Agent-Friendly CLI Spec v0.1 workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Agent-Friendly CLI Spec v0.1 workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills/skills/ai-native-cli from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Agent-Friendly CLI Spec v0.1 When building or modifying CLI tools, follow these rules to make them safe and reliable for AI agents to use.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Philosophy, Layer Model, How It Works, Certification Requirements, Quick Implementation Checklist, Common Pitfalls.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
Use when building a new CLI tool that AI agents will invoke
Use when retrofitting an existing CLI to be agent-friendly
Use when designing command-line interfaces for automation pipelines
Use when auditing a CLI tool's compliance with agent-safety standards
Use when the request clearly matches the imported source intent: 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.
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
Imported Workflow Notes
Imported: Overview
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.
Imported: Core Philosophy
Agent-first -- default output is JSON; human-friendly is opt-in via --human
Agent is untrusted -- validate all input at the same level as a public API
Fail-Closed -- when validation logic itself errors, deny by default
Verifiable -- every rule is written so it can be automatically checked
Examples
Example 1: Ask for the upstream workflow directly
Use @ai-native-cli-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @ai-native-cli-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @ai-native-cli-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @ai-native-cli-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
{"error":true,"code":"AUTH_EXPIRED","message":"Access token expired 2 hours ago","suggestion":"Run 'mycli auth refresh' to get a new token"}
Example 3: Exit Code Table
0 success 10 auth failed 20 resource not found
1 general error 11 permission denied 30 conflict/precondition
2 param/usage error
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Do: Default to JSON output so agents never need to add flags
Do: Include suggestion field in every error response
Do: Use the three-level certification model for incremental adoption
Do: Keep agent/brief.md to one paragraph for token efficiency
Don't: Enter interactive mode on errors -- always exit immediately
Don't: Change JSON schema or error codes within the same version
Don't: Put logs or progress info on stdout -- use stderr only
Imported Operating Notes
Imported: Best Practices
Do: Default to JSON output so agents never need to add flags
Do: Include suggestion field in every error response
Do: Use the three-level certification model for incremental adoption
Do: Keep agent/brief.md to one paragraph for token efficiency
Don't: Enter interactive mode on errors -- always exit immediately
Don't: Change JSON schema or error codes within the same version
Don't: Put logs or progress info on stdout -- use stderr only
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/ai-native-cli, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
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)
Default (no flag) -- JSON to stdout. Agent never needs to add a flag.
--human -- human-friendly format (colors, tables, progress bars)
--agent -- explicit JSON mode (useful when env/config overrides default)
Step 2: agent/ Directory Convention
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.md
Step 3: Four Levels of Self-Description
--brief (business card, injected into agent config)
Every Command Response (always-on context: data + rules + skills + issue)
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.
Agent Directory -- tool identity and behavior contract
[P1] D12: agent/brief.md exists
[P1] D13: agent/rules/ has trigger.md, workflow.md, writeback.md
[P1] D17: agent/rules/*.md have YAML frontmatter (name, description)
[P1] D18: agent/skills/*.md have YAML frontmatter (name, description)
Phase 2: Agent-Ready (+ recommended)
8. --help returns structured JSON (help, commands[], rules[], skills[])
9. --brief reads and outputs agent/brief.md content
10. --human flag switches to human-friendly format
11. Reserved flags: --agent, --version, --dry-run, --quiet, --fields
12. Exit codes: 20 not found, 30 conflict, 10 auth, 11 permission
Phase 3: Agent-Native (+ ecosystem)
13. Create agent/ directory: brief.md, rules/trigger.md, rules/workflow.md, rules/writeback.md
14. Every command response appends: rules[] + skills[] + issue
15. skills subcommand: list all / show one with full content
16. issue subcommand for feedback (create/list/show/close/transition)
17. AGENTS.md at project root
Imported: Common Pitfalls
Problem: CLI outputs human-readable text by default, breaking agent parsing
Solution: Make JSON the default output format; add --human flag for human-friendly mode
Problem: Errors reported in stdout with exit code 0
Solution: Always exit non-zero on failure and write structured error JSON to stderr
Problem: CLI prompts for missing input interactively
Solution: Return structured error with suggestion field and exit immediately
Imported: Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
