academic-deep-research
Transparent, rigorous research with full methodology — not a black-box API wrapper.
Skill by LeoYeAI
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: backend, research
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/academic-deep-research
What's inside
- Skill name
academic-deep-research- Description
- 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 (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.
- License
- MIT
- Instructions
- 618 lines in SKILL.md · view on GitHub
Agent Skills spec issues
- body: 618 lines (spec recommends under 500)
Files (4)
- README.md
- SKILL.md
- example.md
- quickref.md
SKILL.md (first lines)
# Academic 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. ## 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 | 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 | ## 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 | Step | Action | Tool | Expected 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
From LeoYeAI/openclaw-master-skills, MIT.
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Signals
- Stars
- 2,168
- Forks
- 325
- Open issues
- 13
- Last commit (this folder)
- Mar 12, 2026 (7 months ago)
- Last release
- Jul 20, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Website
- myclaw.ai
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
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Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
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academic-deep-research skill. Agent Mart. https://agentmart.dev/skills/leoyeai-academic-deep-research-8998d5 (last updated 2026-10-24)