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Agent skill

Deep Research

by alirezarezvani in alirezarezvani/claude-skills

Run a disciplined, multi-source research investigation for a high-stakes question or decision — fan-out web search across many channels, parallel sub-agents, source triangulation (each claim backed…

MITAuto-check passedResearch & Science

Install Deep Research

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

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

GitHub CLI
$ gh skill install alirezarezvani/claude-skills 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/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/research/deep-research/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
28k
Token cost
~1.9k tokens
SKILL.md length
854 words
Files
2 (incl. references)
Skills in repo
342
Repo updated
First seen
Licence
MIT

At a glance

Run a disciplined, multi-source research investigation for a high-stakes question or decision — fan-out web search across many channels, parallel sub-agents, source triangulation (each claim backed…

  • A low-quality answer is expensive: strategy work
  • SKILL.md covers How it differs from a quick…, The pipeline (9 phases), Core mechanisms and Output structure, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Comparing N products/methods/markets

What it does

Deep Research is an agent skill from alirezarezvani/claude-skills. Run a disciplined, multi-source research investigation for a high-stakes question or decision — fan-out web search across many channels, parallel sub-agents, source triangulation (each claim backed by ≥3 independent sources), an adversarial review pass, and every source saved to its own file with verbatim quotes for reuse. Use when a low-quality answer is expensive: strategy work, comparing N products/methods/markets, validating a hypothesis with external data, or mapping how a field works. NOT for quick…

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/full-catalog.md`).

It sits in Research & Science, covering Deep research, Subagents and Fact-checking and source verification. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.

When your agent uses it

  • A low-quality answer is expensive: strategy work
  • Comparing N products/methods/markets
  • Validating a hypothesis with external data
  • Mapping how a field works

Example prompts

  • “/deep-research”

What it can do on your machine

Read from SKILL.md and the folder at commit 19392f7. 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 1.9k tokens when it runs, and up to ~2.2k if it reads all its reference files. Until then it costs about 179 tokens; SKILL.md has 854 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~179
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.2k

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 alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 854 words, ~1,926 tokens.

Download SKILL.mdSave it as .claude/skills/deep-research/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
deep-research
description
Run a disciplined, multi-source research investigation for a high-stakes question or decision — fan-out web search across many channels, parallel sub-agents, source triangulation (each claim backed by ≥3 independent sources), an adversarial review pass, and every source saved to its own file with verbatim quotes for reuse. Use when a low-quality answer is expensive: strategy work, comparing N products/methods/markets, validating a hypothesis with external data, or mapping how a field works. NOT for quick fact-checks (answer directly), structured 12-dimension competitor scoring (use competitive-teardown), or fast topic overviews where the decision risk is low (use the research router instead).

Deep Research — Disciplined Meta-Research

Turn "research this topic" into an auditable, reusable investigation instead of a one-shot wall of text. The output is a folder you can return to in a month: every claim traces to a specific source file, the plan documents why each choice was made, and a refresh protocol lets you update it later without re-running everything.

This is the heavy, methodical end of research. It is not a fast overview — it is the workflow you reach for when getting the answer wrong costs more than the tokens spent getting it right.

How it differs from a quick research router

A router-style research skill (keyword-classify → delegate → short sequential search → markdown brief) is optimal when you need an answer fast and the decision risk is low. deep-research is the opposite trade: it pays for rigor. Use it when the answer feeds a strategy, an irreversible decision, a published artifact, or a hypothesis you need to actually test — situations where a shallow fallback would be a liability.

Concretely, deep-research adds what a fast overview does not: falsifiable hypotheses up front, parallel sub-agent fan-out across many channels, triangulation with explicit source-type diversity, a mandatory adversarial pass, per-source files with verbatim quotes, and a refresh_targets.md for delta-updates later.

The pipeline (9 phases)

Depth scales with the task — shallow runs the core phases inline; medium/deep add capability discovery, verification, and refresh targets.

#PhaseWhat it does
1ReframeRewrite the question, fix the underlying decision, state 2–4 falsifiable hypotheses
2Genre & blocksPick the report genre (qa / explainer / decision / landscape / validation / custom) and its building blocks
3PlanWrite plan.md: scope, structure, sourcing strategy, opposition queries, risk register, stop-criteria
3.5Capability discoveryAudit available API keys/channels in the environment; map subtopics to sources; fall back to HTML where needed
4Search (loop)Dispatch sources → launch sub-agents in parallel → fetch & dedup → save each to sources/NN.md; re-evaluate between rounds
5Score & triangulateRate every source on Credibility / Recency / Bias; require ≥3 independent, differently-typed sources per thesis
6Synthesize + adversarialAssemble the report from blocks, run 4 self-critique questions, add steel-manned counter-arguments
6.5VerifyLightweight citation check before closing
7Refresh targetsExtract entities / numbers / hypotheses into refresh_targets.md — the entry point for future updates

Core mechanisms

These are what separate a documented investigation from a confident guess:

