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Prp Codebase Question

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Wirasm
prp-codebase-question

Research codebase questions using parallel agents - documents what exists, not what should change. Use when the user asks how the codebase works, where something lives, or invokes /prp-codebase-question.

Overview

PublisherWirasm
Repositoryprp
Skill nameprp-codebase-question
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 Codebase Question 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-codebase-question .claude/skills/prp-codebase-question
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prp Codebase Question 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 Codebase Question 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 Codebase Question 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.

Codebase Research

Input: $ARGUMENTS


Your Mission

Answer codebase questions thoroughly by spawning parallel specialized agents, synthesizing their findings, and producing a research document.

Core Philosophy: Document what IS, not what SHOULD BE. You are a technical cartographer.

Golden Rule: Every claim must have a file:line reference. No speculation, no suggestions, no critique.


CRITICAL: Documentarian Only

  • DO NOT suggest improvements or changes
  • DO NOT perform root cause analysis unless explicitly asked
  • DO NOT propose future enhancements
  • DO NOT critique implementations or identify problems
  • DO NOT recommend refactoring or optimization
  • ONLY describe what exists, where it exists, how it works, and how components interact

Phase 1: PARSE - Understand the Query

1.1 Read Mentioned Files

If the user mentions specific files, read them FULLY first (no limit/offset) before any decomposition.

1.2 Classify the Query

TypeIndicatorsAgent Focus
Where"where is", "find", "locate"prp-core:codebase-explorer primary
How"how does", "trace", "flow"prp-core:codebase-analyst primary
What"what is", "explain", "describe"Both agents in parallel
Pattern"how do we", "convention", "examples"prp-core:codebase-explorer primary
External"docs", "best practice", "API"Add prp-core:web-researcher

1.3 Determine Scope

  • Identify specific components, patterns, or concepts to investigate
  • Note any --web flag for external research
  • Note any --follow-up flag for appending to existing research

PHASE_1_CHECKPOINT:

  • Mentioned files read in full
  • Query type classified
  • Research scope identified
  • Flags parsed (--web, --follow-up)

Phase 2: DECOMPOSE - Break into Research Areas

2.1 Create Research Plan

Break the query into 2-5 composable research areas:

RESEARCH QUESTION: {user's question}

AREAS:
1. {Area} → Agent: {which agent}
2. {Area} → Agent: {which agent}
3. {Area} → Agent: {which agent}

2.2 Agent Selection

AgentUse When
prp-core:codebase-explorerFinding WHERE code lives, locating files, extracting patterns, discovering conventions
prp-core:codebase-analystUnderstanding HOW code works, tracing data flow, mapping integration points
prp-core:web-researcherOnly when --web flag is set or user explicitly asks for external docs

Strategy:

  1. Start with prp-core:codebase-explorer to find what exists
  2. Then use prp-core:codebase-analyst on the most relevant findings to trace how they work
  3. Run agents in parallel when they're searching for different areas

PHASE_2_CHECKPOINT:

  • Query decomposed into 2-5 research areas
  • Agent assigned to each area
  • Parallel vs sequential execution planned

Phase 3: EXPLORE - Spawn Parallel Agents

3.1 Launch Codebase Agents

Launch agents in parallel using multiple Task tool calls in a single message.

For each research area, use the appropriate agent:

prp-core:codebase-explorer:

Find all code relevant to: {research area}

LOCATE:
1. {Specific files/components to find}
2. {Patterns or conventions to extract}
3. {Related test files and configuration}

Categorize findings by purpose. Return ACTUAL code snippets with file:line references.
Remember: Document what exists, no suggestions or improvements.

prp-core:codebase-analyst:

Analyze the implementation of: {research area}

TRACE:
1. {Data flow to trace}
2. {Integration points to document}
3. {Contracts between components}

Document what exists with precise file:line references. No suggestions.

3.2 Launch Web Research (if --web or explicitly requested)

prp-core:web-researcher:

Research external documentation for: {topic}

FIND:
1. {Specific documentation needed}
2. {API references or patterns}

Return findings with direct links and citations.

3.3 Wait for All Agents

IMPORTANT: Wait for ALL agents to complete before proceeding.

PHASE_3_CHECKPOINT:

  • All agents launched (parallel where possible)
  • All agents completed
  • Results collected from each agent

Phase 4: SYNTHESIZE - Merge Findings

4.1 Compile Results

  • Prioritize live codebase findings as primary source of truth
  • Connect findings across different components
  • Include specific file:line references throughout
  • Document patterns, connections, and architectural decisions as they exist

4.2 Answer the Question

Map findings back to the user's original question:

Question AspectFindingEvidence
{aspect 1}{what was found}file.ts:123
{aspect 2}{what was found}file.ts:456

4.3 Identify Gaps

Note any areas that couldn't be fully documented:

  • {Area that needs further investigation}
  • {Question that remains open}

PHASE_4_CHECKPOINT:

  • All agent results synthesized
  • Findings connected across components
  • Original question answered with evidence
  • Gaps identified

Phase 5: DOCUMENT - Generate Research File

5.1 Gather Metadata

bash
date -u +"%Y-%m-%dT%H:%M:%SZ"
git rev-parse --short HEAD
git branch --show-current
basename $(git rev-parse --show-toplevel)

