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Agent skill

Research

by warpdotdev in warpdotdev/common-skills

Delegate noisy investigation to one or more subagents so the orchestrator's context stays clean, then work from the distilled answer.

MITAuto-check passedAgent Workflows

Install Research

skills CLI
$ npx skills add warpdotdev/common-skills --skill research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install warpdotdev/common-skills research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/warpdotdev/common-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/research .claude/skills/research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
research
GitHub stars
606
Used in
1 other repo
Token cost
~1.3k tokens
SKILL.md length
748 words
Files
1
Skills in repo
28
Repo updated
First seen
Licence
MIT

At a glance

Delegate noisy investigation to one or more subagents so the orchestrator's context stays clean, then work from the distilled answer.

  • Works in 3 steps: The direct answer to the question. → The key evidence: exact file paths and… → Anything surprising or any…
  • Answering a question would require reading many files
  • SKILL.md covers Why this matters, When to use it, How to do it and After you get the answer
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Research is an agent skill from warpdotdev/common-skills. Delegate noisy investigation to one or more subagents so the orchestrator's context stays clean, then work from the distilled answer. Use this skill whenever answering a question would require reading many files, long logs, large diffs, or wide codebase surveys — i.e. when producing the answer generates far more noise than the answer itself. Use it for "how does X work", "where is Y used", "what's the root cause of Z", "summarize this PR/log" style questions, and reach for it liberally before reading a pile of…

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Subagents and Root cause analysis. The licence is MIT.

When your agent uses it

  • Answering a question would require reading many files
  • Wide codebase surveys — i.e
  • How does X work
  • Where is Y used

Example prompts

  • “how does X work”
  • “where is Y used”
  • “s the root cause of Z”
  • “/research”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. The direct answer to the question.
  2. The key evidence: exact file paths and symbols (e.g. src/session.rs:142, fn reconnect), so you can jump straight to what matters.
  3. Anything surprising or any caveats/unknowns it hit.

What it can do on your machine

Read from SKILL.md and the folder at commit 69b4753. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Research loads about 1.3k tokens when it runs. Until then it costs about 135 tokens; SKILL.md has 748 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~135
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from warpdotdev/common-skills at commit 69b4753, republished under its MIT licence (© warpdotdev). 748 words, ~1,299 tokens.

Download SKILL.mdSave it as .claude/skills/research/SKILL.md (or your agent's skills folder).
name
research
description
Delegate noisy investigation to one or more subagents so the orchestrator's context stays clean, then work from the distilled answer. Use this skill whenever answering a question would require reading many files, long logs, large diffs, or wide codebase surveys — i.e. when producing the answer generates far more noise than the answer itself. Use it for "how does X work", "where is Y used", "what's the root cause of Z", "summarize this PR/log" style questions, and reach for it liberally before reading a pile of files inline.

Research

Use this skill to answer a question by delegating the work of finding the answer to a subagent, so that the byproducts of that work — file contents, log noise, dead-end reads — never enter your own context. You get back a distilled answer plus the evidence that supports it, and you stay sharp for the actual task.

Why this matters

Your context window is your most valuable and limited resource. Reading twenty files to discover that three of them mattered permanently pollutes your context with seventeen files of noise, degrading every subsequent decision you make. A subagent absorbs that noise on your behalf and hands you only the signal. Think of it as asking a colleague to dig through the archives and report back, rather than dumping the whole archive on your desk.

When to use it

Reach for research delegation when the cost of producing the answer is far greater than the answer itself. Strong signals:

  • You'd need to read many files to find the few that are relevant.
  • You'd need to wade through long test output, CI logs, or stack traces to extract a failure.
  • You'd need to survey how a pattern, API, or symbol is used across the whole repo.
  • You'd need to read and summarize a large diff or PR.
  • The question has several independent sub-parts that could be investigated separately.

Examples — good fits:

  • "What's the root cause of this failing test?" (the subagent reads the logs and traces the code; you get the cause)
  • "How is SessionManager used across the codebase?" (the subagent greps and reads; you get a summary with call sites)
  • "Summarize what this 4,000-line PR changes and why." (the subagent reads the diff; you get the shape of it)

Examples — do NOT delegate:

  • Reading 2–3 files you already know you need. Just read them directly; delegation adds latency for no context savings.
  • A single grep or one-line lookup. Do it yourself.
  • Anything where you need the raw material for your next step. If you're about to edit the files you'd be reading, delegating is counterproductive — you'd just have to re-read them yourself to make the change. Research delegation pays off when the output is a conclusion, not when it's material you'll work on directly.