  • Triangulation. Every thesis must be backed by ≥3 independent sources of different types (primary / academic / industry / discussion). A claim with fewer is flagged "insufficient evidence," not stated as fact.
  • Source-grounding. Each source becomes its own sources/NN_slug.md with metadata, verbatim quotes, and scores. No dangling claim — every assertion links back to a specific file. An empty fetch produces an empty claim, never a fabricated citation.
  • Adversarial pass. Phase 6 always runs the strongest available reasoning: 4 self-critique questions plus an active search for counter-arguments and disconfirming evidence.
  • Falsifiable hypotheses. Phase 1 commits to 2–4 hypotheses; Phases 5–6 explicitly confirm or refute each against the evidence, or mark it under-determined.
  • Parallel sub-agents. Phase 4 launches search sub-agents concurrently (cheap models for broad web sweeps, stronger ones for reasoning-heavy subtopics) — never one-at-a-time.
  • Refresh protocol. Phase 7 emits refresh_targets.md; an update <slug> run produces a delta (new entrants, entity changes, refreshed numbers, adversarial triggers) instead of replaying the whole investigation.
  • Atomic findings. Reusable theses in findings/FN.md plus a sources.csv index — research compounds across questions instead of starting from zero each time.
Show full SKILL.md (295 more words)Show less

Output structure

<root>/<slug>/
├── plan.md                  # scope, sourcing strategy, risk register, changelog
├── sources.csv              # index of every source with scores
├── sources/
│   ├── 01_<slug>.md         # one file = one source (metadata + verbatim quotes)
│   └── ...
├── findings/                # atomic, reusable theses (larger investigations)
│   └── F1_<short>.md
├── refresh_targets.md       # what to watch on update (medium/deep)
├── diffs/
│   └── YYYY-MM-DD_delta.md   # delta from an `update <slug>` run
└── YYYY-MM-DD_<genre>.md     # final report

When to use

  • A low-quality answer is expensive: strategy, business plan, report, or article groundwork.
  • Comparing N institutions, products, methodologies, or markets and you need defensible reasoning.
  • Validating a hypothesis or a decision against external data.
  • Meta-research: "understand how X works," "map the landscape of Y," answering a connected series of questions.

Anti-Patterns

  • Don't skip the existing-work check. Before searching, see whether the answer is already in the project or in a prior research folder — you risk re-researching something you already have.
  • Don't skip reframing, even when the request "seems clear." The decision behind the question usually changes the search.
  • Don't output to chat only. Always persist sources and the report to files — the reuse value is in the folder, not the transcript.
  • Don't fabricate citations. If a fetch returns nothing, the claim is empty — never invent a plausible URL. Bind every claim to a saved verbatim quote.
  • Don't build conclusions on a thin corpus. Too few sources, or sources that all share one type, means triangulation hasn't happened — say so rather than overstating confidence.
  • Don't skip the adversarial pass on medium/deep investigations. Confirmation-only research is the failure mode this skill exists to prevent.
  • Don't run sub-agents sequentially. Fan-out in parallel; serial search wastes the wall-clock advantage.
  • Don't collapse sources/ into one file. Per-source files are what make findings searchable and reusable across investigations.
  • Don't pick the heaviest model for everything. Match model to subtask — cheap for broad sweeps, strong for synthesis and the adversarial pass.

Cross-References

  • research router — for fast topic overviews where decision risk is low; deep-research is the heavyweight alternative when rigor matters more than speed.
  • competitive-teardown — for comparing N competitors on a structured 12-dimension matrix.
  • litreview / dossier / patent — domain specialists when the investigation is narrowly academic, person/company-focused, or patent-focused.

© alirezarezvani, MIT. 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 1 other file (references) in research/deep-research/skills/deep-research of alirezarezvani/claude-skills.

  • SKILL.md
  • references/full-catalog.md

Open the folder on GitHubat commit 19392f7

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 skillalirezarezvani/claude-skills28k—~1.9kAutomated safety check: PassMIT
Deep Research Agent TeamImbad0202/academic-research-skills51k—~13kAutomated safety check: PassCustom licence
Web ResearchJuncai22/spring-ai-agent-learning1232 repos~1.1kAutomated safety check: PassApache-2.0
Workflow PatternsQuintinShaw/pi-dynamic-workflows555—~827Automated safety check: PassMIT
Net Deep Researchh4444433333/net-deep-research123—~3.3kAutomated safety check: PassMIT
Architect ResearchDanMcInerney/architect-loop626—~2.3kAutomated safety check: PassMIT

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Categories

Questions about Deep Research

What does Deep Research do?

Run a disciplined, multi-source research investigation for a high-stakes question or decision — fan-out web search across many channels, parallel sub-agents, source triangulation (each claim backed…. Deep Research is an agent skill from alirezarezvani/claude-skills. Run a disciplined, multi-source research investigation for a high-stakes question or decision — fan-out web search across many channels, parallel sub-agents, source triangulation (each claim backed by ≥3 independent sources), an adversarial review pass, and every source saved to its own file with verbatim quotes for reuse.

When should I use Deep Research?

Deep Research fits situations like: A low-quality answer is expensive: strategy work; comparing N products/methods/markets; validating a hypothesis with external data; mapping how a field works.

How do I install Deep Research in Claude Code?

Run `npx skills add alirezarezvani/claude-skills --skill deep-research -a claude-code`. Or copy the skill folder (research/deep-research/skills/deep-research in alirezarezvani/claude-skills) 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 alirezarezvani/claude-skills --skill deep-research -a codex`. Or copy the skill folder (research/deep-research/skills/deep-research in alirezarezvani/claude-skills) 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 alirezarezvani/claude-skills --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 MIT 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 1.9k tokens (SKILL.md is roughly 7.7k 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 245 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: Deep Research Agent Team (Imbad0202/academic-research-skills, 51k stars), Web Research (Juncai22/spring-ai-agent-learning, 123 stars), Workflow Patterns (QuintinShaw/pi-dynamic-workflows, 555 stars) and Net Deep Research (h4444433333/net-deep-research, 123 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deep Research?

alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,891 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.

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