deep-research

deep-research is a skill for Claude Code, Codex from daymade/claude-code-skills. It costs 63 tokens per session (7,466 once invoked), scanned A, original, MIT.

A structured research workflow for producing reports with tracked evidence, source checks, citations, and review passes.

In plain words
What is it for?
It is for research reports, literature reviews, market and industry analysis, competitive studies, policy briefs, and technical briefs.
Why use it?
It reduces unsupported claims, weak sources, missing citations, and errors introduced when combining research from multiple contributors.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents; mentions Claude Code.

Good fit It is for research reports, literature reviews, market and industry analysis, competitive studies, policy briefs, and technical briefs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/daymade/claude-code-skills/deep-research
About the project

Claude Code Skills Marketplace is a collection and marketplace of skills, plugins, agents, and instructions that extend Claude Code with specialized development workflows. It is for developers who want to install existing workflows or create, validate, and package their own Claude Code skills.

daymade/claude-code-skills · 1,473 stars · on GitHub

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 daymade/claude-code-skills --skill deep-research
Clone the repo
git clone --depth 1 https://github.com/daymade/claude-code-skills

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin deep-research — /plugin install deep-research after adding the marketplace above.

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 deep-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/daymade/claude-code-skills/deep-research/github.svg)](https://agentmods.dev/skills/daymade/claude-code-skills/deep-research)
Your own site
<a href="https://agentmods.dev/skills/daymade/claude-code-skills/deep-research"><img src="https://agentmods.dev/badge/skills/daymade/claude-code-skills/deep-research/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 deep-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/daymade/claude-code-skills/deep-research"><img src="https://agentmods.dev/badge/skills/daymade/claude-code-skills/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,466 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. Third-party audits
  • Socket pass 12 Oct 2026
  • Snyk warn 12 Oct 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 522
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
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 $0.00063 $0.07466
Opus 5.5 $0.00025 $0.02986
Sonnet 5.5 $0.00013 $0.01493
Haiku 4.5 $0.00006 $0.00747

Measured 6d ago against content hash 4cbb96dfda45, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

deep-research 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 6d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/provider_runs.py, scripts/research_assets.py, tests/test_provider_runs.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

deep-research/SKILL.md · 595 lines

How it starts

The opening of the file, as written. The whole thing — 595 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Research

Create high-fidelity research reports with strict format control, evidence mapping, source governance, and multi-pass synthesis.

Architecture: Lead Agent + Subagents

Lead Agent (coordinator — minimizes raw search context)
  |
  P0: Environment + source policy setup
  |
  P1: Question and claim map (decision questions, evidence routes, stop rules)
  |
  Dispatch ──→ Subagent A ──→ writes task-a.md ──┐
           ──→ Subagent B ──→ writes task-b.md ──┤ (parallel)
           ──→ Subagent C ──→ writes task-c.md ──┘
  |                                               |
  |     research-notes/  <────────────────────────┘
  |
  P2: Dispatch + investigate → evidence packets
  P3: Citation registry + source governance
  P4: Evidence-mapped outline with counter-evidence and unknowns
  P5: Draft from evidence packets; reopen decisive originals
  P6: Counter-review (claims, confidence, alternatives)
  P7: Verify → asset check and catalog registration

Context discipline: Keep raw search-result noise in task workspaces. Pass evidence packets to the lead agent, including locators and short source excerpts. Notes are routing aids, not authorities: the lead agent must open the original source for every load-bearing claim, conflicting claim, and exact figure/date/quotation used in the report.

Run and asset contract — every invocation

Before external retrieval, read research-asset-contract.md. Create or resume a durable project study for single-route and multi-route research alike. Search its explicit prior-study catalog, open relevant earlier originals and record reuse/adapt/reject decisions. Draft the decision questions and provider × actual-mode lanes in study.json. Write one exact dispatch_context with the user-supplied seed URL, named entities, and verified codes or other identifiers needed for the query. Include that string in every lane prompt; research_assets.py start rejects an omission. Capture the user's named seed article or document as an original before external dispatch, and follow the provider handoff when a provider cannot read its URL. A model workspace may not see the user's link or local files just because the coordinator does. Then run provider_runs.py plan. Keep every user-requested mode in the plan; a route that adds no value, is unavailable or lacks paid authorization gets a reasoned deferred event. A report assembled from direct web search and internal subagents still needs a direct-source lane and the same archive.

Read the full file on GitHub · 595 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. 6d ago Changed 4cbb96dfda45
  2. 7d ago Changed · +23 lines f3dbcfa02799
  3. 10d ago Changed · +4 lines f451d2cefd97
  4. 21d ago Changed · +14 lines · -25 tokens per session 98fe044e2e06
  5. 22d ago Changed · +18 lines · +3 tokens per session 8a189a2c41e9
  6. 24d ago Changed · -3 lines · -63 tokens per session 36dc7a979b9c
  7. 27d ago Changed · +2 lines · +32 tokens per session 59bd1c5f93df
  8. 1mo ago Changed · -8 lines · -39 tokens per session db96428c47be
  9. 1mo ago First seen · 545 lines · 155 tokens per session scan A ae34edc0b037

Subscribe to this mod's changes

deep-research is a skill published in the GitHub repository daymade/claude-code-skills (1,473 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 7,466 once invoked, about $0.0003 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 2026-09-03.

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