Agent skill

Deep Research

by asgeirtj in asgeirtj/system_prompts_leaks

A skill your agent uses when the user's prompt requires (1) researching a topic across multiple sources, comparing options or alternatives, analyzing trends or history, understanding markets or…

CC0-1.0Auto-check passedResearch & Science

Install Deep Research

skills CLI
$ npx skills add asgeirtj/system_prompts_leaks --skill deep-research -a claude-code

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

GitHub CLI
$ gh skill install asgeirtj/system_prompts_leaks deep-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/asgeirtj/system_prompts_leaks.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Anthropic/claude-code/skills/anthropic-skills/deep-research .claude/skills/deep-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
deep-research
GitHub stars
69k
Token cost
~3.3k tokens
SKILL.md length
1,711 words
Files
3 (incl. references)
Skills in repo
128
Repo updated
First seen
Licence
CC0-1.0

At a glance

A skill your agent uses when the user's prompt requires (1) researching a topic across multiple sources, comparing options or alternatives, analyzing trends or history, understanding markets or…

  • Works in 7 steps: [If necessary] Ask the user follow up… → Choose a title for this research and… → Decompose the user's query to determine… → …
  • The users prompt requires
  • SKILL.md covers Your Role and Process
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Deep Research is an agent skill from asgeirtj/system_prompts_leaks. Use this skill when the user's prompt requires (1) researching a topic across multiple sources, comparing options or alternatives, analyzing trends or history, understanding markets or industries, or reviewing literature or studies and (2) synthesizing that research into a comprehensive, narrative report. If you're planning to search the web or internal knowledge bases, consider using this skill. This skill coordinates research subagents, so use it only when you have a tool for spawning subagents (the Agent or…

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/report-writer.md` and `references/researcher.md`).

It sits in Research & Science, covering Deep research, Subagents and Web search. The repository describes itself as: Documented system prompts from Anthropic - Claude Fable 5.1, Opus 5.5, Claude Design, Claude Code. OpenAI - ChatGPT GPT-6-Astra, Codex. Google - Gemini 3.8 Flash, 3.1 Pro… The licence is CC0-1.0.

When your agent uses it

  • The users prompt requires
  • Researching a topic across multiple sources
  • Comparing options
  • Analyzing trends

Example prompts

  • “/deep-research”

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. [If necessary] Ask the user follow up questions
  2. Choose a title for this research and create its directories
  3. Decompose the user's query to determine subagent count
  4. Spawn foreground research subagents (in parallel)
  5. [If absolutely necessary] Conduct one additional round of research
  6. Spawn foreground report writer subagent
  7. Deliver report to user

What it can do on your machine

Read from SKILL.md and the folder at commit 60d44cc. 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

Deep Research loads about 3.3k tokens when it runs, and up to ~5.9k if it reads all its reference files. Until then it costs about 146 tokens; SKILL.md has 1,711 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~146
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.9k

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 asgeirtj/system_prompts_leaks at commit 60d44cc, republished under its CC0-1.0 licence (© asgeirtj). 1,711 words, ~3,286 tokens.

Download SKILL.mdSave it as .claude/skills/deep-research/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
deep-research
description
Use this skill when the user's prompt requires (1) researching a topic across multiple sources, comparing options or alternatives, analyzing trends or history, understanding markets or industries, or reviewing literature or studies and (2) synthesizing that research into a comprehensive, narrative report. If you're planning to search the web or internal knowledge bases, consider using this skill. This skill coordinates research subagents, so use it only when you have a tool for spawning subagents (the Agent or Task tool); otherwise, research the question directly.

Coordinating Deep Research

Your Role

You are acting as the coordinator. Your job is to analyze the user's question, spawn subagents, and manage output files. In order to keep your context clean, you should never conduct research or write files directly. For the same reason, you also should not read the researcher.md or report-writer.md files.

As you work, if you have a to-do or task-list tool (for example TodoWrite, or TaskCreate and TaskUpdate), use it to keep the user informed of your plan and progress. For clarity, don't reference subagents in those items. For example, you might say "Conduct research" instead of "Spawn research subagents".

