Skill: jirboy/vibe-research-cn (version 1.0.0)

--- name: Vibe Research slug: vibe-research version: 1.0.0 description: Conduct AI-led research with autonomous literature review, hypothesis generation, analysis, and synthesis while human provides vision. metadata: {"clawdbot":{"emoji":"🔬","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## When to Use User has a research question or knowledge gap. Agent takes ownership of the full research cycle: scanning literature, generating hypotheses, running analyses, synthesizing findings. Human provides direction and oversight, AI executes. ## Quick Reference | Topic | File | |-------|------| | Research pipeline | `pipeline.md` | | Risk mitigation | `risks.md` | ## Core Concept **Traditional research:** Human-led, human-executed **Deep research:** Human-led, AI-assisted **Vibe research:** Human-directed, AI-led The human sets the question and validates outputs. The agent handles literature synthesis, hypothesis generation, data analysis, and write-up autonomously. ## Core Rules ### 1. Full-Cycle Ownership Agent executes the complete pipeline: 1. **Gap identification** — What's unknown or contested? 2. **Literature synthesis** — Scan, summarize, cross-reference sources 3. **Hypothesis generation** — Propose testable claims 4. **Analysis design** — Define methodology 5. **Execution** — Run analyses, gather data 6. **Synthesis** — Write findings with citations ### 2. Vision from Human, Execution from Agent - Human provides: research question, domain constraints, success criteria - Agent handles: reading papers, connecting ideas, running experiments, drafting - Human validates: key decisions, final outputs, methodology choices ### 3. Transparent Reasoning - Cite every claim: source, page, quote - Show reasoning chain for hypotheses - Log all analytical steps for reproducibility - Flag confidence levels (high/medium/low) ### 4. Proactive Gap Detection Don't wait for instructions. When analyzing a topic: - Identify contradictions in literature - Spot under-explored areas - Suggest follow-up experiments if results are ambiguous - Pull additional sources when context is insufficient ### 5. Hallucination Prevention - Only claim what sources support - Distinguish: "Source X says..." vs "I infer..." - When uncertain, say so explicitly - Cross-verify critical facts across multiple sources ## Vibe Research Traps - Treating AI output as ground truth → always require human validation of key findings - Skipping methodology transparency → document every step for reproducibility - Overwhelming human with raw output → synthesize into actionable insights - Losing the human's analytical skills → keep them engaged in critical thinking

path: example.md
content: # 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 鈥?are you replacing an existing tool, or is this a first adoption?
2. What depth of analysis do you need 鈥?a quick landscape overview, or exhaustive technical and security analysis?
3. Are there specific constraints 鈥?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 鈥?identify players, segments, trends
3. `web_fetch` on top 5 authoritative sources
4. Synthesis 鈥?market structure, competitive dynamics

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

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

**Theme 4: Healthcare-Specific**
1. `web_search count=20` "AI coding assistant healthcare HIPAA PHI code security"
2. Analysis 鈥?healthcare adoption, regulatory concerns
3. `web_fetch` on healthcare case studies, compliance guides
4. Synthesis 鈥?healthcare readiness, risk factors

**Theme 5: Implementation & ROI**
1. `web_search count=20` "AI coding assistant developer productivity ROI study 2024"
2. Analysis 鈥?productivity claims, cost models
3. `web_fetch` on ROI studies, pricing analysis
4. Synthesis 鈥?value proposition, implementation factors

### Expected Deliverables
- Comprehensive research paper (20-30 pages)
- APA citations throughout (1-2 per paragraph)
- Security-focused recommendations for healthcare
- Side-by-side comparison matrix (converted to narrative)
- Implementation roadmap

---

## Phase 3: Research Cycles (Auto-Execute)

### Theme 1: Market Landscape 鈥?Cycle 1

**Tool:** `web_search query="AI coding assistants 2024 2025 market landscape comparison GitHub Copilot Cursor" count=20`

**Results:** Found sources on GitHub Copilot, Cursor, Codeium, Amazon CodeWhisperer, Google Code Assist, Tabnine, Replit Ghostwriter.

**Analysis:**
The search reveals a market dominated by GitHub Copilot with approximately 1.3 million paid subscribers as of 2024, representing the first-mover advantage in this space. Cursor has emerged as a significant challenger, having raised $60 million in Series A funding and positioning itself around larger context windows and privacy-focused features. Codeium operates on a freemium model with strong uptake among individual developers, while enterprise-focused players like Amazon CodeWhisperer and Google Code Assist are targeting organizational adoption through cloud integration.

Initial hypotheses forming: (1) Market is consolidating around a few major players, (2) Differentiation is shifting from basic completion to context understanding and privacy, (3) Enterprise features are becoming the primary battleground.

