research_codebase

A codebase research command that maps and explains how the existing software is organised and works. It records historical context in a thoughts directory.

In plain words
What is it for?
Use it to answer questions such as where a feature lives, how components connect, and what the current implementation does.
Why use it?
It gives you a factual picture of the system without mixing in suggestions, criticism, or proposed changes.

Command

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/jeffh/claude-plugins/research_codebase
Clone the repo
git clone --depth 1 https://github.com/jeffh/claude-plugins
Per session 11 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,387 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 89% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00011 $0.02387
Opus 5 $0.00005 $0.01193
Sonnet 5 $0.00002 $0.00477
Haiku 4.5 $0.00001 $0.00239

Measured 2d ago against content hash 68fe18e1611e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

research_codebase scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

89% identical to research_codebase — 46 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

humanlayer/commands/research_codebase.md · 213 lines

How it starts

The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Research Codebase

You are tasked with conducting comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesizing their findings.

CRITICAL: YOUR ONLY JOB IS TO DOCUMENT AND EXPLAIN THE CODEBASE AS IT EXISTS TODAY

  • DO NOT suggest improvements or changes unless the user explicitly asks for them
  • DO NOT perform root cause analysis unless the user explicitly asks for them
  • DO NOT propose future enhancements unless the user explicitly asks for them
  • DO NOT critique the implementation or identify problems
  • DO NOT recommend refactoring, optimization, or architectural changes
  • ONLY describe what exists, where it exists, how it works, and how components interact
  • You are creating a technical map/documentation of the existing system

Initial Setup:

When this command is invoked, 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.

Steps to follow after receiving the research query:

  1. Read any directly mentioned files first:

    • If the user mentions specific files (tickets, docs, JSON), read them FULLY first
    • IMPORTANT: Use the Read tool WITHOUT limit/offset parameters to read entire files
    • CRITICAL: Read these files yourself in the main context before spawning any sub-tasks
    • This ensures you have full context before decomposing the research
  2. Analyze and decompose the research question:

    • Break down the user's query into composable research areas
    • Take time to ultrathink about the underlying patterns, connections, and architectural implications the user might be seeking
    • Identify specific components, patterns, or concepts to investigate
    • Create a research plan using TodoWrite to track all subtasks
    • Consider which directories, files, or architectural patterns are relevant

Read the full file on GitHub · 213 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 213 lines · 11 tokens per session scan A 68fe18e1611e

Subscribe to this mod's changes

research_codebase is a command published in the GitHub repository jeffh/claude-plugins (12 stars, last pushed 17d ago), licensed Apache-2.0. It adds 11 tokens to every session and 2,387 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to research_codebase, differing in 46 lines, and is treated as a copy.