5.2 Create Research Directory

bash
# --- PRP store resolver (canonical; keep byte-identical across skills) ---
# Adopt the store that already records this root; mint a key only when none does.
_gd="$(git rev-parse --path-format=absolute --git-common-dir 2>/dev/null)"
case "$_gd" in */.git) _root="${_gd%/.git}" ;; "") _root="$PWD" ;; *) _root="$_gd" ;; esac
_root="$(cd "$_root" && pwd -P)"
_name="$(basename "$_root" | tr '[:upper:]' '[:lower:]' | tr -cs 'a-z0-9' '-' | sed 's/^-*//;s/-*$//')"
_home="${PRP_HOME:-$HOME/.prp}"
_hit="$(grep -lsF "\"path\": \"$_root\"" "$_home"/*/project.json 2>/dev/null | head -1)"
PRP_DIR="${_hit%/project.json}"
[ -n "$PRP_DIR" ] || PRP_DIR="$_home/${_name:-project}-$(printf %s "$_root" | git hash-object --stdin | cut -c1-8)"
mkdir -p "$PRP_DIR"; [ -f "$PRP_DIR/project.json" ] || printf '{"path": "%s", "name": "%s"}\n' "$_root" "${_name:-project}" > "$PRP_DIR/project.json"
mkdir -p "$PRP_DIR/research"

5.3 Determine Filename

If --follow-up: Append to existing research file instead of creating new one.

If new research:

Path: $PRP_DIR/research/{YYYY-MM-DD}-{kebab-case-topic}.md

Examples:

  • 2025-01-08-authentication-flow.md
  • 2025-01-15-database-migration-patterns.md

5.4 Write Research Document

markdown
---
date: {ISO timestamp with timezone}
git_commit: {short hash}
branch: {branch name}
repository: {repo name}
topic: "{User's Question/Topic}"
tags: [research, codebase, {relevant-component-names}]
status: complete
last_updated: {YYYY-MM-DD}
---

# Research: {User's Question/Topic}

**Date**: {ISO timestamp}
**Git Commit**: {short hash}
**Branch**: {branch name}
**Repository**: {repo name}

## Research Question

{Original user query}

## Summary

{High-level documentation of what was found, answering the question by describing what exists}

## Detailed Findings

### {Component/Area 1}

- Description of what exists (`file.ts:123`)
- How it connects to other components
- Current implementation details

### {Component/Area 2}

...

## Code References

| File | Lines | Description |
|------|-------|-------------|
| `path/to/file.ts` | 123-145 | {What's there} |
| `another/file.ts` | 45-67 | {What's there} |

## Architecture Documentation

{Current patterns, conventions, and design implementations found}

## Open Questions

- {Areas that need further investigation}

5.5 Add GitHub Permalinks (if applicable)

bash
# Check if on main or pushed
git branch --show-current
gh repo view --json owner,name -q '"\(.owner.login)/\(.name)"'

If on main/pushed, replace local file references with: https://github.com/{owner}/{repo}/blob/{commit}/{file}#L{line}

5.6 Handle Follow-ups

If --follow-up flag and existing research file:

  1. Read the existing research file
  2. Update frontmatter: last_updated and add last_updated_note
  3. Append new section: ## Follow-up Research {timestamp}
  4. Spawn new agents as needed
  5. Save updated document

PHASE_5_CHECKPOINT:

  • Metadata gathered
  • Research file created (or existing file updated for follow-up)
  • All sections filled with evidence-based content
  • GitHub permalinks added (if applicable)
  • No placeholder values remain

Phase 6: OUTPUT - Present to User

markdown
## Research Complete

**Question**: {original question}
**Document**: `{expanded absolute path to $PRP_DIR/research/{filename}.md}`

### Summary

{2-3 sentence answer to the question}

### Key Findings

- **{Finding 1}**: {brief} (`file.ts:123`)
- **{Finding 2}**: {brief} (`file.ts:456`)
- **{Finding 3}**: {brief} (`file.ts:789`)

### Architecture

{1-2 sentence description of relevant architecture}

### Open Questions

- {Any unanswered aspects}

### Follow-up

To dig deeper: `/prp-codebase-question --follow-up {topic}`
To include external docs: `/prp-codebase-question --web {topic}`

Usage Examples

bash
# Basic codebase question
/prp-codebase-question how does authentication work

# Include external documentation
/prp-codebase-question --web how does the PRP runner execute commands

# Follow up on previous research
/prp-codebase-question --follow-up what error handling patterns exist in the runner

# Locate and document a specific area
/prp-codebase-question where are all the command templates and how are they structured

Critical Reminders

  1. Document, don't evaluate. Describe what IS, never what SHOULD BE.

  2. Evidence required. Every claim needs a file:line reference.

  3. Agents are parallel. Launch multiple agents simultaneously when researching different areas.

  4. Wait for completion. Never synthesize until ALL agents have returned.

  5. Read first. If the user mentions files, read them fully before spawning agents.

  6. No placeholders. Every field in the research document must have real values.

  7. Codebase is truth. Live code always overrides documentation or assumptions.


Success Criteria

  • QUESTION_ANSWERED: User's question addressed with concrete evidence
  • AGENTS_USED: Specialized agents spawned for each research area
  • EVIDENCE_COMPLETE: Every finding has file:line references
  • DOCUMENT_CREATED: Research file saved at {expanded absolute path to $PRP_DIR/research/}
  • NO_OPINIONS: Document describes what exists, not what should change
  • PERMALINKS_ADDED: GitHub links included when possible

Frequently asked questions

What does the Prp Codebase Question AI skill do?

Research codebase questions using parallel agents - documents what exists, not what should change. Use when the user asks how the codebase works, where something lives, or invokes /prp-codebase-question.

Why use Prp Codebase Question on TypingMind?

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

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

Which AI models can use Prp Codebase Question?

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 Codebase Question?

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

Is the Prp Codebase Question 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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