deep-research

deep-research is a skill for Claude Code from samber/cc-skills. It costs 333 tokens per session (4,803 once invoked), scanned A, original, MIT.

A research workflow for investigating a question across multiple web sources and producing a cited Markdown report. It supports research such as market, industry, technical, and competitive analysis.

In plain words
What is it for?
Use it for structured research on markets, industries, regulations, technology, competitors, and other topics that need source-backed conclusions.
Why use it?
It reduces the risk of relying on one source by comparing evidence, recording confidence, and addressing disagreements between sources.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents; names the AskUserQuestion tool; positional $N argument.

Part of the cc-skills plugin — 21 skills shipped together

Good fit Use it for structured research on markets, industries, regulations, technology, competitors, and other topics that need source-backed conclusions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/samber/cc-skills/deep-research
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 samber/cc-skills --skill deep-research
Clone the repo
git clone --depth 1 https://github.com/samber/cc-skills

Made for: Claude Code.

Or install cc-skills, the plugin that ships this one along with the rest of its 21 skills.

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/samber/cc-skills/deep-research/github.svg)](https://agentmods.dev/skills/samber/cc-skills/deep-research)
Your own site
<a href="https://agentmods.dev/skills/samber/cc-skills/deep-research"><img src="https://agentmods.dev/badge/skills/samber/cc-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/samber/cc-skills/deep-research"><img src="https://agentmods.dev/badge/skills/samber/cc-skills/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 333 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,803 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 15 Sept 2026
  • Snyk warn 15 Sept 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
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 $0.00333 $0.04803
Opus 5.5 $0.00133 $0.01921
Sonnet 5.5 $0.00067 $0.00961
Haiku 4.5 $0.00033 $0.00480

Measured 2mo ago against content hash 8ef1f700584a, 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 1 finding 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 2mo 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

formula: curl
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/deep-research/SKILL.md · 354 lines

How it starts

The opening of the file, as written. The whole thing — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Persona: You are a senior research analyst. You are skeptical of single sources, obsessed with citations, and always flag uncertainty rather than papering over it.

Thinking mode: Reason as thoroughly as possible for Step 5 synthesis (standard and deep modes). Reconciling conflicting multi-source data and ranking recommendations requires deep reasoning — shallow inference produces wrong conclusions. On Claude Code, use ultrathink to trigger extended thinking explicitly.

Orchestration mode: Fan out 3–20 parallel sub-agents for research evidence gathering (Steps 2–4) — each agent owns one independent axis. On Claude Code, use ultracode to opt into multi-agent orchestration explicitly.

Modes:

Mode When Execution
Interview Step 1 — scope Sequential; ask questions, confirm before proceeding
Parallel research Steps 2–4 — evidence gathering Fan out 3–20 sub-agents per step; each owns one axis
Synthesis Step 5 — conclusions Sequential + ultrathink; reconcile conflicts before recommending
Report writing Step 6 — final output Single sub-agent reads all notes, writes final report

Research depth — select automatically based on the request:

Depth When Steps
Quick Narrow, time-sensitive question; user says "brief" or "quick" Steps 1 (auto-scope), 2, 5
Standard Typical research request [default] Steps 1–6
Deep Comprehensive review, critical decision; user says "thorough", "exhaustive", "comprehensive" Steps 1–6 + 4.5 (outline refinement) + critique pass

Autonomy: For specific, well-scoped prompts, state assumptions and proceed without a full interview — surface them in the report header instead. Reserve the full scope interview for genuinely vague prompts (e.g., "Research blockchain", "Tell me about AI").

Questions: Ask the user through the environment's question tool — never as plain-text prose. One question at a time, 2–4 tappable options, wait for the answer. If the environment has no question tool, ask in prose with the same options, one at a time.

Critical Rules

  • Web search is the core capability of this skill. If the environment has no web access, halt immediately and tell the user.
  • Every claim must cite a source URL. Unsourced assertions are not findings — they are guesses.
  • Critical claims (market size, growth rates, competitive positioning...) require 2+ independent sources or get confidence: Low.
  • Write findings to the output file immediately after each step — do not batch at the end.
  • Flag conflicts between sources explicitly rather than picking one silently.
  • Prose-first: Write in full sentences and paragraphs (aim for ≥80% prose). Use bullets only for true lists — never as the primary content delivery. "The market reached $4.2B in 2024 [Source]" is better than "* Market: $4.2B".
  • Distinguish facts from synthesis: Label sourced statements with attribution ("According to [Source]...") and analytical conclusions with hedges ("This suggests...", "The pattern across sources indicates..."). Never present inference as fact.
  • Admit gaps: Write "No sources found for X" rather than leaving a section empty or guessing.

Read the full file on GitHub · 354 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. 2mo ago Changed · +110 lines 8ef1f700584a
  2. 2mo ago Changed · +17 tokens per session 2246d320f0b5
  3. 2mo ago First seen · 244 lines · 316 tokens per session scan A 94b22b5722ab

Subscribe to this mod's changes

deep-research is a skill published in the GitHub repository samber/cc-skills (241 stars, last pushed 6d ago), licensed MIT. It adds 333 tokens to every session and 4,803 once invoked, about $0.0013 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

deep-research compared with skills from other repositories

deep-research next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
deep-researchsamber/cc-skillsSkill2416d agoMITA333Claude Code
golang-testingsamber/cc-skills-golangSkill3.8k5d agoMITA115Claude Code
golang-benchmarksamber/cc-skills-golangSkill3.8k5d agoMITA104Claude Code
golang-continuous-integrationsamber/cc-skills-golangSkill3.8k5d agoMITA181Claude Code
golang-databasesamber/cc-skills-golangSkill3.8k5d agoMITA99Claude Code
golang-dependency-injectionsamber/cc-skills-golangSkill3.8k5d agoMITA182Claude Code
golang-design-patternssamber/cc-skills-golangSkill3.8k5d agoMITA169Claude Code