research_codebase

research_codebase is a skill for Claude Code from adrielp/ai-engineering-harness. It costs 19 tokens per session (566 once invoked), scanned A, original, Apache-2.0.

A codebase research workflow that uses several helper agents to inspect different parts of a software project and combines their findings. It is intended for answering questions about how a repository is organized and works.

In plain words
What is it for?
Use it to investigate where code lives, understand how components work, find related patterns, and produce a combined answer to a codebase question.
Why use it?
It reduces the time spent searching through files and helps connect locations, implementation details, and similar examples across a large codebase.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: mentions subagents; names the TodoWrite tool.

Good fit Use it to investigate where code lives, understand how components work, find related patterns, and produce a combined answer to a codebase question.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/adrielp/ai-engineering-harness/research_codebase
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.

Any agent
npx skills add adrielp/ai-engineering-harness --skill research_codebase
Clone the repo
git clone --depth 1 https://github.com/adrielp/ai-engineering-harness

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for research_codebase

README.md
[![agentmods](https://agentmods.dev/badge/skills/adrielp/ai-engineering-harness/research_codebase/github.svg)](https://agentmods.dev/skills/adrielp/ai-engineering-harness/research_codebase)
Your own site
<a href="https://agentmods.dev/skills/adrielp/ai-engineering-harness/research_codebase"><img src="https://agentmods.dev/badge/skills/adrielp/ai-engineering-harness/research_codebase/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for research_codebase

Your own site · 80×15
<a href="https://agentmods.dev/skills/adrielp/ai-engineering-harness/research_codebase"><img src="https://agentmods.dev/badge/skills/adrielp/ai-engineering-harness/research_codebase.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 566 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 5.1 $0.00019 $0.00566
Opus 5.5 $0.00008 $0.00226
Sonnet 5.5 $0.00004 $0.00113
Haiku 4.5 $0.00002 $0.00057

Measured 1mo ago against content hash e9b0a1591f59, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 1mo 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.

claude/skills/research_codebase/SKILL.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 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.

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.

Steps to follow after receiving the research query:

  1. Read any directly mentioned files first:

    • If the user mentions specific files, read them FULLY first
    • Read these files yourself before spawning sub-tasks
  2. Analyze and decompose the research question:

    • Break down the query into composable research areas
    • Create a research plan using TodoWrite
  3. Spawn parallel sub-agent tasks:

    For codebase research:

    • Use codebase-locator to find WHERE files and components live
    • Use codebase-analyzer to understand HOW specific code works
    • Use codebase-pattern-finder for examples of similar implementations

    For thoughts directory:

    • Use thoughts-locator to discover what documents exist
    • Use thoughts-analyzer to extract key insights from documents

    For web research (only if explicitly asked):

    • Use web-search-researcher for external documentation
  4. Wait for all sub-agents to complete and synthesize findings

  5. Generate research document at thoughts/research/YYYY-MM-DD_topic.md:

---
date: [ISO date with timezone]
researcher: [Your name]
topic: "[Research Question]"
tags: [research, relevant-tags]
status: complete
---

# Research: [Topic]

## Research Question
[Original user query]

## Summary
[High-level findings]

## Detailed Findings

### [Component/Area 1]
- Finding with reference (`file.ext:line`)
- Implementation details

## Code References
- `path/to/file.py:123` - Description

## Architecture Insights
[Patterns and design decisions discovered]

## Historical Context (from thoughts/)
[Relevant insights from thought documents]

## Open Questions
[Areas needing further investigation]

Read the full file on GitHub · 90 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. 1mo ago First seen · 90 lines · 19 tokens per session scan A e9b0a1591f59

Subscribe to this mod's changes

research_codebase is a skill published in the GitHub repository adrielp/ai-engineering-harness (22 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 19 tokens to every session and 566 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

component-fixtures

Use when creating or updating component fixtures for screenshot testing or their shared infrastructure, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, validation, and common pitfalls.

microsoft/vscode · 53 tokens