Process

  1. [If necessary] Ask the user follow up questions
  2. Choose a title for this research and create its directories
  3. Decompose the user's query to determine subagent count
  4. Spawn research subagents (in parallel)
  5. [If absolutely necessary] Conduct one additional round of research
  6. Spawn report writer subagent
  7. Deliver report to user
1. [If necessary] Ask the user follow up questions

Read the user's question and consider if there are any missing or ambiguous details that need to be clarified. Some examples include:

  • Ambiguous terminology — Acronyms, jargon, or terms with multiple meanings across different fields
  • Implicit geographic or jurisdictional context — Questions where the answer varies significantly by region but no region is specified
  • Ambiguous entities — Company names, product names, or proper nouns that refer to multiple distinct entities

In order to avoid creating unnecessary friction with the user, follow ups should only be asked when they would meaningfully impact how research is conducted.

If you determine that you do need to ask follow up questions, do so with the AskUserQuestion tool.

2. Choose a title for this research and create its directories

First choose a title for this research. It names the folder that will hold this request's research notes and is also the filename of the report the user receives, where it is shown as the report's title, so make it a short, specific title for this research in the user's language and in sentence case: 3 to 6 words and under 50 characters (count them, and drop words rather than exceed either limit), using only letters, numbers and single spaces. Keep accented and non-Latin letters exactly as the user's language writes them (for example é, ñ, ü, 日本語) rather than converting them to plain ASCII, and use no hyphens, underscores, apostrophes or other punctuation. For example, a question about how the EU AI Act affects startups could use "EU AI Act startup obligations".

This research's folders go in the same directory you used for research earlier in this conversation, or, if this is the first research request here, in your current working directory (not /tmp or an uploads folder). Before settling on the title, recall the titles of any research reports you have already delivered earlier in this conversation, and, if a research_notes/ folder already exists in that directory, list it — each folder inside it holds the notes behind a report of the same name in reports/, possibly from an earlier conversation. Then pick the case that fits:

  • New topic: choose a fresh title.
  • Returning to, extending or updating an earlier topic: choose a title that still names the topic and says what this request adds or changes (for example "EU AI Act startup penalties", not just "Penalties"), or, if nothing distinguishes it from the earlier request, the earlier title with the next number added, dropping a word first if that would exceed the limits above (for example "EU AI Act startup obligations 2"). Note the path of the most recent earlier report on that topic and of its notes folder: you will pass them to the report writer in step 6 as background to build on. If that report or its notes folder is no longer on disk, pass on only what remains and research whatever is missing.
  • The user asks for earlier research to be redone independently: choose a title as in the previous case, but do not pass the earlier material on.

In every case the title must differ from every report delivered earlier in this conversation and from every folder you see, so that this request's notes and report never mix with or replace an earlier one's. Never rename, move or write into an earlier request's folder.

Then create the folders research_notes/{research title}/ and reports/ in that directory. Quote these paths in shell commands, since the title contains spaces, and note the directory's absolute path — you will give the subagents absolute paths into these folders, and they will store their files there.

3. Decompose the user's query to determine subagent count

In order to effectively spawn and task subagents, determine if and how the user's question can be decomposed into independent subtopics. These subtopics should be mutually exclusive and collectively exhaustive. For each subtopic, then consider if the research requires (or would benefit from) multiple independent points of view.

ResearchersDecomposition StrategyExample
1No meaningful decomposition — single factual question"Who won the 2024 Super Bowl?"
3Few natural angles or perspectives on a focused topic"Effects of temperature on salmon migration" → (1) biological mechanisms, (2) observed population data, (3) climate projections
4Distinct facets or evaluation criteria"Compare CRM platforms for small businesses" → (1) pricing models, (2) feature comparison, (3) integrations ecosystem, (4) user reviews/sentiment
5Multiple independent entities, regions, or domains"AI regulation landscape" → (1) US federal, (2) EU/GDPR-AI Act, (3) China, (4) UK, (5) industry self-regulation
6+Large enumerable set requiring systematic coverage"Electric vehicle adoption rates by US state" → researchers assigned to regional clusters

In the next step, you should spawn one subagent for each subtopic and independent point of view that you identified. When in doubt, 3 researchers is a reasonable default. If in step 2 you noted earlier research to build on, decompose only what this request adds or needs re-checked; the report writer will have the earlier material.

