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Ripwire Find Bug

OrganizationPopular
redhat-et
ripwire-find-bug

You have a SYMPTOM — crash, exception, wrong output, failing test, error string — and don't know which code is responsible. Ranks candidates; a stack trace or sanitizer output maps onto frames innermost-first; 'it worked yesterday' / 'my last edit broke it' → --situ. One clear hit plus one focused read is enough.

Overview

Publisherredhat-et
Repositoryripwire
Skill nameripwire-find-bug
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 Find Bug 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-find-bug .claude/skills/ripwire-find-bug
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ripwire Find Bug 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 Find Bug 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 Find Bug 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.

Find the bug with ripwire

Nearest neighbours: • You already know the symbol and just want to understand it → ripwire-navigate. • You want the blast radius / tests for a change (not to find a bug) → ripwire-change-check. • Repo-wide quality once-over, no specific symptom → ripwire-fresh-eyes. • "Why don't I see feature X?" and nothing looks broken — the code may be built but compiled/flagged OFF, not buggy: ripwire <dir> --flags[=SUBSTR] --legend=compact (--flip=NAME for the blast radius of turning one ON) — lives in ripwire-fresh-eyes, worth a look before you go hunting for a bug that isn't one.

<dir> = repo root. Calls are warm after the first parse — chain freely. Pick the branch that matches what you already know; each converges on the same evidence trail (relevance × maintenance pain × blast radius), so you can escalate between them.

Evidence-sufficiency stop: escalate only while the responsible code is still ambiguous. If --for ranks one file/symbol clearly and a focused source read explains the symptom with a minimal fix, stop retrieval and implement/validate it. Do not automatically add --hotspots, --impact, another skill, or a whole-file read after the defect is already proven; those answer different questions and can cost more than the original localization. Resume the ladder only when the source contradicts the candidate, several candidates remain plausible, or the change's blast radius is itself part of the task.

Branch A — "I have a symptom, no idea where it lives"

  1. Symptom searchripwire <dir> --for="<symptom in plain words>" <sigs> ranked by relevance — signatures + doc-comments closest to the symptom. The in= reuse count and cx= complexity are inline; prefer high-cx, high-in matches — complex, widely-called code fails in more ways. If the bundle says weak="1", reformulate — split camelCase terms, add synonyms from the domain, or quote an exact path/symbol from the issue — before trusting the ranking below it.
  2. If several candidates remain, maintenance hotspotsripwire <dir> --hotspots --legend=compact <hotspots> ranked by score = churn × ccx; top= names the worst function per file. Bugs cluster in high-score files — cross with step 1: a symbol in both lists is your prime suspect.
  3. If the symptom is broad, blast radius of each remaining candidateripwire <dir> --impact=SYM --legend=compact for the top 2–3 from step 1. <impact of="SYM" defs="D" reaches="N"> lists everything that reaches SYM. A large reaches count is consistent with a symptom that appears in many places — that's the root, not a downstream effect.
  4. Read narrowly — start with the top symbol/body or the smallest source range that can confirm or reject it. Use the hotspot intersection only when step 1 did not already isolate a defensible candidate.

Branch B — "I suspect a subsystem — narrow it"

  1. Symptom-to-coderipwire <dir> --for="<symptom>" → note the p= (file paths) of the top 5. Which directories recur? That's your first narrowing.
  2. Hotspots in those directoriesripwire <dir> --hotspots --legend=compact → files that are relevant to the symptom AND high churn+complexity are the most likely bug homes.
  3. Find the exact emit siteripwire <dir> --grep="ERROR_STRING" --legend=compact (literal + enclosing symbol) or --regex="pattern". Add --grep-context=N (or --grep-before=N/--grep-after=N) for ripgrep-style lines of source around each hit — often enough to confirm the bug without a follow-up --expand. The enclosing symbol (in=) is ground truth — now --callers=SYM to trace up one level to the true root.

