Ripwire Reuse First logo

Ripwire Reuse First

OrganizationPopular
redhat-et
ripwire-reuse-first

About to write ONE symbol (even a 'quick' one-liner) or add a dependency: reuse before you reinvent. Finds the building block that already exists, the house pattern to imitate, the duplicate you'd recreate, a vendored dependency. An interface or whole feature → before-you-build. One --exemplar or --grep call at most.

Overview

Publisherredhat-et
Repositoryripwire
Skill nameripwire-reuse-first
Stars
2.2K
Forks
141
Bundled files
Instructions only
LicenseApache-2.0
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 redhat-et on GitHub. Read the source before you install it.

Installation

Install the Ripwire Reuse First 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/redhat-et/ripwire.git /tmp/ripwire
mkdir -p .claude/skills
cp -r /tmp/ripwire/skills/ripwire-reuse-first .claude/skills/ripwire-reuse-first
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ripwire Reuse First 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 Ripwire Reuse First 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 Ripwire Reuse First 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.

Reuse before you write

Routing — pick the right door: • Starting a whole FEATURE (multi-symbol, needs a plan / interface / sizing) → ripwire-before-you-build. • The cross-cutting map-before-you-read token discipline for any read → ripwire-orient (map-before-you-read.md). • Judging whether what you wrote got better or worse before "done" → ripwire-quality-bar. • Not sure which skill? → ripwire-router.

The cheapest, simplest, most readable code is the code you don't write. Before authoring anything non-trivial, spend one ripwire call (on PATH) to find what already exists.

Retrieve by ROLE, never by raw similarity

This is the load-bearing rule, not a style preference: similarity-retrieved "here's similar code, paste it" measurably HURTS — up to −15% Pass@1 (arXiv:2503.20589). What helps is API/type-signature context (+17-20%) and dependency-graph-structured retrieval (+6 EM; enabled 5/6 multi-file edits vs 0/6 without a graph). ripwire's shape — signatures + call-graph + role-ranked exemplars — is the validated retrieval shape; "find me a similar-looking snippet" is the anti-pattern to avoid, even though it's the tempting first instinct.

Before you write a function / class / util

  1. Find the building block firstripwire <dir> --for="<what you're about to build>" → ranked existing signatures (plus, when the code has them, the <lego> / <compose> HAS-A blocks — what a class already owns). If you can name the helper you suspect exists, query it verbatim (--for="parseByteSize") — --for auto-routes to name-exact retrieval and lands it at recall@1 ~99%. It also carries the quality lens (cx/ccx/in/churn/amp/tested) so you see which candidates are safe to extend. Often the thing exists — compose from it.
  2. Optionally get a style/shape exemplar — if the retrieval result leaves a local-idiom question, ripwire <dir> --exemplar=fn|method|class|struct|iface|var --legend=compact (a kind) or --exemplar="<task in words>" (the top match's kind is inferred) returns the repo's single best-in-class instance of that shape: highest fan-in, lowest cognitive complexity, tested=1 where possible — selected by ROLE, not text similarity. It returns the full body under <bodies>. Copy its shape (structure, error handling, naming), not its text — it is a model to imitate, not a relevant-code search.
  3. Find candidates by behavior/shaperipwire <dir> --grep="<a word from the behavior>" --legend=compact, or a NARROWED graph query: --graph-query='and(file(all,"<area>"),kind(all,fn))'. (Bare kind(all,fn) is ranked by importance + capped at --top-k, so it's safe — but narrowing by file()/name()/callers() finds the relevant building block, not just the globally-important one.) → implementations to reuse or extend.
  4. Don't duplicateripwire <dir> --clones --legend=compact → if your intended body matches an existing one, call it instead. Rule of Three: extract a shared helper on the third occurrence, not the second — and prefer a little duplication over the wrong abstraction (a helper you bend with boolean flags is worse than two honest copies).

Before you add a dependency

  1. Is it already here?ripwire <dir> --external-surface --legend=compact (what the tree already depends on; the default is a 100-row window with the sh builtins dropped and counted as builtins_excluded=--include-builtins keeps them) and --uses=NAME (the import role). Reuse an in-tree dependency before adding a new one: a new dep is build weight, supply-chain surface, and one more thing every reader must learn — it has to earn its place.
  2. Before adding/vendoring a NEW dependency, map the candidate repo firstripwire <git-url> (https:// or git@; shallow-clones to a temp cache dir, then maps it like any local tree — --refetch forces a fresh clone instead of reusing the cached one). Run --report/--hotspots/--deps on it before you commit to the dependency: is it actually small and well-factored, or a god-file waiting to become your problem?

When you do write

  • Match the local idiomripwire <dir> --for="<the area>" shows the surrounding naming, layout, and error-handling style. Mirror it so your diff reads like the file, not like a graft (cache-friendly for the reader and for KV-cache reuse).
  • Less code wins on every metric at once: lower complexity, fewer call edges, smaller blast radius, less to test, less to read. When in doubt, delete a parameter before adding one.

Then hand off to ripwire-quality-bar before you call it done.

Frequently asked questions

What does the Ripwire Reuse First AI skill do?

About to write ONE symbol (even a 'quick' one-liner) or add a dependency: reuse before you reinvent. Finds the building block that already exists, the house pattern to imitate, the duplicate you'd recreate, a vendored dependency. An interface or whole feature → before-you-build. One --exemplar or --grep call at most.

Why use Ripwire Reuse First on TypingMind?

Because you install it once and use it with any model. Ripwire Reuse First 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 Ripwire Reuse First in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/redhat-et/ripwire/tree/main/skills/ripwire-reuse-first. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ripwire Reuse First?

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 Ripwire Reuse First?

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

Is the Ripwire Reuse First AI skill free?

Yes. It is published on GitHub by redhat-et under the Apache-2.0 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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