Bmad Deep Recon
delorenj/mcp-server-trello
Decision-grade research, three ways: draft a deep-research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), process a finished research report — file it, distill…
Transparent, rigorous research with full methodology — not a black-box API wrapper.
$ npx skills add LeoYeAI/openclaw-master-skills --skill academic-deep-research -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills academic-deep-research --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/keats-deep-research .claude/skills/academic-deep-research && 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 "academic-deep-research" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/keats-deep-research into .claude/skills/academic-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "academic-deep-research", 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/keats-deep-researchType 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 academic-deep-research -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills academic-deep-research --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/keats-deep-research .agents/skills/academic-deep-research && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "academic-deep-research" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/keats-deep-research into .agents/skills/academic-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "academic-deep-research", 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 academic-deep-research -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills academic-deep-research --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/keats-deep-research .cursor/skills/academic-deep-research && 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 "academic-deep-research" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/keats-deep-research into .cursor/skills/academic-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "academic-deep-research", 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/keats-deep-research--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 academic-deep-research -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills academic-deep-research --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/keats-deep-research .gemini/skills/academic-deep-research && 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 "academic-deep-research" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/keats-deep-research into .gemini/skills/academic-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "academic-deep-research", 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 academic-deep-researchInstalls 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 academic-deep-research -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/keats-deep-research .github/skills/academic-deep-research && 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 "academic-deep-research" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/keats-deep-research into .github/skills/academic-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "academic-deep-research", 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 academic-deep-research -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 academic-deep-research --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/keats-deep-research .opencode/skills/academic-deep-research && 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 "academic-deep-research" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/keats-deep-research into .opencode/skills/academic-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "academic-deep-research", 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.
academic-deep-researchTransparent, rigorous research with full methodology — not a black-box API wrapper.
Academic Deep Research is an agent skill from LeoYeAI/openclaw-master-skills. Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (websearch, webfetch, sessionsspawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.
Its SKILL.md is about 6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `README.md`, `_meta.json` and `example.md`).
It sits in Research & Science, covering Deep research, Competitor analysis and Literature review. It works with Box. 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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
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.
Academic Deep Research loads about 6k tokens when it runs. Until then it costs about 112 tokens; SKILL.md has 1,860 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); files beside SKILL.md are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,860 words, ~5,975 tokens.
.claude/skills/academic-deep-research/SKILL.md (or your agent's skills folder). This skill also uses 4 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.
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.
# 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 4 other files in skills/keats-deep-research of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in LeoYeAI/openclaw-master-skills, which our catalogue first saw on October 7, 2026.
Academic Deep Research 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 |
|---|---|---|---|---|---|---|
| Academic Deep Research this skillLeoYeAI/openclaw-master-skills | 2.2k | 2 repos | ~6k | Automated safety check: Pass | MIT | |
| Bmad Deep Recondelorenj/mcp-server-trello | 445 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Interceptor ResearchHacker-Valley-Media/Interceptor | 522 | — | ~3.8k | Automated safety check: Pass | Custom licence | |
| Live Researchbrightdata/skills | 264 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Deep ResearchFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | 1 repos | ~1.1k | Automated safety check: Pass | None | |
| Ulw Researchrlaope/oh-my-hermes | 3.2k | — | ~4.2k | Automated safety check: Pass | MIT |
delorenj/mcp-server-trello
Decision-grade research, three ways: draft a deep-research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), process a finished research report — file it, distill…
Hacker-Valley-Media/Interceptor
Deep web-research methodology for the interceptor browser surface — investigate a topic the way researchers, intelligence analysts, investigative journalists, private investigators, and OSINT…
brightdata/skills
Produce a deep, multi-source, cited research brief on a topic from live web data using Bright Data's Discover API (intent-ranked web search + parsed page content).
FreedomIntelligence/OpenClaw-Medical-Skills
Execute autonomous multi-step deep research on any topic. An agent skill from FreedomIntelligence/OpenClaw-Medical-Skills.
rlaope/oh-my-hermes
[omh] Deep dive before a decision: deep research engine - grounding for specs and decisions: study open-source reference implementations with pinned refs, gather live web evidence with citation…
wentorai/Research-Claw
Methodical research assistant for exhaustive investigations through systematic research cycles.
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
Transparent, rigorous research with full methodology — not a black-box API wrapper. Academic Deep Research is an agent skill from LeoYeAI/openclaw-master-skills. Transparent, rigorous research with full methodology — not a black-box API wrapper.
Academic Deep Research fits situations like: literature reviews; competitive intelligence; any research requiring academic rigor and reproducibility.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill academic-deep-research -a claude-code`. Or copy the skill folder (skills/keats-deep-research in LeoYeAI/openclaw-master-skills) into .claude/skills/academic-deep-research in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill academic-deep-research -a codex`. Or copy the skill folder (skills/keats-deep-research in LeoYeAI/openclaw-master-skills) into .agents/skills/academic-deep-research 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 academic-deep-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/academic-deep-research, .gemini/skills/academic-deep-research, .github/skills/academic-deep-research and .opencode/skills/academic-deep-research in your project.
SKILL.md names no scripts, command-line tools or credentials: Academic Deep Research is instructions for the agent only.
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. Review the folder before installing.
Academic Deep Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 6k tokens (SKILL.md is roughly 24k 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 Academic Deep Research: Bmad Deep Recon (delorenj/mcp-server-trello, 445 stars), Interceptor Research (Hacker-Valley-Media/Interceptor, 522 stars), Live Research (brightdata/skills, 264 stars) and Deep Research (FreedomIntelligence/OpenClaw-Medical-Skills, 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.