The cost of a subagent is real (latency and tokens), so the test is always: does the noise I'd avoid outweigh that cost?

How to do it

Spawn locally with a search model

Always spawn research subagents as local agents, never remote — including when the parent is a factory or cloud agent.

Pick the model for the search task, not your own. Research subagents should search and distill, not analyze: use gpt-5.6-luna-medium for simple search, and gpt-5.6-luna-xhigh for more involved requests (for example, tracing data flow through call sites).

Show full SKILL.md (287 more words)Show less
Single vs. parallel

Default to a single subagent. Spawn multiple subagents in parallel only when the question genuinely decomposes into independent sub-parts that don't need to share intermediate findings — for example, "how does auth work AND how does billing work AND how does the rate limiter work" are three independent investigations that can run at once. Parallelism is a capability worth using when the parts are truly independent, since separate subagents can investigate simultaneously; but don't force a single coherent question into artificial fragments.

Brief the subagent well

The subagent does not share your intent, so spell it out. A good research brief includes:

  • The exact question to answer.
  • Where to look (repo path, branch, suspected files/symbols if you know them).
  • That it is read-only — it should investigate and report, not modify files, unless the task explicitly calls for changes.
  • The output you want back (see below).
Ask for signal, not transcript

Tell the subagent to return a distilled answer plus its supporting evidence, not a raw dump. Specifically:

  1. The direct answer to the question.
  2. The key evidence: exact file paths and symbols (e.g. src/session.rs:142, fn reconnect), so you can jump straight to what matters.
  3. Anything surprising or any caveats/unknowns it hit.

The whole point is that the noise stays with the subagent. If a report comes back bloated, send a focused follow-up to the same subagent asking it to tighten the answer — it retains its context and can refine cheaply.

After you get the answer

Work from the distilled result. If you later find you need the underlying files to make edits, read them directly at that point — now you know exactly which ones matter, so you read three files instead of twenty.

© warpdotdev, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/research of warpdotdev/common-skills.

Open the folder on GitHubat commit 69b4753

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in warpdotdev/common-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Research this skillwarpdotdev/common-skills6061 repos~1.3kAutomated safety check: PassMIT
Bug Hunt SwarmDimillian/Skills4k—~1.6kAutomated safety check: PassMIT
Bug Hunt Swarmsickn33/agentic-awesome-skills47k1 repos~1.9kAutomated safety check: PassMIT
Analyze Trajectoryyologdev/yoyo-evolve1.9k—~3.6kAutomated safety check: PassMIT
Diagnosing Superpowers SessionsjnMetaCode/superpowers-zh8.3k—~858Automated safety check: PassMIT
TroubleshootDeL-TaiseiOzaki/claude-code-orchestra199—~7.6kAutomated safety check: PassMIT

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Categories

Questions about Research

What does Research do?

Delegate noisy investigation to one or more subagents so the orchestrator's context stays clean, then work from the distilled answer. Research is an agent skill from warpdotdev/common-skills. Delegate noisy investigation to one or more subagents so the orchestrator's context stays clean, then work from the distilled answer.

When should I use Research?

Research fits situations like: answering a question would require reading many files; wide codebase surveys — i.e; how does X work; where is Y used.

How do I install Research in Claude Code?

Run `npx skills add warpdotdev/common-skills --skill research -a claude-code`. Or copy the skill folder (.agents/skills/research in warpdotdev/common-skills) into .claude/skills/research in your project. Claude Code loads it when a task matches its description.

How do I install Research in Codex?

Run `npx skills add warpdotdev/common-skills --skill research -a codex`. Or copy the skill folder (.agents/skills/research in warpdotdev/common-skills) into .agents/skills/research in your project. Codex loads it when a task matches its description.

Can I use Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add warpdotdev/common-skills --skill research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/research, .gemini/skills/research, .github/skills/research and .opencode/skills/research in your project.

What does Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Research is instructions for the agent only.

Does Research access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Research safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Research use?

Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Research use?

About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Research?

Skills that share tags, products or a category with Research: Bug Hunt Swarm (Dimillian/Skills, 4k stars), Bug Hunt Swarm (sickn33/agentic-awesome-skills, 47k stars), Analyze Trajectory (yologdev/yoyo-evolve, 1.9k stars) and Diagnosing Superpowers Sessions (jnMetaCode/superpowers-zh, 8.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Research?

warpdotdev (a GitHub organization) maintains it in warpdotdev/common-skills, which has 606 GitHub stars. The repository holds 28 skills in this directory. The repository was last updated on September 30, 2026.

Source: warpdotdev/common-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.