Show full SKILL.md (721 more words)Show less
4. Spawn foreground research subagents (in parallel)

For each subtopic and independent point of view determined above, spawn a foreground research subagent.

To spawn research subagents, use the Agent tool (named Task in some versions) with subagent_type="general-purpose" and run_in_background=false. Ensure that you provide extremely clear, specific instructions to each research subagent. Be sure to propagate constraints of the original question to the research subagent (e.g., temporal, geographical). In the prompts below, replace {path_to_skill} with this skill's base directory (the directory this SKILL.md was loaded from), {absolute path of your working directory} with the path you noted in step 2, and {research title} with the title you chose in step 2. Follow the exact format provided below:

Agent(
  run_in_background=false
  subagent_type="general-purpose",
  description="{3-5 words}",
  prompt="Research {specific narrow topic}.

Objective: {clear description of what's covered in the desired output}

Key questions:
- {Specific question 1}
- {Specific question 2}

Suggested sources:
- {Types of source to prioritize}

Constraints:
- {Any bounds from the initial user query, including temporal or geographic}

Save your output notes to {absolute path of your working directory}/research_notes/{research title}/{topic}.md

**As a first step, you must read {path_to_skill}/references/researcher.md for instructions on how to conduct research.**"
)

Example of a good, clear, detailed prompt for a research subagent:

Research the semiconductor supply chain crisis and its current status as of 2026.

Objective: Compile a dense report of the facts, covering the current situation, ongoing solutions, and future outlook, with specific timelines and quantitative data where available.

Key questions:

  • What are current bottlenecks?
  • What are the projected capacity increases from new fab construction?
  • What are geopolitical factors affecting supply chains?
  • When do experts predict supply will meet demand?

Suggested sources:

  • Recent quarterly reports from major chip manufacturers like TSMC, Samsung, and Intel, found on investor relations pages or through the SEC EDGAR database
  • Industry reports from SEMI, Gartner, and IDC that provide market analysis and forecasts
  • Government responses, including US CHIPS Act implementation progress at commerce.gov, EU Chips Act at ec.europa.eu, and similar initiatives in Japan, South Korea, and Taiwan through their respective government portals

Constraints:

  • Must reflect current state as of 2026 with prior issues clearly noted as such

Save your output notes to {absolute path of your working directory}/research_notes/Semiconductor supply chain outlook 2026/semiconductor_supply_chain.md

As a first step, you must read {path_to_skill}/references/researcher.md for instructions on how to conduct research."

In order to ensure research is conducted as quickly as possible, it's essential that all subagents are spawned in parallel, i.e., that you make all your calls to the Agent tool in a single turn.

5. [If absolutely necessary] Conduct one additional round of research

Based on the task summaries returned by the research subagents, determine if there are any critical gaps that would make the report incomplete. If deemed absolutely necessary, spawn researchers in parallel to close those gaps. Spawning additional researchers in this step delays delivery of the report to the user, leading to a much worse experience, so this must be used with extreme care.

This step can only be followed once - after the researchers finish, move immediately to Step 6, regardless of what results you get back. If you kick off any more research, you will hit timeouts or rate limits, meaning the user will never get an answer to their query. To help yourself remember this, you should always output "After these researchers finish, I will move directly to coordinating the report writer" when choosing to kick off additional research.

6. Spawn foreground report writer subagent

After research is done, spawn a single report writer subagent. Use the Agent tool (named Task in some versions) with subagent_type="general-purpose" and run_in_background=false, and fill in the placeholders as in step 4. For {the user's original question}, state the question this report must answer in full — if this request returns to or extends earlier research, that is the earlier question as amended by this request (for example "How does the EU AI Act affect startups, including penalties?"), not just the follow-up message. On the line for earlier research, give the absolute paths of the earlier report and notes folder you noted in step 2, or write "none" if you noted nothing to build on or the user asked for an independent redo. Follow the exact format provided below:

Agent(
  run_in_background=false
  subagent_type="general-purpose",
  description="Write final report",
  prompt="Read the notes in {absolute path of your working directory}/research_notes/{research title}/ and synthesize into a research report that answers: {the user's original question}.

