief.md` to one paragraph for token efficiency
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- Don't: Enter interactive mode on errors -- always exit immediately
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- Don't: Change JSON schema or error codes within the same version
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- Don't: Put logs or progress info on stdout -- use stderr only
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- Don't: Accept unknown flags silently -- reject with exit code 2
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## Common Pitfalls
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- **Problem:** CLI outputs human-readable text by default, breaking agent parsing
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**Solution:** Make JSON the default output format; add `--human` flag for human-friendly mode
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- **Problem:** Errors reported in stdout with exit code 0
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**Solution:** Always exit non-zero on failure and write structured error JSON to stderr
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- **Problem:** CLI prompts for missing input interactively
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**Solution:** Return structured error with suggestion field and exit immediately
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## Related Skills
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- `@cli-best-practices` - General CLI design patterns (this skill focuses specifically on AI agent compatibility)
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## Additional Resources
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- [Agent CLI Spec Repository](https://github.com/ChaosRealmsAI/agent-cli-spec)
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