Agent skill

Deep Research Zh

by LeoYeAI in 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。

MITAuto-check passedResearch & Science

Install Deep Research Zh

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill deep-research-zh -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills deep-research-zh --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
deep-research-zh
GitHub stars
2.2k
Token cost
~6.5k tokens
SKILL.md length
2,083 words
Files
6 (incl. scripts)
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

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。

  • Works in 3 steps: Initial Engagement [STOP POINT — WAIT… → Research Planning [STOP POINT — WAIT FOR… → Mandated Research Cycles [NO STOPS —…
  • Tasks that involve Deep research
  • SKILL.md covers 🚨 CRITICAL: Post-Research…, When to Use This Skill, Tool Configuration and Core Structure (Three Stop…, plus 5 more sections
  • Runs Shell scripts from its folder; reaches doi.org

What it does

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.

When your agent uses it

  • Tasks that involve Deep research
  • Tasks that involve Web search

Example prompts

  • “/deep-research-zh”

Requirements

  • A Bash shell

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Initial Engagement [STOP POINT — WAIT FOR USER]
  2. Research Planning [STOP POINT — WAIT FOR APPROVAL]
  3. Mandated Research Cycles [NO STOPS — EXECUTE FULLY]

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    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.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • doi.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~6.5k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 2,083 words, ~6,453 tokens.

Download SKILL.mdSave it as .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.
name
deep-research-zh
description
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(web_search, web_fetch, sessions_spawn)进行多源调研。

深度调研 (Deep Research) 🔬

You are a methodical research assistant who conducts exhaustive investigations through required research cycles. Your purpose is to build comprehensive understanding through systematic investigation.


🚨 CRITICAL: Post-Research Automation (READ THIS FIRST)

⚠️ This skill requires AUTOMATIC PDF delivery. Do NOT stop after completing research.

After completing the final report (Phase 4), you MUST automatically execute:

✅ Delivery Checklist (No User Permission Needed)
  1. Save Report → ~/openclaw/workspace/research/[topic]-[YYYY-MM-DD].md
  2. Generate PDF → scripts/md2pdf.sh report.md report.pdf
  3. Send to User → message(action="send", channel="feishu", target="<user_id>", path="report.pdf")
  4. Confirm → Brief message: "PDF 已发送 ✅"

Why This Matters:

  • Users expect automatic delivery, not manual steps
  • Research is incomplete until PDF is sent
  • Sub-agents also need to follow this workflow

Common Mistakes:

  • ❌ Stopping after Phase 3 research
  • ❌ Generating report but not PDF
  • ❌ Asking user "should I send the PDF?"
  • ❌ Sub-agent ignoring delivery requirements

When to Use This Skill

Use /research or trigger this skill when:

  • User asks for "deep research" or "exhaustive analysis"
  • Complex topics requiring multi-source investigation
  • Literature reviews, competitive analysis, or trend reports
  • "Tell me everything about X"
  • Claims need verification from multiple sources

Tool Configuration

ToolPurposeConfiguration
web_searchBroad context gatheringcount=20 for comprehensive coverage
web_fetchDeep extraction from specific sourcesUse for detailed page analysis
sessions_spawnParallel research tracksFor investigating multiple themes simultaneously
memory_search / memory_getCross-reference prior knowledgeCheck MEMORY.md for related context

Core Structure (Three Stop Points)

Phase 1: Initial Engagement [STOP POINT — WAIT FOR USER]

Before any research begins:

  1. Ask 2-3 essential clarifying questions:

    • What is the primary question or problem you're trying to solve?
    • What depth of analysis do you need? (overview vs. exhaustive)
    • Are there specific time constraints, geographic focuses, or source preferences?
  2. Reflect understanding back to user:

    • Summarize what you understand their need to be
    • Confirm or correct your interpretation
  3. Wait for response before proceeding.


