Research Codebase logo

Research Codebase

Organization
modiqo
research-codebase

Document and explain the codebase as-is using parallel sub-agents. Use when the user asks to research, explore, map, document, or understand the codebase — including questions like 'how does X work', 'where is Y implemented', 'show me the architecture', 'what does this module do', or any request for codebase-level understanding. Also use when the user says /research_codebase. Even if the user's question seems simple, if it requires reading multiple files or understanding cross-component interactions, use this skill.

Overview

Publishermodiqo
Repositoryskillspec
Skill nameresearch-codebase
Stars
705
Forks
43
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 modiqo on GitHub. Read the source before you install it.

Installation

Install the Research Codebase 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.
    https://github.com/modiqo/skillspec/tree/main/.claude/skills/research-codebase/source
  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/modiqo/skillspec.git /tmp/skillspec
mkdir -p .claude/skills
cp -r /tmp/skillspec/.claude/skills/research-codebase/source .claude/skills/research-codebase
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Codebase 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 Research Codebase 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 Research Codebase 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.

Research Codebase

Conduct comprehensive read-only research across the codebase by spawning parallel sub-agents and synthesizing their findings into a structured document.

Your only job: document what exists

  • DO NOT suggest improvements or changes unless explicitly asked
  • DO NOT perform root cause analysis unless explicitly asked
  • DO NOT propose enhancements, critique the implementation, or recommend refactoring
  • ONLY describe what exists, where it exists, how it works, and how components interact
  • You are creating a technical map of the existing system

When invoked without a query

Respond with:

I'm ready to research the codebase. Please provide your research question or area of interest, and I'll analyze it thoroughly by exploring relevant components and connections.

Then wait for the user's research query.

Research workflow

1. Read directly mentioned files first

If the user mentions specific files (docs, configs, TOML, JSON), read them FULLY before doing anything else. Use the Read tool without limit/offset parameters. Read these yourself in the main context — this ensures full context before decomposing the research.

2. Decompose the research question

  • Break the query into composable research areas
  • Think deeply about underlying patterns, connections, and architectural implications
  • Identify specific components, patterns, or concepts to investigate
  • Create a research plan with tasks to track subtasks
  • Pay special attention to Rust-specific structures: crates, modules, traits, impls, derive macros, feature flags

3. Spawn parallel sub-agents

Create multiple agents to research different aspects concurrently. Use the specialized agents:

Codebase research:

  • codebase-locator — find WHERE files and components live
    • Look for Cargo.toml, lib.rs, main.rs, mod.rs to understand crate/module structure
    • Search *.rs files, build.rs, .cargo/config.toml
  • codebase-analyzer — understand HOW specific code works (without critiquing it)
    • Focus on trait definitions, impl blocks, type aliases, error types, module hierarchies
  • codebase-pattern-finder — find examples of existing patterns (without evaluating them)
    • Look for builder pattern, newtype pattern, From/Into impls, error handling, async patterns

Web research (when external context helps):

  • web-search-researcher — external docs, crate docs, RFCs, blog posts
    • Spawn proactively when the topic involves external crates, Rust language features, or ecosystem patterns
    • Instruct them to return LINKS with findings — include those links in the final report

All agents are documentarians, not critics. They describe what exists without suggesting improvements.

Agent usage strategy:

  • Start with locator agents to find what exists
  • Then use analyzer agents on the most promising findings
  • Run multiple agents in parallel when searching for different things
  • Each agent knows its job — tell it what you're looking for, not how to search
  • Remind agents they are documenting, not evaluating

4. Synthesize findings

Wait for ALL sub-agents to complete before proceeding. Then:

  • Compile all results
  • Connect findings across crates, modules, and components
  • Include specific file paths and line numbers
  • Highlight patterns, connections, and architectural decisions
  • Answer the user's questions with concrete evidence
  • Document trait relationships, generic type parameters, and lifetime annotations where relevant

5. Generate research document

Structure the output as:

markdown
# Research: [Topic]

**Date**: [Current date]
**Git Commit**: [Current commit hash]
**Branch**: [Current branch]

## Research Question
[Original query]

## Summary
[High-level documentation answering the question by describing what exists]

## Crate & Module Structure
[Workspace layout, crate dependencies, module tree relevant to the research]

## Detailed Findings

### [Component/Area 1]
- Description of what exists (file.rs:line)
- How it connects to other components
- Current implementation details (without evaluation)
- Key traits, types, and impls involved

### [Component/Area 2]
...

## Code References
- `path/to/file.rs:123` — Description
- `another/module/mod.rs:45-67` — Description

## Architecture Documentation
[Current patterns, conventions, and design implementations]
- Error handling approach (thiserror, anyhow, custom Result types)
- Async runtime usage (tokio, async-std, etc.)
- Serialization patterns (serde derives, custom impls)
- Feature flag organization

## Open Questions
[Areas that need further investigation]

6. Add GitHub permalinks (if applicable)

  • Check if on main branch or if commit is pushed: git branch --show-current and git status
  • If on main/master or pushed, generate permalinks:
    • Get repo info: gh repo view --json owner,name
    • Format: https://github.com/{owner}/{repo}/blob/{commit}/{file}#L{line}
  • Replace local file references with permalinks

7. Present findings

  • Present a concise summary to the user
  • Include key file references for easy navigation
  • Ask if they have follow-up questions

8. Handle follow-ups

  • Append to the same research document under ## Follow-up Research [timestamp]
  • Spawn new sub-agents as needed

Important notes

  • Always use parallel agents to maximize efficiency and minimize context usage
  • Always run fresh research — never rely solely on existing documents
  • Focus on concrete file paths and line numbers
  • Research documents should be self-contained
  • Each sub-agent prompt should be focused on read-only documentation
  • Document cross-component connections
  • Link to GitHub when possible for permanent references
  • Keep the main agent focused on synthesis, not deep file reading
  • Sub-agents should document examples and usage patterns as they exist
  • Read mentioned files FULLY before spawning sub-tasks
  • Rust-specific:
    • Explore Cargo.toml and Cargo.lock for dependency graphs
    • Map out workspace members
    • Document pub visibility boundaries
    • Note #[cfg(...)] conditional compilation and feature gates
    • Track use imports for module dependency flow
    • Identify derive macros and proc macros in use
    • Document unsafe blocks and their safety invariants
    • Note FFI boundaries (extern "C", #[no_mangle])

Frequently asked questions

What does the Research Codebase AI skill do?

Document and explain the codebase as-is using parallel sub-agents. Use when the user asks to research, explore, map, document, or understand the codebase — including questions like 'how does X work', 'where is Y implemented', 'show me the architecture', 'what does this module do', or any request for codebase-level understanding. Also use when the user says /research_codebase. Even if the user's question seems simple, if it requires reading multiple files or understanding cross-component interactions, use this skill.

Why use Research Codebase on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/modiqo/skillspec/tree/main/.claude/skills/research-codebase/source. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Research Codebase?

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 Research Codebase?

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

Is the Research Codebase AI skill free?

Yes. It is published on GitHub by modiqo 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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