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Prp Meta Skill

CommunityPopular
Wirasm
prp-meta-skill

Authors, refactors, and consolidates Agent Skills PRP-style. Use when the user wants to "create a skill", "write a new skill", "trim a SKILL.md", "split a skill into references", "consolidate workflow skills", "replace duplicated skill logic with composition", redesign a skill workflow, or invokes /prp-meta-skill.

Overview

PublisherWirasm
Repositoryprp
Skill nameprp-meta-skill
Stars
2.2K
Forks
607
Bundled files
7
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.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Wirasm on GitHub. Read the source before you install it.

Installation

Install the Prp Meta Skill 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.
  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/Wirasm/prp.git /tmp/prp
mkdir -p .claude/skills
cp -r /tmp/prp/plugins/prp-core/skills/prp-meta-skill .claude/skills/prp-meta-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prp Meta Skill 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 Prp Meta Skill 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 Prp Meta Skill 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.

PRP Meta-Skill — Author, Refactor & Consolidate Skills

A PRP-style runbook for three jobs:

  • Create a new skill from scratch (or from an existing command/prompt).
  • Refactor an existing skill — split detail into references/, move output formats into templates/, and trim SKILL.md to a lean spine of pointers.
  • Consolidate overlapping workflows — trace their contracts, choose one composition owner, reuse specialist skills, and retire duplicate paths without losing outcomes or human gates.

This skill is built the way it teaches: a lean body that defers detail to references/. Follow that example.

Prescribe the craft, not the content

This skill is opinionated about how to build a skill well and deliberately agnostic about what any given skill — or the artifact it produces — should contain.

  • Prescribe (firm, universal): progressive disclosure, third-person trigger-rich descriptions, imperative body, no-duplication, lean body, every reference wired, deliberate invocation control, validation that fits. See references/skill-standards.md.
  • Do NOT prescribe (per-project, the author's call): the sections a plan / PRD / report should contain, what a project's template looks like, which phases exist, the domain vocabulary. There is no canonical output shape — guide the author to a good decision, never hand them a fixed one.

Restrictions are not rigidity: be strict on the craft so the author stays free on the content.

The PRP lens — applied to the skill type

Classify the skill first (workflow / artifact-generator / knowledge-reference / tool-wrapper — see references/skill-standards.md → Skill types), then apply only the principles that fit:

  1. Context is King — give the agent ALL the context it needs (patterns, gotchas, schemas, examples) via whichever source fits: inline, bundled and disclosed on demand, pointed to by file path or URL, or gathered from the user at runtime. Curate it — don't dump it. (references/skill-standards.md → Context sources.)
  2. Validation that fitsworkflow skills ship verifiable gates, and prefer an external, authoritative check (exit code, file presence) over the agent's own "done" sentinel. A knowledge/reference skill has nothing to validate — don't bolt a loop onto it.
  3. Information dense — real trigger phrases, real examples, real file:line. No filler, no restating what the model already knows.
  4. Progressive success — ship the smallest complete SKILL.md that triggers correctly first, validate, then enrich with references. Don't build all the references before the spine works.

Step 0 — Pick the mode

  • Creating a new skill → follow references/creating-skills.md.
  • Refactoring / trimming an existing skill → follow references/refactoring-skills.md.
  • Consolidating / redesigning overlapping workflow skills → follow references/consolidating-workflows.md.
  • All modes obey the same rules (frontmatter spec, writing style, progressive disclosure, no-duplication, invocation control) → read references/skill-standards.md first.

If the argument is blank, ask which mode and what the skill/target is. Do not guess.

Create — quick spine (full detail in references/creating-skills.md)

  1. Gather context (PRP): collect the concrete phrases that should trigger the skill, the task it performs, the gotchas, and existing patterns to mirror.
  2. Plan resources: what repeats → a scripts/ script; what informs thinking → a references/ doc; what is reused in the output → a templates//assets/ file.
  3. Scaffold: copy templates/SKILL.template.md into .claude/skills/<name>/SKILL.md; create references/, templates/ only as needed.
  4. Write the spine: third-person trigger-rich description; imperative, lean body; push detail to references.
  5. Validate & iterate (references/validation.md): checklist → skill-reviewer → trigger test.

Refactor — quick spine (full detail in references/refactoring-skills.md)

  1. Inventory the target SKILL.md. Classify each block: spine (keep) vs extractable (output-format templates, schemas, long worked examples, exhaustive pattern catalogs, troubleshooting, edge-case lists).
  2. Extract verbatim into the TARGET skill's own references/ (or templates/ for output formats). Do not reword anything that affects behavior.
  3. Replace with a pointer. For always-needed content (e.g. an output format the agent must always follow), add a mandatory-read instruction: "Before producing output, read templates/<x>.md." For sometimes-needed content, a lazy pointer ("For edge cases, see references/<x>.md").
  4. Behavior-preservation check — the trimmed skill + references must drive the SAME process and SAME output as before. Nothing lost, nothing duplicated.
  5. Validate (references/validation.md).

Consolidate — quick spine (full detail in references/consolidating-workflows.md)

  1. Trace before editing. Inventory inputs, callers, artifacts, side effects, publication, gates, correction paths, and every terminal outcome.
  2. Define the invariant. Preserve the user-visible outcome and authoritative checks; distinguish intentional redesign from accidental regression.
  3. Assign ownership. Give each phase, durable artifact, human decision, and retry loop one maintainer. Compose specialist skills by name instead of copying their instructions.
  4. Design handoffs. Prefer semantic identifiers and complete durable artifacts over remembered filenames, parallel indexes, or lossy summaries. State when to preserve a context and when independence matters.
  5. Remove the old path. Update every caller and public surface, regenerate derived targets, then exercise the new workflow end-to-end.

The single biggest refactor risk: moving an always-needed output format into a lazily-loaded reference, so the agent forgets to read it and the output silently changes. Always pair such extractions with a mandatory-read instruction. See references/refactoring-skills.md.

Resources

  • references/skill-standards.md — frontmatter spec, progressive disclosure, writing style, no-duplication, invocation control, cross-provider notes
  • references/creating-skills.md — full PRP-style create runbook
  • references/refactoring-skills.md — full split-and-trim runbook with a before/after example
  • references/consolidating-workflows.md — workflow trace, ownership, context, publication, correction-loop, and retirement guidance
  • references/validation.md — validation gates, checklist, skill-reviewer, trigger test
  • templates/SKILL.template.md — lean SKILL.md skeleton
  • templates/reference.template.md — skeleton for an extracted reference/template file

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Prp Meta Skill AI skill do?

Authors, refactors, and consolidates Agent Skills PRP-style. Use when the user wants to "create a skill", "write a new skill", "trim a SKILL.md", "split a skill into references", "consolidate workflow skills", "replace duplicated skill logic with composition", redesign a skill workflow, or invokes /prp-meta-skill.

Why use Prp Meta Skill on TypingMind?

Because you install it once and use it with any model. Prp Meta Skill 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 Prp Meta Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Wirasm/prp/tree/development/plugins/prp-core/skills/prp-meta-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Prp Meta Skill?

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 Prp Meta Skill?

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

Is the Prp Meta Skill AI skill free?

Yes. It is published on GitHub by Wirasm 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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