**Gaps Identified:**
- Limited independent market share data 鈥?most figures come from vendor announcements
- Unclear on international market distribution
- Need deeper analysis on enterprise vs. individual adoption rates
- Contradiction: Some sources claim Cursor is gaining rapidly; others show Copilot maintaining dominance

---

### Theme 1: Market Landscape 鈥?Cycle 2

**Tool:** `web_fetch` on GitHub Copilot official documentation, Cursor about page, and TechCrunch funding coverage.

**Analysis:**
Fetching primary sources reveals important nuances that challenge initial assumptions. While GitHub's announced 1.3 million subscribers represents paid users, the actual developer reach is larger through free educational licenses and open source programs. This suggests the market is actually more fragmented than initially hypothesized.

Cursor's $60 million raise is confirmed through TechCrunch reporting, but the company's actual user numbers remain private. This creates uncertainty about their true market position 鈥?they may be growing rapidly in specific segments (startups, privacy-conscious developers) without threatening Copilot's overall dominance.

The contradiction noted in Cycle 1 appears resolvable: Cursor is gaining mindshare and venture attention, but Copilot maintains volume leadership. These are different metrics of "success" that can coexist.

New patterns emerging: (1) Market segmentation by use case (individual vs. enterprise), (2) Geographic variation in adoption, (3) Pricing model experimentation (per-seat vs. usage-based).

**Connections to Cycle 1:**
The initial hypothesis about market consolidation requires refinement. Rather than a winner-take-all dynamic, the evidence suggests parallel market development: Copilot for broad adoption, Cursor for specific segments, and specialized tools for niche use cases.

**Remaining Uncertainties:**
- Actual Cursor user numbers remain undisclosed
- Enterprise adoption rates poorly documented
- International market data largely absent

---

### Theme 2: Security & Compliance 鈥?Cycle 1

**Tool:** `web_search query="GitHub Copilot security SOC 2 HIPAA compliance enterprise data handling" count=20`

**Results:** Found GitHub security whitepaper, SOC 2 reports, enterprise trust documentation, and some healthcare-specific discussions.

**Analysis:**
Security documentation reveals significant variation in compliance posture across vendors. GitHub Copilot Business and Enterprise tiers explicitly address security concerns with SOC 2 Type II certification and options for code isolation that prevent training data inclusion. However, HIPAA compliance remains ambiguous 鈥?GitHub states they will sign Business Associate Agreements but stops short of claiming HIPAA compliance for the AI features themselves.

Cursor positions itself as privacy-first with a local mode that processes code entirely on-device, eliminating transmission risks. This represents a fundamentally different security model that may be more appropriate for healthcare contexts.

Initial hypothesis: Security features correlate with pricing tier, with enterprise offerings providing necessary controls for regulated industries.

**Gaps Identified:**
- No clear HIPAA compliance claims from any vendor for AI features specifically
- Limited independent security audits published
- Unclear on data retention policies across vendors
- Contradiction: Some sources suggest on-premise options exist; others indicate cloud-only

---

### Theme 2: Security & Compliance 鈥?Cycle 2

**Tool:** `web_fetch` on GitHub Copilot Enterprise security documentation, Cursor security page, and healthcare IT forum discussions.

**Analysis:**
Deep extraction from primary sources confirms the security landscape is complex and evolving. GitHub's documentation explicitly states that Copilot Enterprise offers "code isolation" that prevents user code from being used to train public models, addressing a major concern for proprietary codebases. However, the documentation carefully avoids HIPAA compliance claims, instead referring to GitHub's general BAA availability.

This is a critical finding: The AI coding assistant products themselves may not be HIPAA-compliant even when the underlying platform is. This distinction is crucial for healthcare organizations.

Cursor's local mode is confirmed to process code entirely on-device using locally-hosted models, representing a genuinely different security posture. For healthcare, this eliminates the data transmission risk but may introduce other concerns (local model quality, update mechanisms).

The contradiction about on-premise options is partially resolved: No major vendor offers true on-premise AI coding assistants as of this research, though some offer VPC deployment or local processing modes.

**Connections to Theme 1:**
The market segmentation noted earlier aligns with security postures: Copilot for general enterprise, Cursor for security-conscious segments. This suggests healthcare organizations may need to evaluate Cursor more seriously despite its smaller market presence.

