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Prp Debug

CommunityPopular
Wirasm
prp-debug

Diagnoses a bug, error, stack trace, regression, or unexplained behavior and publishes the evidence-backed root cause to GitHub. Use when the user says "debug this", "find the root cause", provides a bug report or stack trace, asks to investigate a GitHub issue, or invokes /prp-debug. Defaults to commenting on the matching open issue or creating one when none exists.

Overview

PublisherWirasm
Repositoryprp
Skill nameprp-debug
Stars
2.2K
Forks
607
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Prp Debug 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-debug .claude/skills/prp-debug
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prp Debug 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 Debug 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 Debug 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 Debug

Diagnose broken current behavior through the prp-core:root-cause-analyzer, then make the finding durable in the appropriate GitHub issue. Do not implement the fix.

Input: $ARGUMENTS (if absent, use the conversation).

1. Resolve the report and publication target

Read repository guidance and establish the complete report: symptom, expected behavior, environment, reproduction, logs, and any supplied stack trace.

When the input points to a GitHub issue, read its body, relevant comments, linked issues, duplicates, pull requests, and attachments before diagnosing. Treat the body as historical context, not verified causality.

When no issue is specified, search open issues in the current GitHub repository using the symptom, distinctive errors, affected feature, and likely underlying behavior. Inspect candidate bodies and discussion; title similarity alone is not a duplicate.

  • One clear match → use it.
  • Several plausible matches → ask the user which issue should own the finding.
  • No clear match → plan to create an issue after diagnosis.

If GitHub access or the repository cannot be resolved, complete the diagnosis but stop before publication and state what access is missing. Honor --no-publish or an equivalent explicit request without treating publication as a failure.

2. Run the root-cause analysis

Spawn prp-core:root-cause-analyzer with the original report, complete tracker context, repository path, and any decisive runtime evidence already available. Do not give it a preferred cause or fix.

Require:

  • reproduction at the cheapest authoritative boundary when reasonably possible;
  • competing hypotheses and focused falsification;
  • a causal chain from observed symptom to the smallest fixable cause;
  • the violated invariant and its smallest responsible fix boundary: the existing or missing primitive that should own it, not merely the smallest patch;
  • rejected alternatives and explicit uncertainty;
  • a regression check that fails before the fix and passes after it.

Do not publish a UNRESOLVED diagnosis as fact. Report the missing evidence and the next investigation step instead. Publish a CONDITIONAL diagnosis only with its condition prominent.

3. Reconcile duplicates with the diagnosis

For an unspecified issue, re-check the open candidates against the diagnosed behavior and cause. Reuse an issue only when it represents the same underlying problem, not merely a similar symptom.

If the diagnosis reveals that a supplied issue is a duplicate, comment on the issue the user supplied with the evidence and link the canonical open issue. Do not silently move the conversation elsewhere.

4. Publish the durable artifact

Unless publication was disabled or the current harness says its driver owns external publication:

  • Existing issue: add a concise comment containing the corrected problem statement, reproduction evidence, root cause or explicit condition, causal chain, fix boundary, regression proof, and remaining uncertainty.
  • No matching issue: create one with a concise problem-oriented title that states the observed impact and the same evidence as its body. Include expected and actual behavior and a reproducible procedure.
  • Wrong assumptions in an existing body: correct them in the comment. Rewrite the body only when the user explicitly requests --rewrite-body or its natural-language equivalent; preserve still-useful original report details.

Do not create a separate local RCA document. The GitHub issue or comment is the artifact.

Read the created or updated issue back to verify the content and capture its URL. Report the diagnosis status, one-sentence cause, publication action, issue URL, fix boundary, regression proof, and any remaining uncertainty.

Frequently asked questions

What does the Prp Debug AI skill do?

Diagnoses a bug, error, stack trace, regression, or unexplained behavior and publishes the evidence-backed root cause to GitHub. Use when the user says "debug this", "find the root cause", provides a bug report or stack trace, asks to investigate a GitHub issue, or invokes /prp-debug. Defaults to commenting on the matching open issue or creating one when none exists.

Why use Prp Debug on TypingMind?

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

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

Which AI models can use Prp Debug?

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 Debug?

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

Is the Prp Debug 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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