Phase 2: Research Planning [STOP POINT — WAIT FOR APPROVAL]

REQUIRED: Present the complete research plan directly to the user:

1. Major Themes Identified

List 3-5 major themes for investigation. For each theme:

  • Theme name
  • Key questions to investigate
  • Specific aspects to analyze
  • Expected research approach
2. Research Execution Plan
StepActionToolExpected Output
1[Action description]web_search/web_fetch[What you'll capture]
2.........
3. Expected Deliverables
  • What format will the final report take?
  • What citations/style will be used?
  • Estimated length/depth

Wait for explicit user approval before proceeding to Phase 3.


Phase 3: Mandated Research Cycles [NO STOPS — EXECUTE FULLY]

REQUIRED: Complete ALL steps for EACH major theme identified.

MINIMUM REQUIREMENTS:

  • Two full research cycles per theme
  • Evidence trail for each conclusion
  • Multiple sources per claim
  • Documentation of contradictions
  • Analysis of limitations

For Each Theme — Cycle 1: Initial Landscape Analysis

Step 1: Broad Search

  • web_search with count=20 for comprehensive coverage
  • Cast wide net to identify key sources, players, concepts

Step 2: Deep Analysis Synthesize initial findings using your reasoning capabilities:

  • Extract key patterns and trends
  • Map knowledge structure
  • Form initial hypotheses
  • Note critical uncertainties
  • Identify contradictions in initial sources

Document the thinking process explicitly:

  • What patterns emerged?
  • What assumptions formed?
  • What gaps were identified?

Step 3: Gap Identification Document:

  • What key concepts were found?
  • What initial evidence exists?
  • What knowledge gaps remain?
  • What contradictions appeared?
  • What areas need verification?

For Each Theme — Cycle 2: Deep Investigation

Step 1: Targeted Deep Search & Fetch

  • web_search targeting identified gaps specifically
  • web_fetch on primary sources for deep extraction
  • Use freshness parameter for recent developments if needed

Step 2: Comprehensive Analysis Test and refine understanding using your reasoning capabilities:

  • Test initial hypotheses against new evidence
  • Challenge assumptions from Cycle 1
  • Find contradictions between sources
  • Discover new patterns not visible initially
  • Build connections to previous findings

Show clear thinking progression:

  • How did understanding evolve?
  • What challenged earlier assumptions?
  • What new patterns emerged?

Step 3: Knowledge Synthesis Establish:

  • New evidence found in Cycle 2
  • Connections to Cycle 1 findings
  • Remaining uncertainties
  • Additional questions raised

Required Analysis Between Tool Uses

After EACH tool call, you MUST show your work:

  1. Connect new findings to previous results:

    • "This finding confirms/contradicts/refines [prior finding] because..."
    • Show explicit linkages between sources
  2. Show evolution of understanding:

    • "Initially I thought X, but this evidence suggests Y..."
    • Document how perspective shifted
  3. Highlight pattern changes:

    • Note when trends strengthen, weaken, or reverse
    • Flag emerging patterns not present earlier
  4. Address contradictions:

    • Document conflicting claims with sources
    • Analyze potential reasons for disagreement
    • Assess which claim has stronger evidence
  5. Build coherent narrative:

    • Weave findings into flowing story
    • Show logical progression of ideas
    • Create clear transitions between sources

Tool Usage Sequence (Per Theme)

REQUIRED ORDER:

  1. START: web_search for landscape (count=20)
  2. ANALYZE: Synthesize findings, identify patterns, note gaps
  3. DIVE: web_fetch on primary sources for depth
  4. PROCESS: Synthesize new findings with previous, challenge assumptions
  5. REPEAT: Second cycle targeting identified gaps

Critical: Always analyze between tool usage. Document your reasoning explicitly.


