What makes Academic Deep Research notable?

1.2K downloads. Trust evidence available

How should Academic Deep Research be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Academic Deep Research

Rigorous 2-cycle research with APA7 citations. Sub-agents write to files to prevent queue delivery loss.

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.0release · observed Jun 26, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s177tncb2rev33nneh5b4z7mkx84qd3q:academic-deep-research

Install using `clawhub skill install s177tncb2rev33nneh5b4z7mkx84qd3q:academic-deep-research` in an isolated environment before connecting it to live workloads.
No published capability contract is available yet, so validate auth and request/response behavior manually.
Review the upstream CLAWHUB listing at https://clawhub.ai/cxbjames/academic-deep-research before using production credentials.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-cxbjames-academic-deep-research/snapshot"

Documentation

CLAWHUB

46,621 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: "academic-deep-research"
description: "Rigorous 2-cycle research with APA7 citations. Sub-agents write to files to prevent queue delivery loss."
homepage: https://github.com/kesslerio/academic-deep-research-clawhub-skill
metadata:
  openclaw:
    emoji: 🔬
---

# 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

README.md

# Academic Deep Research 🔬

**Transparent, rigorous, self-contained research** — not a black-box API wrapper.

## Why This Skill Exists

Most "deep research" tools are wrappers around external APIs. You send a query, get a report, and have no idea what happened in between.

**This skill is different:**
- ✅ **Full methodology visible** — Every step documented, reproducible
- ✅ **No external dependencies** — Runs entirely on OpenClaw native tools
- ✅ **User control** — 3 explicit checkpoints for approval
- ✅ **Academic rigor** — APA citations, evidence hierarchy, confidence levels
- ✅ **Works offline** — No API keys, no cloud services

## Comparison with Cloud-Based Research Tools

| Feature | This Skill | Cloud API Wrappers |
|---------|------------|-------------------|
| Methodology | Fully documented | Black box |
| Dependencies | None | External API + key |
| Offline | ✅ Yes | ❌ No |
| User Checkpoints | 3 approval points | Usually none |
| Citation Format | APA 7th edition | Varies/unspecified |
| Evidence Hierarchy | Explicit (meta-analyses → opinion) | Unspecified |
| Output Control | Strict prose, no bullet points | Varies |
| Reproducibility | ✅ Same inputs = same process | ❓ Unknown |

## Core Features

### Mandated Research Cycles
Every theme gets **minimum 2 full research cycles**:
1. Broad landscape search → Analysis → Gap identification
2. Targeted deep dive → Challenge assumptions → Synthesis

No shortcuts. No single-pass summaries.

### Evidence Standards
- **Every conclusion cites multiple sources**
- **Contradictions must be addressed** — not hidden
- **Confidence annotations:** [HIGH], [MEDIUM], [LOW], [SPECULATIVE]
- **Evidence hierarchy:** Meta-analyses > RCTs > Observational > Expert opinion

### Academic Output
- Flowing narrative prose (no bullet point dumps)
- APA 7th edition citations (1-2 per paragraph)
- Proper paragraph structure: claim → evidence → analysis → transition
- Executive summary, methodology, findings, limitations, references

### User Control
Three mandatory stop points:
1. **Initial Engagement** — Clarify scope before research
2. **Research Planning** — Approve themes and approach
3. **Final Report** — Review completed analysis

## Quick Start

```
/research "Comprehensive analysis of [your topic]"
```

Or just ask for "deep research on..." or "exhaustive analysis of..."

## Research Protocol

### Phase 1: Clarification
Agent asks 2-3 essential questions, confirms understanding, **waits for you**.

### Phase 2: Planning
Agent presents:
- Major themes identified (3-5)
- Research execution plan (table format)
- Expected deliverables

**You approve before execution begins.**

### Phase 3: Execution (Auto)
For each theme, two full cycles:
- `web_search` (count=20) for landscape
- Analysis and gap identification
- `web_fetch` on primary sources
- Synthesis and assumption challenging
- Repeat for depth

**Required:** Explicit analysis between every tool call showing evolution of understanding.

### Phase 4: Rep

_meta.json

{
  "ownerId": "kn7cjdxrntr9pc4712fwxp62ns84qarh",
  "slug": "academic-deep-research",
  "version": "1.0.0",
  "publishedAt": 1782500350144
}

example.md

# Deep Research Example Workflow

## User Request
> "I need to understand the current state of AI coding assistants. What's the landscape, who are the players, and what should I know if I'm choosing one for my team?"

---

## Phase 1: Initial Engagement

**Clarifying Questions:**
1. What is the primary question or problem you're trying to solve — are you replacing an existing tool, or is this a first adoption?
2. What depth of analysis do you need — a quick landscape overview, or exhaustive technical and security analysis?
3. Are there specific constraints — budget limits, security requirements, or integration needs with your existing stack?

**User Response:**
> "First adoption, need exhaustive analysis for a 15-person engineering team using VS Code. Security is paramount because we handle healthcare data. Budget is flexible if the value is there."

