deep-research

deep-research is a skill for Claude Code, Codex from hssh8917/cc-skills. It costs 316 tokens per session (2,915 once invoked), scanned A, a copy of deep-research, MIT.

A research workflow for investigating questions using multiple web searches, source checks, and cited conclusions.

In plain words
What is it for?
Use it for market, industry, technical, competitive, and other research that needs evidence and a Markdown report.
Why use it?
It reduces the risk of relying on one source or presenting uncertain information as fact. It also records confidence and points out disagreements between sources.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/hssh8917/cc-skills/deep-research
Any agent
npx skills add hssh8917/cc-skills --skill deep-research
Clone the repo
git clone --depth 1 https://github.com/hssh8917/cc-skills

Made for: Claude Code, Codex.

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/hssh8917/cc-skills/deep-research.svg)](https://agentmods.dev/skills/hssh8917/cc-skills/deep-research)
Your own site
<a href="https://agentmods.dev/skills/hssh8917/cc-skills/deep-research"><img src="https://agentmods.dev/badge/skills/hssh8917/cc-skills/deep-research.svg" alt="Measured on agentmods" height="20"></a>
Per session 316 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,915 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin 92% copy Near-identical to another mod 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 $0.00316 $0.02915
Opus 5 $0.00158 $0.01458
Sonnet 5 $0.00063 $0.00583
Haiku 4.5 $0.00032 $0.00292

Measured 4d ago against content hash 5268278594db, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 4d 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

This is a copy

92% identical to deep-research — 39 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/deep-research/SKILL.md · 217 lines

How it starts

The opening of the file, as written. The whole thing — 217 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: Use ultrathink for Step 5 synthesis (standard and deep modes). Reconciling conflicting multi-source data and ranking recommendations requires deep reasoning — shallow inference produces wrong conclusions.

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

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 1–5
Deep Comprehensive review, critical decision; user says "thorough", "exhaustive", "comprehensive" Steps 1–5 + 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").

Critical rules

  • Web search is REQUIRED. If WebSearch is unavailable, 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 · 217 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. 4d ago First seen · 217 lines · 316 tokens per session scan A 5268278594db

Subscribe to this mod's changes

deep-research is a skill published in the GitHub repository hssh8917/cc-skills (2 stars, last pushed yesterday), licensed MIT. It adds 316 tokens to every session and 2,915 once invoked, about $0.0016 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 92% identical to deep-research, differing in 39 lines, and is treated as a copy.

Related

Other skills, from other repositories

omh-legal-compliance-review

This is a Hermes-native legal-compliance-review workflow skill.

rlaope/oh-my-hermes · 64 tokens

novel-compliance

网文敏感词规避与合规。当用户需要了解平台审核规则、规避敏感词、确保内容合规、了解违规判定标准时触发。覆盖各平台审核规则、敏感词库、违规内容判定、章节审核流程、2026年法规要求。.

uu201/character-arc · 69 tokens

comply

Regulatory compliance and audit agent. Maps business regulatory requirements (SOC2/PCI-DSS/HIPAA/ISO 27001), checks control implementations, designs audit trails, and implements Policy as Code. Use when compliance auditing is needed.

seaworld008/Commonly-used-high-value-skills · 51 tokens

levy

Domain knowledge agent for Japanese income tax filing (kakutei shinkoku). Guides income classification, deduction optimization, tax calculation, and filing for freelancers, sole proprietors, and side-business earners. Does not write code.

seaworld008/Commonly-used-high-value-skills · 50 tokens

information-security-manager-iso27001

ISO 27001 ISMS implementation and cybersecurity governance for HealthTech and MedTech companies. Use for ISMS design, security risk assessment, control implementation, ISO 27001 certification, security audits, incident response, and compliance verification. Covers ISO 27001, ISO 27002, healthcare security, and medical…

seaworld008/Commonly-used-high-value-skills · 74 tokens

advisor-ethicist

Use when weighing ethical implications, assessing stakeholder impact, evaluating responsible AI practices, or considering long-term consequences. Analyze decisions through an ethical lens — evaluate who benefits and who bears risk, assess reputation trajectory, and apply moral frameworks.

backchainai/backchain-plugins · 51 tokens