Deep-Research-skills: Structured deep research skill for Claude Code/Open Code/Codex with human-in-the-loop control

by Weizhena · Claude Skill · ★ 2.0k

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

How to install Deep-Research-skills

git clone https://github.com/Weizhena/deep-research-skills.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 73/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

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

Quick Facts

Stars2,001
Forks166
LanguagePython
CategoryClaude Skill
LicenseMIT
Quality Score73/100
Open Issues1
Last Updated2026-08-23
Created2025-12-29
Platformsclaude-code, codex, python
Est. Tokens~14k

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...

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%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

View on GitHub → Browse Claude Skill tools