**Reflection:**
> "So you need a comprehensive, security-focused analysis of AI coding assistants for healthcare software development. The research must prioritize security certifications, data handling practices, and HIPAA compliance considerations alongside technical capabilities. I'll investigate market players, security frameworks, real-world healthcare adoption, and implementation considerations for regulated environments."

---

## Phase 2: Research Planning

### Major Themes Identified

| Theme | Key Questions | Approach |
|-------|---------------|----------|
| **1. Market Landscape** | Who are the major players? Market share? Differentiation? | Broad search + competitive analysis |
| **2. Security & Compliance** | How is code handled? SOC 2? HIPAA? On-prem options? | Deep dive on security docs, compliance certifications |
| **3. Technical Capabilities** | Context understanding, language support, IDE integration | Feature comparison, benchmark analysis |
| **4. Healthcare-Specific Considerations** | Healthcare adoption, PHI handling, regulatory alignment | Specialized sources, case studies |
| **5. Implementation & ROI** | Setup complexity, productivity data, pricing models | Real-world reports, cost analysis |

### Research Execution Plan

**Theme 1: Market Landscape**
1. `web_search count=20` "AI coding assistants 2024 2025 market landscape comparison"
2. Analysis — identify players, segments, trends
3. `web_fetch` on top 5 authoritative sources
4. Synthesis — market structure, competitive dynamics

**Theme 2: Security & Compliance**
1. `web_search count=20` "GitHub Copilot security SOC 2 HIPAA compliance enterprise"
2. Analysis — security frameworks, data handling
3. `web_fetch` on security whitepapers, compliance docs
4. Synthesis — security landscape, gaps, recommendations

**Theme 3: Technical Capabilities**
1. `web_search count=20` "AI coding assistant benchmarks code completion accuracy 2024"
2. Analysis — feature matrices, performance claims
3. `web_fetch` on benchmark studies, technical docs
4. Synthesis — capability assessment, differentiators

**Theme 4: Healthcare-Specific**
1. `w

quickref.md

# Deep Research Quick Reference

## Invocation
- `/research` or mention "deep research" / "exhaustive analysis"

## Four Phases

| Phase | User Action | Your Action | Key Output |
|-------|-------------|-------------|------------|
| 1. Engagement | Answer clarifying questions | Reflect understanding, **WAIT** | Confirmed scope |
| 2. Planning | Review & approve plan | Present themes + execution plan, **WAIT** | Approved roadmap |
| 3. Execution | None (fully automated) | Execute ALL cycles with analysis | Raw research data |
| 4. Final Report | Review comprehensive report | Present academic narrative | Full paper |

## Stop Points (Only Three)
1. ✅ After clarifying questions (Phase 1)
2. ✅ After research plan presentation (Phase 2)
3. ✅ Final report delivery (Phase 4)

## Tool Usage Sequence (Per Theme)
1. **START:** `web_search` for landscape (count=20)
2. **ANALYZE:** Synthesize findings, identify patterns and gaps
3. **DIVE:** `web_fetch` for depth on key sources
4. **PROCESS:** Synthesize new findings, challenge assumptions
5. **REPEAT:** Second cycle targeting identified gaps

## Required Analysis After Every Tool Use
- Connect new findings to previous results
- Show evolution of understanding  
- Highlight pattern changes
- Address contradictions
- Build coherent narrative

## Research Standards
- Every conclusion cites **multiple sources**
- All **contradictions addressed**
- **Uncertainties acknowledged**
- **Limitations discussed**
- **Gaps identified**

## Writing Style (Final Report)
- **Flowing narrative** — paragraphs only, no lists
- **Academic but accessible**
- **Evidence integrated naturally** in prose
- **Progressive logical development**
- **Smooth transitions** between concepts

## Prohibited in Final Report
- Bullet points or numbered lists
- Tables (convert to prose)
- Isolated data without context
- Section headers without narrative

## Citation Standards (APA 7th)
- **Density:** 1-2 citations per paragraph
- **Format:** (Author, Year) in-text
- **References:** Full APA with hanging indent
- **All claims cited** — no exceptions

## Confidence Annotations
- **[HIGH]** — Multiple high-quality sources agree
- **[MEDIUM]** — Limited or mixed evidence
- **[LOW]** — Single source, needs verification
- **[SPECULATIVE]** — Emerging area

## Report Sections (Narrative Format)
1. **Executive Summary** — 2-3 paragraphs
2. **Knowledge Development** — evolution of understanding (6-8+ paragraphs)
3. **Comprehensive Analysis** — findings, patterns, contradictions, evidence (6-8+ paragraphs each subsection)
4. **Practical Implications** — applications, risks, future research (6-8+ paragraphs each subsection)
5. **References** — APA format, alphabetical
6. **Appendices** — optional

## Critical Reminders
- Stop only at three major points
- Always analyze between tool usage
- Show clear thinking progression
- Connect findings explicitly
- Build coherent narrative throughout
- No shortcuts or rushed analysis

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