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ai-native-cli

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.

45

Quality

47%

Does it follow best practices?

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tessl review fix ./plugins/antigravity-awesome-skills/skills/ai-native-cli/SKILL.md

The canonical home for this skill is ai-native-cli in popey/claude-code-skills

SKILL.md
Quality
Evals
Security

Quality

Content

27%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill is a comprehensive reference document for the Agent-Friendly CLI Spec, but it suffers from significant verbosity and repetition — the same concepts appear in the overview, layer model, certification requirements, checklist, best practices, and pitfalls sections. It provides good structural organization of the 98 rules but lacks executable implementation code and validation steps. The entire spec is packed into one file with no bundle support, making it a poor fit for progressive disclosure.

Suggestions

Eliminate redundant sections: merge 'Best Practices', 'Common Pitfalls', 'Core Philosophy', and 'Overview' into the rule listings themselves, or remove them entirely since they restate what the rules already say.

Split the three certification levels into separate bundle files (e.g., level1-core.md, level2-recommended.md, level3-ecosystem.md) and keep SKILL.md as a concise overview with references.

Add executable implementation snippets — e.g., a Python error handler function, a JSON output wrapper, a guardrail validation function — rather than just showing CLI invocation examples.

Add validation steps: provide a compliance-checking script or checklist command (e.g., 'Run these 5 test commands to verify Level 1 compliance') to create feedback loops during implementation.

DimensionReasoningScore

Conciseness

The skill is extremely verbose at ~300+ lines, explaining concepts Claude already understands (what JSON output is, what exit codes mean, what stderr is for). The 'Core Philosophy', 'Overview', 'When to Use', 'Common Pitfalls', 'Best Practices', and 'Limitations' sections are largely redundant with the rule listings. The same information is repeated across the Layer Model explanation, the Certification Requirements, and the Quick Implementation Checklist.

1 / 3

Actionability

The skill provides concrete rules with specific codes, exit values, and flag names, plus a few bash/JSON examples. However, it lacks executable implementation code — there are no copy-paste-ready code snippets showing how to actually implement the error handler, JSON output wrapper, or guardrail checks. The examples show CLI invocation output but not the implementation.

2 / 3

Workflow Clarity

The Quick Implementation Checklist provides a clear three-phase sequence, and the certification levels create a logical progression. However, there are no validation checkpoints — no way to verify compliance after each phase, no test commands to confirm rules are met, and no feedback loop for catching implementation errors during the build process.

2 / 3

Progressive Disclosure

Despite being a 98-rule spec, everything is crammed into a single monolithic file with no bundle files to offload detail. The full rule listings for all three certification levels are inline, along with reserved flag tables, examples, best practices, pitfalls, and checklists. This content would benefit enormously from splitting into separate files per certification level or topic area.

1 / 3

Total

6

/

12

Passed

Description

67%Scale 1-3

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong in specificity and distinctiveness, clearly carving out a unique niche around building agent-friendly CLI tools with concrete capability areas listed. However, it lacks an explicit 'Use when...' clause, which caps completeness at 2, and could benefit from more natural trigger terms that users would actually say when needing this skill.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when building or reviewing command-line tools intended for AI agent use, or when designing CLI interfaces with structured output.'

Include additional natural trigger terms users might say, such as 'command line', 'terminal commands', 'machine-readable CLI', 'agentic tools', or 'shell scripts for automation'.

DimensionReasoningScore

Specificity

Lists multiple specific concrete areas: structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description. These are concrete, well-defined capabilities.

3 / 3

Completeness

Clearly answers 'what' (design spec with 98 rules for building CLI tools for AI agents, covering specific areas), but lacks an explicit 'Use when...' clause or equivalent trigger guidance for when Claude should select this skill.

2 / 3

Trigger Term Quality

Includes relevant terms like 'CLI tools', 'AI agents', 'JSON output', 'error handling', 'exit codes', but misses common user phrasings like 'command line', 'terminal', 'shell commands', 'machine-readable output', or 'agentic tooling'.

2 / 3

Distinctiveness Conflict Risk

The niche of 'CLI tools designed for AI agent consumption' is highly specific and unlikely to conflict with general CLI, general AI, or general coding skills. The combination of agent-safe CLI design is distinctive.

3 / 3

Total

10

/

12

Passed

Validation

90%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
popey/claude-code-skills
Reviewed

Table of Contents

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