cli-anything: wrap any CLI into an agent skill

SkillAI & models

Generate or refine agent-usable CLIs for existing software/codebases using the CLI-Anything methodology. Use when the user wants to turn a GUI app, desktop tool, repository, SDK, or web/API surface into a structured CLI for agents; when adapting CLI-Anything into OpenClaw workflows; or when packagin

Available today. Use it from your connected AI after setup.

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Then ask your AI: use the cli-anything: wrap any CLI into an agent skill skill

What this skill tells your AI

The instructions your AI receives, as published by aliyun/alibabacloud-rds-openapi-mcp-server in skill/cli-anything/SKILL.md and read by ahel’s review.

Turn any command-line tool into something an AI agent can call reliably and parse. The output is a thin, well-documented wrapper contract — never a reimplementation of the tool. Use the real tool; document how to drive it.

When to use

You have a CLI (your own, or a third-party binary) and you want an agent to invoke it predictably, read structured results, and recover from errors. This skill produces a SKILL.md wrapper that encodes that contract.

Step 1 — Introspect the CLI (do not guess)

Enumerate the real surface before writing anything:

  • Run <tool> --help and <tool> <subcommand> --help for each subcommand.
  • Capture: subcommands, flags (required vs optional), positional args, exit codes, and the output format (human text vs structured).
  • If --help is thin, read the source or man page. Every claim in the wrapper must trace to observed behavior.

Step 2 — Define the output contract

  • Prefer a --json machine-readable mode for every command an agent will call. Document the exact JSON keys the agent should read (one shape per command).
  • If the tool has no JSON mode, say so honestly and document what to parse from stdout (and which token carries the answer). Do not pretend output is structured when it is not.
  • Human output stays the default; structured output is the agent path.

Step 3 — Define the error contract

  • Map exit codes to meaning (0 = success; document each non-zero class).
  • On failure, surface a parseable signal ({"error": "...", "code": N} if the tool supports it, otherwise the captured stderr) — never a bare stack trace.
  • Fail loud and parseable. Never half-succeed silently.

Step 4 — Write the SKILL.md wrapper

Standard structure so an agent can discover and drive the tool:

  • Frontmatter: name, description (include natural trigger phrases).
  • Commands — each command: purpose, exact invocation, args, and the result shape it returns.
  • Examples — 2-4 real invocations with expected output.
  • Errors — the exit-code/error contract from Step 3.
  • Notes — auth, side effects, idempotency, network egress, prerequisites.

Step 5 — Verify against reality

  • Run each documented command once; confirm the real output matches what the wrapper claims.
  • Confirm any --json shape parses.
  • A wrapper that drifts from reality is worse than none — re-introspect and fix.

Principles

  • Use the real tool; never reimplement its logic in the wrapper.
  • Deterministic and structured beats clever.
  • The wrapper is a contract: introspect, structure, document, verify.

Worked example

skills/coco-cli/SKILL.md is a wrapper produced with this pattern over the cocosuperintelligence command — note how it documents the (human-only) output contract honestly rather than inventing a JSON mode the tool lacks.

Signals

GitHub stars
55
Forks
20
Last commit
Aug 2026

Others that do the same job

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Catalog kind
skill
Gateway key
cli-anything
Source
github.com/aliyun/alibabacloud-rds-openapi-mcp-server