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Claude

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dzhng
claude

Use Claude Code as an independent `claude -p` subagent when the user explicitly asks for Claude, wants a second-agent opinion from Claude, or asks to delegate a well-scoped task to Claude. Supports selecting `--model` and thinking/effort level with defaults of `opus` and `high`.

Overview

Publisherdzhng
Repositoryskills
Skill nameclaude
Stars
1.1K
Forks
66
Bundled files
Instructions only
LicenseMIT
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 dzhng on GitHub. Read the source before you install it.

Installation

Install the Claude 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/dzhng/skills/tree/main/skills/engineering/claude
  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/dzhng/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/engineering/claude .claude/skills/claude
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Claude 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 Claude 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 Claude 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.

Claude

Claude Code is an independent agent on PATH (claude), sharing this working tree and already authenticated. Use it as a second opinion or delegated worker, not as ground truth: verify its claims, own any changes, and keep its task contract narrow.

Defaults

Use claude -p --model opus --effort high unless the user or task calls for a different model or thinking level.

  • Model: accept aliases such as opus, sonnet, or fable, or a full model name. Default opus.
  • Thinking level: pass Claude Code's --effort flag. Valid levels are low, medium, high, xhigh, and max. Default high.
  • Cost guard: for exploratory asks, add --max-budget-usd <amount> when the user gives a budget or the task is likely to sprawl.

Example:

sh
claude -p --model opus --effort high "<prompt>"

Prompting Claude

Prompt Claude like an operator: compact, block-structured, and explicit about the artifact you need. A sharper contract beats higher effort.

  • One task per run. Split review, implementation, and docs into separate prompts.
  • Name repo skills when relevant. Claude can read repo files; say "follow write-docs for the doc" or "obey refactor-clean: no compatibility wrappers" instead of pasting those skills into the prompt.
  • Blocks, added only where useful:
    • <task> — the concrete job, context, and expected end state.
    • <output_contract> — exact response shape, highest-value first.
    • <default_follow_through> — take low-risk interpretations and continue; stop only when a missing detail changes correctness, safety, or an irreversible action.
    • <verification_loop> — inspect the result against requirements and revise before finalizing.
    • <grounding> — ground every claim in code or tool output; label inferences.
    • <action_safety> — keep the diff tightly scoped; no drive-by refactors.
  • Anti-patterns: vague prompts, mixed jobs, no output contract, asking it to "think harder" instead of tightening the task, and accepting its answer without checking the evidence.

Consultation

Use Claude for consultation when the user asks for Claude's view or when a second-agent read would materially reduce risk. Keep it read-oriented unless the user explicitly asks it to edit.

  1. Ask for evidence, not vibes: require file paths, observed behavior, command output, or labelled inferences.
  2. Prefer a read-only tool surface for reviews: --tools "Read,Grep,Glob,Bash" --permission-mode dontAsk.
  3. Triage every finding against the code before acting. Report what Claude flagged, what you accepted, and what you dismissed.

Implementation — explicit ask only

Delegate implementation to Claude only when the user names Claude for the task. Never hand it work on your own initiative, and never re-delegate follow-up work without a fresh ask.

  1. Slice the task sharply: goal, constraints, allowed scope, and verification. An underspecified task stays with you until a fresh agent could not misread it.
  2. Start from a clean tree or record the baseline commit so Claude's diff is separable from yours. Tell Claude to keep scratch files (probes, dumps, notes) out of the tree — its diff must contain only the deliverable.
  3. Choose permissions by what the task must do:
    • Read/review: --tools "Read,Grep,Glob,Bash" --permission-mode dontAsk.
    • Narrow edits: add edit tools and keep the prompt's allowed paths explicit.
    • Broad autonomous work: use --dangerously-skip-permissions only in a dedicated worktree, with a self-authored prompt and a mandatory diff review after.
  4. Capture the response with --output-format text by default. Use --output-format json only when the caller needs structured metadata.
  5. You own the result: inspect the diff, run the relevant tests, and only then report it. "Claude says it's done" is not done.

Liveness

claude -p is a networked, authenticated non-interactive run. If it fails, classify the failure before retrying.

  • Auth failure: do not repair credentials unless the user asks; report the blocker. If sandboxed claude -p says Not logged in, retry once with the required approval because local auth can depend on keychain/session access.
  • Sandbox/network failure: rerun with the required approval if the command is important to the task.
  • Long run: prefer --max-budget-usd or a narrower prompt before raising effort.
  • Workspace trust: -p skips the interactive trust dialog, so run it only from a repo or worktree you intentionally trust.

Rules

  • Use claude -p --model opus --effort high as the default invocation.
  • Let the user override model and effort in plain language; translate that to --model and --effort.
  • Keep Claude's writes out of the working tree unless delegation was explicit.
  • Do not touch the same working tree while Claude is making edits.
  • Treat Claude output as evidence to verify, not authority to relay.

Frequently asked questions

What does the Claude AI skill do?

Use Claude Code as an independent `claude -p` subagent when the user explicitly asks for Claude, wants a second-agent opinion from Claude, or asks to delegate a well-scoped task to Claude. Supports selecting `--model` and thinking/effort level with defaults of `opus` and `high`.

Why use Claude on TypingMind?

Because you install it once and use it with any model. Claude 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 Claude in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dzhng/skills/tree/main/skills/engineering/claude. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Claude?

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 Claude?

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

Is the Claude AI skill free?

Yes. It is published on GitHub by dzhng under the MIT 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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