Deep-Research-skills — security grade SAFE, quality 73/100

Security audit verdict: SAFE · quality 73/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by Weizhena · Claude Skill · ★ 2.0k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is Deep-Research-skills safe to install? View the security audit →

About Deep-Research-skills

Deep Research Skill for Claude Code / OpenCode / Codex English | 中文 If you find this project helpful, please give it a star! :star: Inspired by RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context A structured research workflow skill for Claude Code, OpenCode, and Codex, supporting two-phase research: outline generation (extensible) and deep investigation. Human-in-the-loop design ensures precise control at every stage. Use Cases Academic Research: Paper surveys, benchmark reviews, literature analysis Technical Research: Technology comparison, framework evaluation, tool selection Market Research: Competitor analysis, industry trends, product comparison Due Diligence: Company research, investment analysis, risk assessment Installation Claude Code OpenCode (default: gpt-5.4) bash Skills (same as Claude Code) cp -r skills/research-en/ /.clau

claude-codeclaude-code-skillsdeep-research-agentgpt-skillsllm-agentopencodeopencode-skillsresearch

Quick Facts

Stars2,001
Forks166
LanguagePython
CategoryClaude Skill
LicenseMIT
Quality Score72.6925465882316/100
Open Issues1
Last Updated2026-08-23
Created2025-12-29
Platformsclaude-code, codex, python
Est. Tokens~14k

Compatible Skills

These tools work well together with Deep-Research-skills for enhanced workflows:

  • deep-research-mcp — semantic(0.40)+complementary+same_lang+similar_pop+shared_platform (64%)
  • gptr-mcp — semantic(0.35)+complementary+same_lang+similar_pop+shared_platform (62%)
  • Cerno-Agentic-Local-Deep-Research — semantic(0.30)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • sgr-agent-core — semantic(0.29)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)

Deep-Research-skills alternative? Top 6 similar tools

Looking for a Deep-Research-skills alternative? If you're comparing Deep-Research-skills with other claude skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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Frequently Asked Questions

What is Deep-Research-skills?

Deep-Research-skills is Structured deep research skill for Claude Code/Open Code/Codex with human-in-the-loop control. It is categorized as a Claude Skill with 2.0k GitHub stars.

What programming language is Deep-Research-skills written in?

Deep-Research-skills is primarily written in Python. It covers topics such as claude-code, claude-code-skills, deep-research-agent.

How do I install or use Deep-Research-skills?

You can find installation instructions and usage details in the Deep-Research-skills GitHub repository at github.com/Weizhena/Deep-Research-skills. The project has 2.0k stars and 166 forks, indicating an active community.

What license does Deep-Research-skills use?

Deep-Research-skills is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to Deep-Research-skills?

The top alternatives to Deep-Research-skills on Agent Skills Hub include plannotator, claude-squad, openpencil. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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