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Ripwire Before You Build

OrganizationPopular
redhat-et
ripwire-before-you-build

Starting a FEATURE (multi-symbol work): the plan, the API boundary, the scope — how big does this change get — from the codebase's real structure. Implementing an EXISTING interface? --lego=Iface lists its contract and current implementors. ONE standalone symbol → reuse-first. A small feature with an obvious home needs none of this.

Overview

Publisherredhat-et
Repositoryripwire
Skill nameripwire-before-you-build
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 Before You Build 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-before-you-build .claude/skills/ripwire-before-you-build
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ripwire Before You Build 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 Before You Build 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 Before You Build 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.

Before you build — with ripwire

Routing: • Writing ONE symbol (fn/class/helper) and want to reuse rather than reinvent → ripwire-reuse-first. • You've already written the change and want the pre-submit check → ripwire-change-check. • You want to judge whether the code you wrote got better/worse → ripwire-quality-bar. • Restructuring EXISTING code (not new work) → ripwire-fresh-eyes. • Not sure which skill? → ripwire-router.

<dir> = repo root. FEATURE/APPROACH = your task in 2–4 plain words; SYM = a representative symbol. Run only the sections your question needs — they share the same building blocks.

Common first move — recall + reusable blocks

Almost every section starts here; do it once. One-call shortcut: ripwire <dir> --pack-task="FEATURE" --legend=compact assembles the recall + --for ranking + top bodies + caller signatures + notes + tests_to_run into ONE bundle under one token budget — reach for it first instead of firing the calls below one at a time; drop to the individual calls only when you need a specific section's full output.

  • Recall prior decisionsripwire <dir> --recall="FEATURE" → full bodies of the most relevant markdown docs (planning/design notes, READMEs) ranked by relevance. If a doc already covers this, read it fully — the decision may be made. Look for explicit "rejected" / "future work" language.
  • Reusable building blocks — the one-call write-mode bundleripwire <dir> --for="FEATURE"<sigs> ranked by task-relevance, each carrying the quality lens in the same call: cx/ccx (complexity), in/amp (reuse count / change-amplification), churn (recent-commits), clone="1" (duplicated), tested="1", plus the inline <doc> block. That's read-context-and-quality-signal in ONE call — you learn what to reuse AND what's fragile (high-churn, high-amp, cloned, or untested) before you write a line, not after. High-in symbols are already widely composed — prefer extending them over reimplementing; treat a high-churn/high-amp one as something to touch carefully and test around.
  • The shape to imitate for each new symbolripwire <dir> --exemplar=fn|method|class|struct|iface|var --legend=compact or --exemplar="<the sub-task in words>" (top match's kind inferred) → the repo's single best-in-class instance of that shape — cognitive complexity must clear an eligibility ceiling first (a blob can never win no matter how reused/tested), then among eligible candidates it's ranked tested=1 first, highest fan-in second, lowest cognitive-cx as the final tie-break — full body under <bodies>. Run it per new symbol the feature adds so each one copies a proven shape (structure, error handling, naming) instead of being invented from memory. (Deep-dive on exemplar-by-ROLE → ripwire-reuse-first.)
  • Implementing against an interface?ripwire <dir> --lego=I --legend=compact (or --lego=file:I to disambiguate a same-named type) → the interface's method contract (the exact signatures you must satisfy) plus every existing implementor, own-language only. Read one existing impl as the template so your new one matches the house pattern (method order, error handling, registration) instead of guessing the shape from the interface alone. The single highest-value call when the thing you're building has to satisfy an I.

Feasibility spike — "is this approach even viable?"

After the recall + --for above:

  1. If highly-relevant symbols already exist with in > 0, the approach is partially implemented — build on it. If --recall shows it was decided against, stop.
  2. Integration seamsripwire <dir> --seams --legend=compact<seams> (untested cross-module edges). If your approach must connect two modules with no test-covered seam between them, that's an integration cost.
  3. Coupling costripwire <dir> --deps --legend=compact (skim <godfiles> + shape=). Adding a dependency on a god-file adds coupling cost for every future change — flag it. → Verdict: "build on existing" / "greenfield but seams exist" / "needs new seam, flag risk".

Plan — "give me the ordered implementation steps"

  1. Recall + --for (above) — check whether any existing symbol already solves part of the problem. Low in= = an underused candidate to extend or replace; high in= = load-bearing, extend cautiously.
  2. Integration seamsripwire <dir> --seams --legend=compact. Each seam is a wiring point; your plan should name which seam(s) the new code wires through, and add a test at each new seam it creates.
  3. Impact of anything you'll modifyripwire <dir> --impact=SYM --legend=compact. Large reaches= → plan a staged rollout or a compatibility shim. → Plan: ordered steps (recall → design → wire seams → write tests → implement), each naming the specific file+symbol to touch, the blast radius of any modification, and its test gate (from --affected). Flag any step that creates a new seam without a test.

Interface — "what should the boundary / API look like?"

  1. Neighborhood of the subsystemripwire <dir> --around=SYM --legend=compact --metrics [--around-depth=2] → the ego graph with ranks + call edges. --metrics is what adds the in= fan-in annotation (bare --around emits none); high-in= symbols are already acting as the de-facto API surface.
  2. The seam it lives onripwire <dir> --seams --legend=compact. If your subsystem straddles a <seam from="X" to="Y">, that's where the interface belongs.
  3. Who currently crosses the boundaryripwire <dir> --callers=SYM --legend=compact for the top 2–3 in the ego graph. Callers from outside the subsystem's directory are the external clients the interface must serve.
  4. What to hideripwire <dir> --deps --legend=compact, skim instab=. High-instability internal files (leaves) stay behind the interface; stable (low-instability) files may be safe to expose. → Proposal: the 3–5 high-fan-in, called-from-outside symbols that form the natural API surface, the seam they live on, and the internal symbols to hide. Write it in terms of callers' needs, not internals.

Sizing rubric — "how big is this change?"

  1. Blast radiusripwire <dir> --impact=SYM --legend=compactreaches="N" is the blast-radius symbol count (count distinct p= files for the file count); defs="D">1 means overloads, each a separate blast root.
  2. Affected testsripwire <dir> --affected=fileA.cpp,fileB.h --legend=compact (the files defining SYM, from --impact). tests="0" = no test cover — flag it.
  3. Caller-stack depthripwire <dir> --callers=SYM --legend=compact, then spot-check --callers=<top-caller> one level up. A 2-hop caller graph is a tactical change; 5+ hops is architectural.
  4. Hidden couplingripwire <dir> --cochange=fileA.cpp --legend=compact; surprising="1" partners must be verified manually even if not in the blast radius. → Classify: local (≤5 blast, tests exist) / moderate (5–20) / wide (>20 or no tests).

Honesty

ripwire maps structure, not data flow; seams/impact are name-based (dynamic dispatch/callbacks/macros can be missing). Compose from what exists; note what's genuinely missing rather than reinventing it.

Frequently asked questions

What does the Ripwire Before You Build AI skill do?

Starting a FEATURE (multi-symbol work): the plan, the API boundary, the scope — how big does this change get — from the codebase's real structure. Implementing an EXISTING interface? --lego=Iface lists its contract and current implementors. ONE standalone symbol → reuse-first. A small feature with an obvious home needs none of this.

Why use Ripwire Before You Build on TypingMind?

Because you install it once and use it with any model. Ripwire Before You Build 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 Before You Build in TypingMind?

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

Which AI models can use Ripwire Before You Build?

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 Before You Build?

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

Is the Ripwire Before You Build 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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