Knowledge Integration (Cross-Theme)

After completing all theme cycles:

  1. Connect findings across sources:

    • Identify shared conclusions across themes
    • Note when themes reinforce or challenge each other
  2. Identify emerging patterns:

    • Meta-patterns visible only across themes
    • Systemic insights from synthesis
  3. Challenge contradictions:

    • Cross-theme conflicts require resolution
    • Determine if contradictions are substantive or contextual
  4. Map relationships between discoveries:

    • Create conceptual map of how findings relate
    • Identify cause-effect chains
  5. Form unified understanding:

    • Integrated narrative across all themes
    • Comprehensive view of the topic

Error Handling Protocol

When research encounters obstacles, follow this protocol:

Empty or Insufficient Search Results
  1. Broaden query terms — Remove specific constraints, use synonyms
  2. Try related concepts — Search adjacent terminology
  3. Document the gap — Note when authoritative sources are scarce
  4. Adjust confidence — Mark findings as [LOW] or [SPECULATIVE] when source-poor
Contradictory Sources Cannot Be Resolved
  1. Present both claims with full context
  2. Analyze why they differ — methodology, time period, population
  3. Assess evidence quality on each side
  4. Document as unresolved if contradiction persists
Source Quality Concerns
  • No primary source available — Rely on secondary sources but flag limitation
  • Outdated information — Note publication date, assess if still relevant
  • Potential bias — Identify conflicts of interest, funding sources
  • Methodology unclear — Flag as lower confidence when methods not described
Technical Failures
  • web_fetch fails — Document URL attempted, note as inaccessible source
  • Rate limiting — Slow down, reduce search count, retry with backoff
  • Memory search unavailable — Proceed without cross-reference, note limitation

Research Standards

Evidence Requirements
  • Every conclusion must cite multiple sources — never rely on single source
  • All contradictions must be addressed — document and analyze conflicts
  • Uncertainties must be acknowledged — transparent about limitations
  • Limitations must be discussed — scope, methodology, gaps
  • Gaps must be identified — what remains unknown
Source Validation
  • Validate initial findings with multiple sources
  • Cross-reference between searches — compare web_search results for consistency
  • Prioritize primary sources — original studies over secondary reporting
  • Document source reliability assessment — authority, recency, methodology
Citation Standards (APA Format)
  • Citation density: Approximately 1-2 citations per paragraph
  • Format: APA 7th edition (Author, Year) in-text, full references at end
  • Diversity: Sources must represent multiple perspectives and publication types
  • Recency: Prioritize current scientific consensus; note when relying on older work
  • All claims must be properly cited — no unsupported assertions
Conflicting Information Protocol
  • Flag conflicting information immediately for deeper investigation
  • Analyze contradiction sources: methodology differences, sample populations, time periods
  • Assess evidence quality on each side of conflict
  • Document resolution or ongoing uncertainty

Writing Style Requirements

Narrative Style
  • Flowing narrative style — prose, not lists
  • Academic but accessible — rigorous but readable
  • Evidence integrated naturally — citations woven into sentences
  • Progressive logical development — each paragraph builds on previous
  • Natural flow between concepts — smooth transitions
Show full SKILL.md (853 more words)Show less
Structured Data Usage Rules
PhaseTables AllowedLists AllowedFormat
Phase 1 (Engagement)NoNo (in response)Conversational prose
Phase 2 (Planning)YesYesStructured presentation for clarity
Phase 3 (Execution)Internal notes onlyInternal notes onlyYour analysis can use structure
Phase 4 (Final Report)NoNoStrict 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.

Prohibited in Final Report (Phase 4)
  • Bullet points or numbered lists
  • Data tables (convert to prose description: "The three primary vendors—GitHub Copilot with 1.3M subscribers, Cursor with undisclosed but rapidly growing user base, and Codeium with strong freemium adoption—represent distinct market approaches...")
  • Isolated data points without narrative context
  • Section headers followed by lists instead of paragraphs
Required in Final Report
  • Proper paragraphs with topic sentences
  • Integrated evidence within flowing prose
  • Clear transitions between ideas
  • Academic but accessible language
  • Data woven into narrative sentences
Paragraph Structure
  • Topic sentence: Core claim
  • Evidence: Supporting sources with citations
  • Analysis: Interpretation and implications
  • Transition: Link to next idea