Earlier research in this conversation to build on: {absolute paths of the earlier report and notes folder from step 2, or "none"}

Save your final report to this exact path: {absolute path of your working directory}/reports/{research title}.md

**As a first step, you must read {path_to_skill}/references/report-writer.md for instructions on how to write your research report.**"
)
7. Deliver report to user

After the report writer is done, make the report available to the user: if you have a tool for sending files to the user (for example SendUserFile), send reports/{research title}.md with it; otherwise, if your environment has a user-visible outputs folder, copy the report there under the same filename; otherwise leave it at reports/{research title}.md. Then read the finalized report and send the user a brief summary (aim for 3-5 sentences) that says where the full report is (sent to them, in the outputs folder, or its absolute path).

© asgeirtj, CC0-1.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in Anthropic/claude-code/skills/anthropic-skills/deep-research of asgeirtj/system_prompts_leaks.

  • SKILL.md
  • references/report-writer.md
  • references/researcher.md

Open the folder on GitHubat commit 60d44cc

Compare with similar skills

Deep 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.

Deep Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Deep Research this skillasgeirtj/system_prompts_leaks69k—~3.3kAutomated safety check: PassCC0-1.0
Web ResearchJuncai22/spring-ai-agent-learning1242 repos~1.1kAutomated safety check: PassApache-2.0
Web Researchbrycewang-stanford/Auto-Empirical-Research-Skills4.6k—~819Automated safety check: PassCustom licence
Deep Research312362115/claude107—~6.6kAutomated safety check: PassMIT
ULW Deep Researchcode-yeongyu/oh-my-openagent70k—~14kAutomated safety check: PassCustom licence
Deep ResearchXiaomiMiMo/MiMo-Code14k—~1.2kAutomated safety check: PassMIT

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Questions about Deep Research

What does Deep Research do?

A skill your agent uses when the user's prompt requires (1) researching a topic across multiple sources, comparing options or alternatives, analyzing trends or history, understanding markets or…. Deep Research is an agent skill from asgeirtj/system_prompts_leaks. Use this skill when the user's prompt requires (1) researching a topic across multiple sources, comparing options or alternatives, analyzing trends or history, understanding markets or industries, or reviewing literature or studies and (2) synthesizing that research into a comprehensive, narrative report.

When should I use Deep Research?

Deep Research fits situations like: the users prompt requires; researching a topic across multiple sources; comparing options; analyzing trends.

How do I install Deep Research in Claude Code?

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

How do I install Deep Research in Codex?

Run `npx skills add asgeirtj/system_prompts_leaks --skill deep-research -a codex`. Or copy the skill folder (Anthropic/claude-code/skills/anthropic-skills/deep-research in asgeirtj/system_prompts_leaks) into .agents/skills/deep-research in your project. Codex loads it when a task matches its description.

Can I use Deep 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 asgeirtj/system_prompts_leaks --skill deep-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/deep-research, .gemini/skills/deep-research, .github/skills/deep-research and .opencode/skills/deep-research in your project.

What does Deep Research need to run?

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

Does Deep 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 Deep 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 Deep Research use?

Deep Research is published under the CC0-1.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deep Research use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.6k tokens, read only when the agent opens those files.

What are the alternatives to Deep Research?

Skills that share tags, products or a category with Deep Research: Web Research (Juncai22/spring-ai-agent-learning, 124 stars), Web Research (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars), Deep Research (312362115/claude, 107 stars) and ULW Deep Research (code-yeongyu/oh-my-openagent, 70k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deep Research?

asgeirtj (a GitHub user) maintains it in asgeirtj/system_prompts_leaks, which has 69,280 GitHub stars. The repository holds 128 skills in this directory. The repository was last updated on October 10, 2026.

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