Argo Search and Verification
taxueseek/argo
Unified web search, page fetching and evidence checking across hundreds of sources, with result verification, a research-dossier mode and vertical search engines.
中文深度调研工具。基于 academic-deep-research fork,针对中文场景优化:自动生成中文 PDF(内嵌样式、容错降级)、Tavily 搜索集成、飞书自动交付。适用于竞品分析、行业调研、政策研究等需要严格方法论的场景。使用 native OpenClaw 工具(websearch, webfetch, sessionsspawn)进行多源调研。
$ npx skills add LeoYeAI/openclaw-master-skills --skill deep-research-zh -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills deep-research-zh --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/deep-research-zh .claude/skills/deep-research-zh && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "deep-research-zh" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deep-research-zh into .claude/skills/deep-research-zh/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-zh", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deep-research-zhType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill deep-research-zh -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills deep-research-zh --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/deep-research-zh .agents/skills/deep-research-zh && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "deep-research-zh" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deep-research-zh into .agents/skills/deep-research-zh/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-zh", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill deep-research-zh -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills deep-research-zh --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/deep-research-zh .cursor/skills/deep-research-zh && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "deep-research-zh" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deep-research-zh into .cursor/skills/deep-research-zh/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-zh", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/deep-research-zh--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill deep-research-zh -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills deep-research-zh --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/deep-research-zh .gemini/skills/deep-research-zh && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "deep-research-zh" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deep-research-zh into .gemini/skills/deep-research-zh/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-zh", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills deep-research-zhInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill deep-research-zh -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/deep-research-zh .github/skills/deep-research-zh && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "deep-research-zh" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deep-research-zh into .github/skills/deep-research-zh/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-zh", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill deep-research-zh -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills deep-research-zh --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/deep-research-zh .opencode/skills/deep-research-zh && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "deep-research-zh" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/deep-research-zh into .opencode/skills/deep-research-zh/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research-zh", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
deep-research-zh中文深度调研工具。基于 academic-deep-research fork,针对中文场景优化:自动生成中文 PDF(内嵌样式、容错降级)、Tavily 搜索集成、飞书自动交付。适用于竞品分析、行业调研、政策研究等需要严格方法论的场景。使用 native OpenClaw 工具(websearch, webfetch, sessionsspawn)进行多源调研。
Deep Research Zh is an agent skill from LeoYeAI/openclaw-master-skills. 中文深度调研工具。基于 academic-deep-research fork,针对中文场景优化:自动生成中文 PDF(内嵌样式、容错降级)、Tavily 搜索集成、飞书自动交付。适用于竞品分析、行业调研、政策研究等需要严格方法论的场景。使用 native OpenClaw 工具(websearch, webfetch, sessionsspawn)进行多源调研。
Its SKILL.md is about 6.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts (for example `README.md`, `_meta.json` and `example.md`).
It sits in Research & Science, covering Deep research and Web search. It works with Tavily. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Shell), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
doi.orgFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Deep Research Zh loads about 6.5k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 2,083 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 2,083 words, ~6,453 tokens.
.claude/skills/deep-research-zh/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.You are a methodical research assistant who conducts exhaustive investigations through required research cycles. Your purpose is to build comprehensive understanding through systematic investigation.
⚠️ This skill requires AUTOMATIC PDF delivery. Do NOT stop after completing research.
After completing the final report (Phase 4), you MUST automatically execute:
~/openclaw/workspace/research/[topic]-[YYYY-MM-DD].mdscripts/md2pdf.sh report.md report.pdfmessage(action="send", channel="feishu", target="<user_id>", path="report.pdf")Why This Matters:
Common Mistakes:
Use /research or trigger this skill when:
| Tool | Purpose | Configuration |
|---|---|---|
web_search | Broad context gathering | count=20 for comprehensive coverage |
web_fetch | Deep extraction from specific sources | Use for detailed page analysis |
sessions_spawn | Parallel research tracks | For investigating multiple themes simultaneously |
memory_search / memory_get | Cross-reference prior knowledge | Check MEMORY.md for related context |
Before any research begins:
Ask 2-3 essential clarifying questions:
Reflect understanding back to user:
Wait for response before proceeding.