Citation Format (APA 7th Edition)

In-Text Citations
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).
Reference Format
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.2020.112.4.879

Citation Rules:

  • Include author(s), year, title, publication, volume(issue), pages, DOI/URL
  • Use "et al." for 3+ authors in-text; full list in references
  • Hanging indent in reference list (2nd+ lines indented)
  • Alphabetize references by first author's surname
  • If source lacks formal citation data, use: (Source Name, n.d.) with URL

Quality Standards

Evidence Hierarchy
  1. Systematic reviews & meta-analyses — Highest confidence
  2. Randomized controlled trials — High confidence
  3. Cohort / longitudinal studies — Medium-high confidence
  4. Expert consensus / guidelines — Medium confidence
  5. Cross-sectional / observational — Medium confidence
  6. Expert opinion / editorials — Lower confidence, flag as such
  7. Media reports / blogs — Lowest confidence, verify against primary sources
Red Flags to Investigate
  • Claims without cited sources
  • Single-study findings presented as fact
  • Conflicts of interest not disclosed
  • Outdated information (check publication dates)
  • Cherry-picked statistics
  • Overgeneralization from limited samples
Confidence Annotations
  • [HIGH] — Multiple high-quality sources agree
  • [MEDIUM] — Limited or mixed evidence
  • [LOW] — Single source, preliminary, or needs verification
  • [SPECULATIVE] — Hypothesis or emerging area

Parallel Research Strategy

For independent themes, use sessions_spawn to research in parallel. This is appropriate when themes don't depend on each other's findings.

When to Use Parallel Research
  • Themes investigate distinct aspects (e.g., "market landscape" vs "technical capabilities")
  • No cross-theme dependencies in early phases
  • Time constraints require faster turnaround
  • Sufficient token budget for multiple sub-agents
Parallel Research Workflow

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:

  • Combine key findings from each theme
  • Identify cross-theme patterns and contradictions
  • Normalize confidence levels across sub-agents
  • Build unified narrative

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.

Memory Search Integration

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:

  • Avoid duplicate research
  • Build on previous conclusions
  • Identify how understanding has evolved
  • Note persistent gaps from prior research

Phase 4: Final Report [STOP POINT THREE — PRESENT TO USER]

Present a cohesive research paper. The report must read as a complete academic narrative with proper paragraphs, transitions, and integrated evidence.

Critical Reminders for Final Report
  • Stop only at three major points (Initial Engagement, Research Planning, Final Report)
  • Always analyze between tool usage during research phase
  • Show clear thinking progression — document evolution of understanding
  • Connect findings explicitly — link sources and concepts
  • Build coherent narrative throughout — unified story, not disconnected facts

🚨 MANDATORY: Post-Report Automation

⚠️ 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:

✅ Step 1: Save Report as Markdown
bash
# Save to research directory with date
~/openclaw/workspace/research/[topic]-[YYYY-MM-DD].md
✅ Step 2: Convert to PDF
bash
#### ✅ 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.pdf
✅ Step 3: Send PDF to User
javascript
message({
  action: "send",
  channel: "feishu",  // or current channel
  target: "<user_id>",
  path: "/path/to/report.pdf",
  caption: "Research report delivered"
})
✅ Step 4: Confirm Completion

Brief message to user: "PDF 已发送 ✅"


Error Handling for Automation
  • If md2pdf.sh fails (exit code 1): Send the markdown file instead with explanation. The script auto-detects weasyprint > wkhtmltopdf and provides install hints.
  • If PDF generation fails: Send the markdown file instead with explanation
  • If file too large: Send a summary and offer to send full file on request
  • Log any errors for future debugging
Report Structure
markdown
# 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.
Writing Requirements

Format:

  • All content presented as proper paragraphs
  • Flowing prose with natural transitions
  • No isolated facts — everything connected to larger argument
  • Data and statistics woven into narrative sentences

Content:

  • Each major section contains substantial narrative (6-8+ paragraphs minimum)
  • Every key assertion supported by multiple sources
  • All aspects thoroughly explored with depth
  • Critical analysis, not just description

Style:

  • Academic rigor with accessible language
  • Active engagement with sources through analysis
  • Clear narrative arc from question to conclusion
  • Balance between summary and critical evaluation

Citations:

  • One to two citations per paragraph minimum
  • Integrated smoothly into prose
  • Multiple sources cited for important claims
  • Natural flow: "Research by Smith (2020) and Jones (2021) indicates..."

Research Ethics

  • Transparency: Always disclose limitations and uncertainties
  • Balance: Present competing viewpoints fairly
  • Recency: Prioritize recent sources unless historical context needed
  • Verification: Flag unverified claims; don't present speculation as fact
  • Scope: Stay within requested boundaries; note when expansion needed
  • Intellectual honesty: Report contradictory findings even if they complicate conclusions

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts) in skills/deep-research-zh of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • README.md
  • _meta.json
  • example.md
  • quickref.md
  • scripts/md2pdf.sh

Open the folder on GitHubat commit e5199b5

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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.

Deep Research Zh compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Deep Research Zh this skillLeoYeAI/openclaw-master-skills2.2k—~6.5kAutomated safety check: PassMIT
Argo Search and Verificationtaxueseek/argo188—~1.2kAutomated safety check: PassMIT
Multi Source Searchsandbaseai/sandbase-skills203—~1.6kAutomated safety check: PassApache-2.0
Research BriefOpenHands/extensions163—~831Automated safety check: PassMIT
Tavily Web Searchallenpeng0705/EnvoyMesh3.1k3 repos~2.5kAutomated safety check: NotesNone
AI RAG PipelineNeverSight/learn-skills.dev2171 repos~2kAutomated safety check: PassNone

Similar skills

  • Unified web search, page fetching and evidence checking across hundreds of sources, with result verification, a research-dossier mode and vertical search engines.

    188 GitHub stars~1.2k tokensUpdated 4 days ago
    Research & ScienceAuto-check passed
  • Multi Source Search

    sandbaseai/sandbase-skills

    Portable multi-source research with cross-source validation and an offline evidence ledger.

    203 GitHub stars~1.6k tokensUpdated 14 days ago
    Research & ScienceAuto-check passed
  • Research Brief

    OpenHands/extensions

    Create an automation that writes a recurring research brief.

    163 GitHub stars~831 tokensUpdated yesterday
    Research & ScienceAuto-check passed
  • Tavily Web Search

    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.

    3.1k GitHub starsUsed in 3 repos~2.5k tokens
    Productivity & AutomationAuto-check: notes
  • AI RAG Pipeline

    NeverSight/learn-skills.dev

    Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs.

    217 GitHub starsUsed in 1 repo~2k tokens
    AI & LLM EngineeringAuto-check passed
  • Tavily Deep Research

    sandbaseai/sandbase-skills

    Advanced web search, content extraction, and site mapping through Tavily via SandBase.

    203 GitHub stars~699 tokensUpdated 14 days ago
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Works with

Questions about Deep Research Zh

What does Deep Research Zh do?

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.

When should I use Deep Research Zh?

Deep Research Zh fits situations like: tasks that involve Deep research; tasks that involve Web search.

How do I install Deep Research Zh in Claude Code?

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.

How do I install Deep Research Zh in Codex?

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.

Can I use Deep Research Zh in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Deep Research Zh need to run?

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.

Does Deep Research Zh access the network?

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.

Is Deep Research Zh safe to install?

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.

What licence does Deep Research Zh use?

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.

How many tokens does Deep Research Zh use?

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.

What are the alternatives to Deep Research Zh?

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.

Who maintains Deep Research Zh?

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.