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Cli Review

Community
Dark-Alex-17
cli-review

Review the command-line surface contract of a change - exit codes, stdout/stderr channel discipline, help text, non-interactive operation, signal/cleanup behavior, and config precedence. Load when a diff touches argument-parser definitions, the main/entrypoint of a binary, or subcommand modules. Findings fold into the standard code-review severity taxonomy. Grants read-only filesystem access for tracing entrypoints, parsers, and exit paths.

Overview

PublisherDark-Alex-17
Repositorycoyote
Skill namecli-review
Stars
56
Forks
2
Bundled files
Instructions only
LicenseAGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Dark-Alex-17 on GitHub. Read the source before you install it.

Installation

Install the Cli Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Dark-Alex-17/coyote/tree/main/assets/skills/cli-review
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Dark-Alex-17/coyote.git /tmp/coyote
mkdir -p .claude/skills
cp -r /tmp/coyote/assets/skills/cli-review .claude/skills/cli-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cli Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cli Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cli Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

You are reviewing a command-line interface. The generic correctness checklist asks "does this command work when a human runs it?"; you ask "does this command keep its contract with the scripts, pipes, and CI jobs that run it unattended?" A CLI's real callers are rarely humans at a terminal — they are shell scripts branching on $?, pipelines parsing stdout, and cron jobs with no TTY. Most CLI breakage in the wild is not wrong logic; it is a success that exits 1, a diagnostic that corrupts a pipe, or a prompt that hangs a CI job forever.

When to load this skill

The diff touches ANY of: argument-parser definitions (flag/option/subcommand declarations), the main/entrypoint of a binary, or subcommand modules. If the diff is library internals behind an unchanged command surface — unload; this checklist has nothing for you.

Marker semantics

Every checklist item below carries a severity emoji AND a [convention] or [correctness] marker; both ride in the finding title so downstream tooling can act on them mechanically. [convention] findings are rigor-foldable (the orchestrator may lower them under a relaxed quality bar) and rejectable — but ONLY with cited evidence: a repo convention at file:line, or a recorded plan decision. [correctness] is reserved for contract breaks; those findings are neither foldable nor rejectable.

Linters and mechanized checks

The review orchestrator runs mechanized checks (shell linters, help-text validators); your CONTEXT may already include their output — do not re-derive it. Spend your prose on what linters cannot reach: exit-code semantics on each error path, which stream a message lands on, whether a prompt has an escape hatch. If the repo plausibly warrants a linter config it lacks (shell scripts but no shell linter config), emit a 🟢 [convention] finding naming the gap.

The checklist

Severities below are the production bar. Each item is a context-sensitive question, not an absolute — trace the actual exit paths and output calls before flagging.

1. 🔴 [correctness] Error paths exiting 0 / success paths exiting non-zero

Trace every exit path the diff adds or modifies: does each failure propagate a non-zero exit code all the way out of main, and does success exit 0? The classic bugs: an error that is printed and then falls through to a normal return; a caught exception that logs and continues; a match arm that swallows a Result. Scripts branch on $? — an inverted exit code silently corrupts every automation built on this command. This is the exit-code contract; it is never foldable and never rejectable.

2. 🟡 [convention] Diagnostics on stdout corrupting pipeable output

If the command's stdout is (or plausibly will be) piped or parsed — it prints data, JSON, lists, paths — then progress messages, warnings, and diagnostics on stdout corrupt the stream. Do new prints route diagnostics to stderr and reserve stdout for payload? A purely interactive command with no parseable output can be exempt — say so when you rely on that. Which level and format diagnostics use is logging-discipline's question; yours is which stream they land on.

3. 🟢 [convention] Missing or wrong --help for new flags

Does every flag, option, and subcommand the diff adds appear in help output with an accurate description? Check the parser declarations: a flag with no help string, a stale description contradicting new behavior, or a new subcommand missing from the top-level help listing. Help text is the CLI's only discoverable documentation.

4. 🟡 [convention] Interactive prompt with no non-interactive escape

Does the diff add a prompt (confirmation, password, selection)? Then there must be a non-interactive path: a flag (--yes/--force-style), an environment variable, or reading from stdin — and ideally the prompt should detect a missing TTY rather than hang. A prompt with no escape hatch deadlocks CI and cron callers. Check what escape idiom the repo's existing prompts use and whether the new one matches.

5. 🟡 [convention] No signal/cleanup handling for long-running commands with temp state

If the diff adds a long-running command that creates temp files, lockfiles, partial output, or spawns children: what happens on Ctrl-C or SIGTERM? Look for signal handling, cleanup guards (drop/defer/finally/trap), or an idiom the repo already uses. Orphaned locks and half-written files are the finding. Short-lived commands with no temp state are exempt — this item is scoped to commands that hold state long enough for interruption to be a realistic event.

6. 🟢 [convention] Config precedence violated or undocumented

If the command reads configuration from more than one source, the conventional precedence is flag > environment variable > config file. Does the diff's resolution order honor that — and honor whatever order the repo has already established? A new setting that reads only the file when its siblings accept a flag override, or a precedence order documented nowhere, is the finding. Cite the repo's existing resolution code when flagging a deviation.

Ground-truth discipline

  • READ the full path from error site to process exit — exit-code bugs live in the propagation, not the error site. fs_grep for the exit/return conventions the entrypoint uses.
  • Check sibling subcommands for the established idioms (stderr usage, prompt escape flags, cleanup guards) — a new subcommand skipping the house pattern is the strongest form of evidence.
  • Do not flag hypothetical piping of a command that is documented interactive-only; note the assumption instead.

What this skill does NOT check

  • Whether argument or path inputs are exploitable (injection, traversal, secrets on the command line) → security-review.
  • Whether the state a command mutates is changed idempotently and atomically under reruns → transactional-integrity.
  • Log levels, formats, and message register of diagnostics → logging-discipline (this skill only checks which stream they use).
  • Metrics and alerting for operationally significant commands → observability-review.

Frequently asked questions

What does the Cli Review AI skill do?

Review the command-line surface contract of a change - exit codes, stdout/stderr channel discipline, help text, non-interactive operation, signal/cleanup behavior, and config precedence. Load when a diff touches argument-parser definitions, the main/entrypoint of a binary, or subcommand modules. Findings fold into the standard code-review severity taxonomy. Grants read-only filesystem access for tracing entrypoints, parsers, and exit paths.

Why use Cli Review on TypingMind?

Because you install it once and use it with any model. Cli Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cli Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dark-Alex-17/coyote/tree/main/assets/skills/cli-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cli Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cli Review?

As many as you like. As long as a model supports skills, you can use Cli Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cli Review AI skill free?

Yes. It is published on GitHub by Dark-Alex-17 under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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