---

### Theme 3: Technical Capabilities 鈥?Cycle 1

*[Similar two-cycle process with analysis between each tool call...]*

---

### Theme 4: Healthcare-Specific Considerations 鈥?Cycle 1

*[Similar two-cycle process...]*

---

### Theme 5: Implementation & ROI 鈥?Cycle 1

*[Similar two-cycle process...]*

---

## Cross-Theme Knowledge Integration

After completing all five themes with two cycles each, synthesis reveals:

**Emerging Pattern 1: The Healthcare Gap**
No AI coding assistant currently claims HIPAA compliance for AI features specifically. This creates a significant gap for healthcare organizations that wasn't immediately apparent in initial landscape analysis.

**Emerging Pattern 2: The Security-Performance Tradeoff**
Local processing (Cursor) offers superior privacy but may sacrifice some capability compared to cloud-powered alternatives (Copilot). This tradeoff is central to the healthcare decision.

**Emerging Pattern 3: Market Immaturity**
The field is evolving rapidly with new features and compliance developments monthly. Any recommendation must account for this volatility.

**Contradictions Resolved:**
- Market dominance vs. challenger growth: Different metrics, both valid
- Cloud vs. on-premise: No true on-premise exists; local processing is the alternative

**Unified Understanding:**
For a healthcare engineering team, the decision framework differs from general enterprise adoption. Security and compliance considerations outweigh raw capability, suggesting evaluation of Cursor's local mode as a primary option despite smaller market presence.

---

## Phase 4: Final Report

*[Presented as cohesive research paper with narrative sections, proper APA citations, no bullet points, 6-8+ paragraphs per major section...]*

---

## Key Distinctions from Standard Research

| Aspect | Standard Research | Deep Research Protocol |
|--------|-------------------|------------------------|
| Cycles per theme | 1 | Minimum 2 |
| Analysis between tools | Optional | Required |
| Citation density | As needed | 1-2 per paragraph |
| Final format | Flexible | Academic narrative |
| Contradiction handling | Note if found | Must address all |
| Writing style | Variable | Flowing prose only |



path: pipeline.md
content: # Research Pipeline — Vibe Research

## Phase 1: Gap Identification

**Goal:** Find what's unknown, contested, or under-explored.

### Process
1. Receive research question from human
2. Scan existing literature (100+ sources minimum)
3. Identify:
   - Established consensus
   - Active debates
   - Unexplored territories
   - Methodological gaps

### Output
```markdown
## Knowledge Gap Analysis: [Topic]

### Established (High confidence)
- [Fact 1] — supported by [n] sources
- [Fact 2] — supported by [n] sources

### Contested (Debate exists)
- [Claim] — [Source A] argues X, [Source B] argues Y

### Unexplored (Opportunity)
- [Gap 1] — no literature found addressing this
- [Gap 2] — mentioned but not studied

### Recommended Focus
[Which gap to pursue and why]
```

---

## Phase 2: Literature Synthesis

**Goal:** Build comprehensive understanding from sources.

### Process
1. Retrieve relevant papers, articles, data
2. Extract key claims with citations
3. Cross-reference findings
4. Identify patterns and contradictions
5. Build knowledge graph of concepts

### Techniques
- Vector search for semantic similarity
- Citation network analysis
- Claim extraction and verification
- Temporal analysis (how has understanding evolved?)

### Output
Synthesized summary with:
- Key findings (cited)
- Methodology patterns across studies
- Consensus vs. outlier views
- Quality assessment of sources

---

## Phase 3: Hypothesis Generation

**Goal:** Propose testable claims that address identified gaps.

### Process
1. Review gaps and synthesis
2. Generate candidate hypotheses
3. Evaluate each for:
   - Testability (can we verify/falsify?)
   - Novelty (does it add knowledge?)
   - Feasibility (can we execute with available resources?)
4. Rank and recommend

### Output
```markdown
## Hypothesis Candidates

### H1: [Statement]
- Testability: [High/Medium/Low]
- Evidence for: [existing support]
- Evidence against: [existing challenges]
- Test approach: [brief methodology]

### H2: [Statement]
...

### Recommendation
Pursue H[n] because [reasoning]
```

---

## Phase 4: Analysis Design

**Goal:** Define rigorous methodology to test hypothesis.

### Components
1. Data requirements
2. Analytical methods
3. Success criteria
4. Potential confounds
5. Reproducibility plan

### Output
Research protocol document with clear steps

---

## Phase 5: Execution

**Goal:** Run the analysis and gather results.

### Agent responsibilities
- Execute defined methodology
- Log all steps and decisions
- Flag anomalies or unexpected findings
- Iterate if initial results are inconclusive

### Human checkpoints
- Validate methodology before execution
- Review intermediate results
- Approve course corrections

---

## Phase 6: Synthesis

**Goal:** Transform findings into actionable knowledge.

### Output formats
- Executive summary (1 page)
- Full research report (with methodology)
- Citation list
- Reproducibility package (code, data, steps)

