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Ripwire Layers

OrganizationPopular
redhat-et
ripwire-layers

Architecture HEALTH and ENFORCEMENT — 'is this a dependency mess / does the UI reach into the database / enforce module boundaries in CI?': cycles, the godfile, propagation cost (how far a touch ripples), --arch rules with a baseline gate. Overview without gating → orient. One pass answers it.

Overview

Publisherredhat-et
Repositoryripwire
Skill nameripwire-layers
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 Layers 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-layers .claude/skills/ripwire-layers
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ripwire Layers 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 Layers 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 Layers 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.

Layers with ripwire

Nearest neighbours: • You want the file→file dependency graph itself (not a health read) → --deps is step 2 below, or go straight to it if you already know you want godfiles/cycles. • Reviewing YOUR OWN diff for coupling risk, not a repo-wide sweep → ripwire-change-check. • Unfamiliar subsystem, general risk sweep (not architecture-specific) → ripwire-fresh-eyes.

Trigger: you want a factual picture of architectural health before a large refactor, or you're reviewing whether the codebase is trending toward or away from clean layering.

<dir> = repo root. Every metric below has a NUMBER → THRESHOLD → ACTION — don't stop at reporting it.

  1. Layering rules (if a rules file exists) — ripwire <dir> --arch=rules.txt Output: <arch> with violations listed by layer pair and file. Exit 2 = NEW violations found (CI gate). If no rules file exists yet, this step is the prompt to write one (grammar: layer NAME = substr…, deny FROM -> TO, allow FROM -> TO; # comments). Action: every violation is a concrete edge to either fix (remove the dependency) or explicitly allow (document why it's intentional) — don't leave it silently baselined forever.

    Adopting rules on a codebase with existing debt — the baseline workflow:

    • ripwire <dir> --arch=rules.txt --baseline — accept every CURRENT violation as known debt: writes a .ripwire_arch_baseline sidecar in the CWD (commit it), exits 0.
    • From then on, plain --arch=rules.txt suppresses baselined violations and exits 2 only on NEW ones — the gate stops the bleeding without demanding an up-front cleanup.
    • --arch=rules.txt --baseline-update — deliberately accept new debt by merging current violations into the sidecar (exit 0). Use sparingly, in its own reviewed commit.

    --arch=rules.txt also emits <metrics propagation_cost="X.XXX"> — the DSM (design-structure-matrix) transitive-closure density: the fraction of the file-dep graph reachable from an average file (MacCormack; a validated coupling form, computed here as a directory-level estimate from name-based deps). Direction: lower is better — 0 means files are mostly isolated from each other's transitive reach, 1 means touching any file risks rippling through the whole tree. Action at a high reading (no fixed universal threshold — compare against this same repo's own history/other modules, or treat >0.3 as worth a look): a high propagation cost is a change-amplification TAX — every edit here is more likely to have knock-on effects. Don't just report the number; find which directories are driving it (the <m path=... ca= ce=> per-module rows) and formalize a boundary between them — a layer/deny rule in rules.txt that would have caught the coupling.

  2. Dependency healthripwire <dir> --deps --legend=compact Output: <deps> with <health> metrics:

    • acd (average component dependency) — lower is better
    • nccd (normalized CCD) — < 0.25 is healthy
    • shape — "horizontal" (layered, good) vs "vertical" (coupled, risk) Then <godfiles> ranked by afferent (dependents) — each godfile is an implicit layer boundary that hasn't been formalized. afferent/cycles are validated defect predictors; nccd (Lakos) and the --arch Martin Ca/Ce/I/A/D I/A/D/zone= block are design heuristics — mechanistically plausible, widely implemented, but no independent outcome-based study has validated them. Trust the god-file/cycle read hardest; treat nccd/D as descriptive, not proof. Action: a file in the top-3 by afferent is doing double duty as a de-facto layer boundary with no rule enforcing it — that's the concrete next step, not just a note: add it as a named layer in rules.txt and write the deny rules that keep new dependents out of its internals. A cycle in --report is worse than a high-afferent file — break it before formalizing anything downstream of it.
  3. Module clusteringripwire <dir> --communities --legend=compact Output: <communities modules="N"> clusters with dominant directory and lead symbols. <bridge edges="N"> shows where clusters are tightly coupled across module boundaries. High bridge counts between non-adjacent modules are the layering violations --arch catches. Action: a high bridge count between two clusters that AREN'T adjacent in your intended layering is the specific violation to gate — turn it into a deny FROM -> TO rule rather than leaving it as an observation; a high bridge count between clusters that ARE meant to talk to each other is fine and doesn't need a rule.

  4. Mermaid diagram (optional, for visual review) — ripwire <dir> --mermaid Output: a flowchart LR Mermaid snippet with module nodes and inter-module call counts. Paste at mermaid.live to render. Edges labeled with high counts are the hot coupling seams — same action as step 3: a heavy edge crossing an intended boundary becomes a deny rule.

Output

Health summary: shape=, nccd=, propagation_cost=, cycle count (from --report), top 3 god-files, and any --arch violations. Classify overall health: healthy / at-risk / needs restructuring — and for anything "at-risk" or worse, name the specific boundary to formalize (which layer, which deny rule) rather than stopping at the classification.

Frequently asked questions

What does the Ripwire Layers AI skill do?

Architecture HEALTH and ENFORCEMENT — 'is this a dependency mess / does the UI reach into the database / enforce module boundaries in CI?': cycles, the godfile, propagation cost (how far a touch ripples), --arch rules with a baseline gate. Overview without gating → orient. One pass answers it.

Why use Ripwire Layers on TypingMind?

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

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

Which AI models can use Ripwire Layers?

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 Layers?

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

Is the Ripwire Layers 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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