REQUIRED: Present the complete research plan directly to the user:
List 3-5 major themes for investigation. For each theme:
| Step | Action | Tool | Expected Output |
|---|---|---|---|
| 1 | [Action description] | web_search/web_fetch | [What you'll capture] |
| 2 | ... | ... | ... |
Wait for explicit user approval before proceeding to Phase 3.
REQUIRED: Complete ALL steps for EACH major theme identified.
MINIMUM REQUIREMENTS:
Step 1: Broad Search
web_search with count=20 for comprehensive coverageStep 2: Deep Analysis Synthesize initial findings using your reasoning capabilities:
Document the thinking process explicitly:
Step 3: Gap Identification Document:
Step 1: Targeted Deep Search & Fetch
web_search targeting identified gaps specificallyweb_fetch on primary sources for deep extractionfreshness parameter for recent developments if neededStep 2: Comprehensive Analysis Test and refine understanding using your reasoning capabilities:
Show clear thinking progression:
Step 3: Knowledge Synthesis Establish:
After EACH tool call, you MUST show your work:
Connect new findings to previous results:
Show evolution of understanding:
Highlight pattern changes:
Address contradictions:
Build coherent narrative:
REQUIRED ORDER:
web_search for landscape (count=20)web_fetch on primary sources for depthCritical: Always analyze between tool usage. Document your reasoning explicitly.
After completing all theme cycles:
Connect findings across sources:
Identify emerging patterns:
Challenge contradictions:
Map relationships between discoveries:
Form unified understanding:
When research encounters obstacles, follow this protocol:
| Phase | Tables Allowed | Lists Allowed | Format |
|---|---|---|---|
| Phase 1 (Engagement) | No | No (in response) | Conversational prose |
| Phase 2 (Planning) | Yes | Yes | Structured presentation for clarity |
| Phase 3 (Execution) | Internal notes only | Internal notes only | Your analysis can use structure |
| Phase 4 (Final Report) | No | No | Strict narrative prose only |
Phase 2 Exception: Research Planning uses tables and lists intentionally — this is the one phase where structured presentation aids clarity. The user reviews and approves this plan before execution.
Recent research has demonstrated that GLP-1 agonists are associated with
significant reductions in lean mass (Johnson et al., 2023).
Multiple meta-analyses have confirmed that resistance training combined
with adequate protein intake is more effective for preserving muscle mass
than either intervention alone (Smith, 2020; Williams & Thompson, 2021;
Garcia et al., 2022).
Studies indicate that approximately 40-60% of weight loss from GLP-1
treatment may come from lean mass (Johnson et al., 2023, p. 1831).Garcia, J., Martinez, A., & Lee, S. (2022). Resistance training protocols
for muscle preservation during weight loss: A systematic review and
meta-analysis. Journal of Exercise Science, 15(3), 245-267.
https://doi.org/10.xxxx/jes.2022.15.3.245
Johnson, K. L., Wilson, P., Anderson, R., & Thompson, M. (2023). Body
composition changes associated with GLP-1 receptor agonist treatment:
A comprehensive analysis. Diabetes Care, 46(8), 1823-1842.
https://doi.org/10.xxxx/dc.2023.46.8.1823
Smith, R. (2020). Protein requirements for muscle preservation during
caloric restriction: Current evidence and practical recommendations.
American Journal of Clinical Nutrition, 112(4), 879-895.
https://doi.org/10.xxxx/ajcn.2020.112.4.879Citation Rules:
For independent themes, use sessions_spawn to research in parallel. This is appropriate when themes don't depend on each other's findings.
Step 1: Spawn Sub-Agents for Each Theme
Theme A (Market Landscape):
→ sessions_spawn(
task="Research AI coding assistant market landscape. Complete 2 cycles:
Cycle 1: web_search count=20 on market share, key players, trends.
Analyze findings, identify gaps.
Cycle 2: web_fetch on top 5 sources, deep dive on contradictions.
Return: Key findings, confidence levels, gaps remaining, source list."
)
Theme B (Security):
→ sessions_spawn(
task="Research security & compliance for AI coding assistants. Complete 2 cycles:
Cycle 1: web_search count=20 on SOC 2, HIPAA, data handling.
Analyze findings, identify gaps.
Cycle 2: web_fetch on security whitepapers, compliance docs.
Return: Key findings, confidence levels, gaps remaining, source list."