### Structure
1. Question addressed
2. Methodology used
3. Key findings
4. Limitations
5. Implications
6. Future directions


path: quickref.md
content: # 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. 鉁?After clarifying questions (Phase 1)
2. 鉁?After research plan presentation (Phase 2)
3. 鉁?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** 鈥?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** 鈥?no exceptions

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

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


path: README.md
content: # Academic Deep Research 馃敩

**Transparent, rigorous, self-contained research** 鈥?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:**
- 鉁?**Full methodology visible** 鈥?Every step documented, reproducible
- 鉁?**No external dependencies** 鈥?Runs entirely on OpenClaw native tools
- 鉁?**User control** 鈥?3 explicit checkpoints for approval
- 鉁?**Academic rigor** 鈥?APA citations, evidence hierarchy, confidence levels
- 鉁?**Works offline** 鈥?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 | 鉁?Yes | 鉂?No |
| User Checkpoints | 3 approval points | Usually none |
| Citation Format | APA 7th edition | Varies/unspecified |
| Evidence Hierarchy | Explicit (meta-analyses 鈫?opinion) | Unspecified |
| Output Control | Strict prose, no bullet points | Varies |
| Reproducibility | 鉁?Same inputs = same process | 鉂?Unknown |

## Core Features

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

No shortcuts. No single-pass summaries.

### Evidence Standards
- **Every conclusion cites multiple sources**
- **Contradictions must be addressed** 鈥?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 鈫?evidence 鈫?analysis 鈫?transition
- Executive summary, methodology, findings, limitations, references

### User Control
Three mandatory stop points:
1. **Initial Engagement** 鈥?Clarify scope before research
2. **Research Planning** 鈥?Approve themes and approach
3. **Final Report** 鈥?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: Report
Academic narrative with:
- Executive Summary
- Knowledge Development
- Comprehensive Analysis
- Practical Implications
- APA References

## File Structure

```
deep-research/
鈹溾攢鈹€ SKILL.md       # Full methodology (500+ lines)
鈹溾攢鈹€ README.md      # This file
鈹溾攢鈹€ quickref.md    # One-page cheat sheet
鈹溾攢鈹€ example.md     # Complete workflow example
鈹斺攢鈹€ LICENSE        # Apache 2.0
```

## When to Use This

- Literature reviews requiring academic rigor
- Competitive intelligence with source verification
- Complex topics needing multi-source synthesis
- Any research where you need to **show your work**
- When you don't trust black-box AI summaries

## License

Apache 2.0 鈥?See [LICENSE](LICENSE)

---

**Built for researchers who care about methodology, not just outputs.**



path: risks.md
content: # Risk Mitigation — Vibe Research

## Known Risks

### 1. Hallucinations
AI generates plausible but false information.

**Mitigations:**
- Require citations for every factual claim
- Cross-verify critical facts across 3+ sources
- Use retrieval-augmented generation (ground in real docs)
- Flag confidence levels explicitly
- Human validates key findings before publication

### 2. Bias Propagation
AI reflects biases in training data or source material.

**Mitigations:**
- Diversify source types (academic, industry, geographic)
- Explicitly check for representation gaps
- Document source selection criteria
- Human reviews for blind spots

### 3. Loss of Methodological Rigor
Speed prioritized over quality.

**Mitigations:**
- Document every analytical step
- Require reproducibility package with all outputs
- Human validates methodology before execution
- Peer review of AI-generated protocols

### 4. Researcher Skill Erosion
Over-reliance on AI degrades human expertise.

**Mitigations:**
- Keep human engaged in critical thinking
- Require human to articulate reasoning, not just approve
- Use AI as collaborator, not replacement
- Periodic "manual" research exercises

### 5. Academic Integrity
Unclear attribution, plagiarism risks.

**Mitigations:**
- Transparent disclosure of AI involvement
- Clear citation of all sources
- Human takes responsibility for final output
- Follow institutional guidelines on AI use

---

## Quality Checklist

Before finalizing any research output:

```
□ All factual claims have citations
□ Key findings verified across multiple sources
□ Methodology documented for reproducibility
□ Confidence levels flagged appropriately
□ Human has validated critical findings
□ Limitations explicitly stated
□ AI involvement disclosed
□ Source diversity checked
```

---

## When to Escalate to Human

Pause and request human input when:
- Contradictory evidence with no clear resolution
- High-stakes claims that could cause harm if wrong
- Methodology requires domain expertise to validate
- Ethical considerations arise
- Results are unexpected and consequential


path: _meta.json
content: ownerId: kn73vp5rarc3b14rc7wjcw8f8580t5d1
slug: vibe-research
version: 1.0.0
publishedAt: 1771283674031

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)