Branch C — "I changed X and now something's broken" (regression)

  1. Situational awareness on the changeripwire <dir> --situ=fileA.cpp,fileB.h (or bare --situ to read from git diff). Emits, in one pass:
    • blast radius — everything that transitively reaches the changed symbols
    • tests to run now (--affected under the hood)
    • co-change partners NOT in your diff — files that historically move together (hidden coupling). This is the Shotgun Surgery check: did the change land everywhere it usually has to?
  2. Who calls the broken symbolripwire <dir> --callers=SYM --legend=compact → each recorded caller (a floor — counts_floor=) is a candidate for an unexpected side-effect.
  3. Co-change historyripwire <dir> --cochange=fileA.cpp --legend=compact → partners ranked by deg (fraction of commits). A surprising="1" partner has no #include link — pure behavioural coupling, the non-obvious suspect.
  4. Read the functions that appear in BOTH the blast radius and the co-change list first.

Branch D — "I have a stack trace / sanitizer report / compiler error"

You have the failing artifact's TEXT (a Python traceback, an ASan/UBSan report, a node/js stack, a clang/gcc diagnostic) — don't hand-translate its frames into queries one by one. Pipe it straight in:

  1. Map the trace onto symbolsripwire <dir> --from-trace=FILE --legend=compact (or --from-trace=- to read the trace from stdin, e.g. pytest ... 2>&1 | ripwire <dir> --from-trace=- --legend=compact). Table-driven frame extraction (python / asan / node / compiler / generic), ranked innermost-first over the frames that resolve to your indexed code. Out-of-corpus frames (stdlib, vendored deps) are listed and counted, never ranked.
  2. Read rank 1 first — the innermost="1" suspect is the crash/throw site; its FULL body is emitted inline, the other suspects as signatures. skipped= tells you how many frames fell outside every root.
  3. Compose the budget--from-trace=FILE --token-budget=N fits the bundle to N tokens for a tight context window. Unparseable input refuses loudly (never a misleading empty map).
  4. Or skip the run-read-paste loop entirelyripwire <dir> --run-trace="make -j" --legend=compact RUNS the build/test command itself (sh -c, your user, your environment — the make trust model, no sandbox) and, on a non-zero exit, serves the SAME from-trace bundle for the captured output plus a token-frugal <lines view="relevant"> cut of the error/frame-shaped output lines. Exit 0 gets a minimal success record and no bundle — nothing failed, nothing to map. The command's own exit code is always disclosed (<run exit=>), and --run-timeout=SECONDS caps a hanging command (default 600 s, reported timed_out="1" honestly, never as an empty success).

Output

Report the branch you took, then: ranked candidate symbol(s) with name, file:line, and why (from --for); their hotspot score if present; their blast-radius count (--impact reaches=); the error site's enclosing symbol if --grep found it; and any surprising="1" co-change partner (branch C). Recommend the top 1–2 to inspect first, with the evidence trail.

Honesty: the call graph gives structure; --slice=SYM:VAR (add --slice-flow=back|fwd|both for the transitive reaching-definition walk) gives intra-procedural, name-based data flow — reach for it on a wrong-value symptom instead of re-reading the whole function by eye. Its own legend discloses the limits that matter here, so trust that over this line: statement/line-granular, flow-sensitive reaching definitions inside one function for C-family/Python and source-ordered for JS/Go/Java/Rust (the root's reach= says which; each use row's rd= lists its reaching def lines), no alias analysis, block scopes separated (a shadowed name's rows carry b=, the declaration each binds to; pp="1" marks a build-dependent #ifdef row), and it stops at the function boundary (chain --callers/--impact for the inter-procedural half). For use-after-move / taint / null / type bugs that cross that boundary you still need the compiler — use these results to focus where to look, not as proof. A high-amb symbol can be a dispatch hub, not the bug.

Found it? Pin the gotcha with ripwire <dir> --note-add="SYM_or_path: what actually went wrong" — the next agent (or you, next session) gets it automatically the next time --for/--expand surfaces that symbol.

Frequently asked questions

What does the Ripwire Find Bug AI skill do?

You have a SYMPTOM — crash, exception, wrong output, failing test, error string — and don't know which code is responsible. Ranks candidates; a stack trace or sanitizer output maps onto frames innermost-first; 'it worked yesterday' / 'my last edit broke it' → --situ. One clear hit plus one focused read is enough.

Why use Ripwire Find Bug on TypingMind?

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

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

Which AI models can use Ripwire Find Bug?

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 Find Bug?

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

Is the Ripwire Find Bug 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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