)Step 2: Synthesize Results
When all sub-agents complete, integrate their findings:
Important: Sub-agents run in isolation. They cannot see each other's work. You must explicitly pass any cross-cutting context in their task descriptions.
Before starting research, check for relevant prior knowledge:
→ memory_search(query="previous research on [topic]")
→ memory_get(path="memory/YYYY-MM-DD.md") [if relevant date found]Use prior findings to:
Present a cohesive research paper. The report must read as a complete academic narrative with proper paragraphs, transitions, and integrated evidence.
⚠️ WARNING: Research is INCOMPLETE until PDF is delivered. This is NOT optional.
After completing the final report text, you MUST automatically execute these steps WITHOUT user prompting or permission:
# Save to research directory with date
~/openclaw/workspace/research/[topic]-[YYYY-MM-DD].md#### ✅ Step 2: Convert to PDF
```bash
SKILL_DIR="$(dirname "$(readlink -f "$0")")"
|| SKILL_DIR="$(dirname "$0")/.."
$SKILL_DIR/scripts/md2pdf.sh /path/to/report.md /path/to/report.pdfmessage({
action: "send",
channel: "feishu", // or current channel
target: "<user_id>",
path: "/path/to/report.pdf",
caption: "Research report delivered"
})Brief message to user: "PDF 已发送 ✅"
# Research Report: [Topic]
## Executive Summary
Two to three substantial paragraphs that capture the core research question,
primary findings, and overall significance. This section provides readers
with a clear understanding of what was investigated and what conclusions
were reached, along with the confidence level attached to those conclusions.
---
## Knowledge Development
This section traces how understanding evolved through the research process,
beginning with initial assumptions and documenting how they were challenged,
refined, or confirmed as investigation proceeded. The narrative addresses
key turning points where new evidence shifted perspective, describes how
uncertainties were either resolved or acknowledged as persistent limitations,
and reflects on the challenges encountered during the research process.
Particular attention is paid to how confidence in various claims changed
as additional sources were examined and cross-referenced, demonstrating
the iterative nature of building comprehensive understanding through
systematic investigation.
---
## Comprehensive Analysis
### Primary Findings and Their Implications
The core findings of the research are presented here as a flowing narrative
that addresses the central research question. Each significant discovery
is explored in depth with supporting evidence integrated naturally into
the prose. The implications of these findings are analyzed with attention
to their significance within the broader context of the field, connecting
individual discoveries to larger patterns and trends.
### Patterns and Trends Across Research Phases
This subsection examines the meta-patterns that emerged only through the
synthesis of multiple research phases. The trajectory of the field or topic
is analyzed, showing how individual findings coalesce into larger movements
and identifying which trends appear robust versus which may be ephemeral.
### Contradictions and Competing Evidence
Where sources conflict, those contradictions are presented fairly and
analyzed thoroughly. The discussion addresses potential reasons for
disagreement, such as differences in methodology, sample populations,
or time periods. Evidence quality on each side of conflicts is assessed,
and instances where contradictions remain unresolved are documented
transparently.
### Strength of Evidence for Major Conclusions
For each major conclusion, the quantity and quality of supporting sources
is evaluated. The consistency of evidence across sources is examined,
and limitations in the available evidence are discussed openly.
### Limitations and Gaps in Current Knowledge
This subsection acknowledges what remains unknown despite thorough
investigation. Weaknesses in available evidence are identified, areas
where research is preliminary are noted, and questions that emerged
during research but remain unanswered are documented.
### Integration of Findings Across Themes
The connections between themes are explored here, demonstrating how
separate lines of investigation reinforce and illuminate each other.
The unified understanding that emerges from synthesis is presented,
identifying systemic insights that only became visible through
cross-theme analysis.
---
## Practical Implications
### Immediate Practical Applications
Concrete and actionable recommendations based on the research findings
are presented here. Specific guidance is offered for practitioners,
decision-makers, or researchers who wish to apply these findings in
real-world contexts.
### Long-Term Implications and Developments
The discussion addresses how the findings may shape the field going
forward, identifying emerging trends that may become significant and
potential paradigm shifts that could result from this research.
### Risk Factors and Mitigation Strategies
Risks associated with the findings or their application are identified,
and evidence-based mitigation approaches are proposed.
### Implementation Considerations
Practical factors for applying the findings are addressed, including
resource requirements, timeline considerations, prerequisites, and
potential barriers to implementation.
### Future Research Directions
Questions that remain unanswered after this investigation are
documented, along with methodological improvements needed and
promising avenues for further investigation.
### Broader Impacts and Considerations
The societal, ethical, or systemic implications of the findings
are explored, along with connections to other fields or domains
and unintended consequences that should be considered.
---
## References
[Full APA-formatted reference list in alphabetical order by first author's
surname. Every in-text citation must appear here with complete bibliographic
information including hanging indentation.]
---
## Appendices (if needed)
### Appendix A: Search Strategy
Search queries used for each theme along with databases and sources
consulted, with dates of search clearly documented.
### Appendix B: Source Reliability Assessment
Evaluation criteria used to assess sources with ratings for major
references included in the research.
### Appendix C: Excluded Sources
Sources that were reviewed but ultimately not cited in the final
report, with explanations for their exclusion.
### Appendix D: Research Timeline
Chronology of the investigation with key milestones in the research
process documented.Format:
Content:
Style:
Citations:
© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 5 other files (scripts) in skills/deep-research-zh of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Deep Research Zh next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Deep Research Zh this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~6.5k | Automated safety check: Pass | MIT | |
| Argo Search and Verificationtaxueseek/argo | 188 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Multi Source Searchsandbaseai/sandbase-skills | 203 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Research BriefOpenHands/extensions | 163 | — | ~831 | Automated safety check: Pass | MIT | |
| Tavily Web Searchallenpeng0705/EnvoyMesh | 3.1k | 3 repos | ~2.5k | Automated safety check: Notes | None | |
| AI RAG PipelineNeverSight/learn-skills.dev | 217 | 1 repos | ~2k | Automated safety check: Pass | None |
taxueseek/argo
Unified web search, page fetching and evidence checking across hundreds of sources, with result verification, a research-dossier mode and vertical search engines.
sandbaseai/sandbase-skills
Portable multi-source research with cross-source validation and an offline evidence ledger.
OpenHands/extensions
Create an automation that writes a recurring research brief.
allenpeng0705/EnvoyMesh
Searches the web through the Tavily API with LLM-friendly output: clean structured results, optional AI-written answers, domain filters, news mode, images and raw content.
NeverSight/learn-skills.dev
Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs.
sandbaseai/sandbase-skills
Advanced web search, content extraction, and site mapping through Tavily via SandBase.
LeoYeAI/openclaw-master-skills
Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.
LeoYeAI/openclaw-master-skills
Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.
LeoYeAI/openclaw-master-skills
Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.
LeoYeAI/openclaw-master-skills
Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.
LeoYeAI/openclaw-master-skills
Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.
LeoYeAI/openclaw-master-skills
Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
Works with
Categories
中文深度调研工具。基于 academic-deep-research fork,针对中文场景优化:自动生成中文 PDF(内嵌样式、容错降级)、Tavily 搜索集成、飞书自动交付。适用于竞品分析、行业调研、政策研究等需要严格方法论的场景。使用 native OpenClaw 工具(websearch, webfetch, sessionsspawn)进行多源调研。. Deep Research Zh is an agent skill from LeoYeAI/openclaw-master-skills.
Deep Research Zh fits situations like: tasks that involve Deep research; tasks that involve Web search.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill deep-research-zh -a claude-code`. Or copy the skill folder (skills/deep-research-zh in LeoYeAI/openclaw-master-skills) into .claude/skills/deep-research-zh in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill deep-research-zh -a codex`. Or copy the skill folder (skills/deep-research-zh in LeoYeAI/openclaw-master-skills) into .agents/skills/deep-research-zh in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill deep-research-zh -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deep-research-zh, .gemini/skills/deep-research-zh, .github/skills/deep-research-zh and .opencode/skills/deep-research-zh in your project.
Going by SKILL.md and its folder, Deep Research Zh needs a shell for the scripts in its folder. Our summary lists: A Bash shell.
SKILL.md names 1 domain. In commands or code: doi.org; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Deep Research Zh is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 6.5k tokens (SKILL.md is roughly 26k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Deep Research Zh: Argo Search and Verification (taxueseek/argo, 188 stars), Multi Source Search (sandbaseai/sandbase-skills, 203 stars), Research Brief (OpenHands/extensions, 163 stars) and Tavily Web Search (allenpeng0705/EnvoyMesh, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.