deep-reading-analyst▌
ovachiever/droid-tings · updated Apr 8, 2026
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Transforms surface-level reading into deep learning through systematic analysis using 10+ proven thinking frameworks. Guides users from understanding to application through structured workflows.
Deep Reading Analyst
Transforms surface-level reading into deep learning through systematic analysis using 10+ proven thinking frameworks. Guides users from understanding to application through structured workflows.
Framework Arsenal
Quick Analysis (15min)
- 📋 SCQA - Structure thinking (Situation-Complication-Question-Answer)
- 🔍 5W2H - Completeness check (What, Why, Who, When, Where, How, How much)
Standard Analysis (30min)
- 🎯 Critical Thinking - Argument evaluation
- 🔄 Inversion Thinking - Risk identification
Deep Analysis (60min)
- 🧠 Mental Models - Multi-perspective analysis (physics, biology, psychology, economics)
- ⚡ First Principles - Essence extraction
- 🔗 Systems Thinking - Relationship mapping
- 🎨 Six Thinking Hats - Structured creativity
Research Analysis (120min+)
- 📊 Cross-Source Comparison - Multi-article synthesis
Workflow Decision Tree
User provides content
↓
Ask: Purpose + Depth Level + Preferred Frameworks
↓
┌─────────────────┬─────────────────┬─────────────────┬─────────────────┐
│ Level 1 │ Level 2 │ Level 3 │ Level 4 │
│ Quick │ Standard │ Deep │ Research │
│ 15min │ 30min │ 60min │ 120min+ │
├─────────────────┼─────────────────┼─────────────────┼─────────────────┤
│ • SCQA │ Level 1 + │ Level 2 + │ Level 3 + │
│ • 5W2H │ • Critical │ • Mental Models │ • Cross-source │
│ • Structure │ • Inversion │ • First Princ. │ • Web search │
│ │ │ • Systems │ • Synthesis │
│ │ │ • Six Hats │ │
└─────────────────┴─────────────────┴─────────────────┴─────────────────┘
Step 1: Initialize Analysis
Ask User (conversationally):
- "What's your main goal for reading this?"
- Problem-solving / Learning / Writing / Decision-making / Curiosity
- "How deep do you want to go?"
- Quick (15min) / Standard (30min) / Deep (60min) / Research (120min+)
- "Any specific frameworks you'd like to use?"
- Suggest based on content type (see Framework Selection Guide below)
Default if no response: Level 2 (Standard mode) with auto-selected frameworks
Framework Selection Guide
Based on content type, auto-suggest:
📄 Strategy/Business articles → SCQA + Mental Models + Inversion
📊 Research papers → 5W2H + Critical Thinking + Systems Thinking
💡 How-to guides → SCQA + 5W2H + First Principles
🎯 Opinion pieces → Critical Thinking + Inversion + Six Hats
📈 Case studies → SCQA + Mental Models + Systems Thinking
Step 2: Structural Understanding
Always start here regardless of depth level.
Phase 2A: Basic Structure
📄 Content Type: [Article/Paper/Report/Guide]
⏱️ Estimated reading time: [X minutes]
🎯 Core Thesis: [One sentence]
Structure Overview:
├─ Main Argument 1
│ ├─ Supporting point 1.1
│ └─ Supporting point 1.2
├─ Main Argument 2
└─ Main Argument 3
Key Concepts: [3-5 terms with brief definitions]
Phase 2B: SCQA Analysis (Quick Framework)
Load references/scqa_framework.md and apply:
## SCQA Structure
**S (Situation)**: [Background/context the article establishes]
**C (Complication)**: [Problem/challenge identified]
**Q (Question)**: [Core question being addressed]
**A (Answer)**: [Main solution/conclusion]
📊 Structure Quality:
- Clarity: [★★★★☆]
- Logic flow: [★★★★★]
- Completeness: [★★★☆☆]
Phase 2C: 5W2H Completeness Check (if Level 1+)
Quick scan using references/5w2h_analysis.md:
## Information Completeness
✅ Well-covered: [What, Why, How]
⚠️ Partially covered: [Who, When]
❌ Missing: [Where, How much]
🔴 Critical gaps: [List 1-2 most important missing pieces]
Step 3: Apply Thinking Models
Select based on depth level and user preference:
Level 1 (Quick - 15 min)
Core: Structure + SCQA + 5W2H Quick Check
Output:
- SCQA breakdown
- Information gaps (from 5W2H)
- TOP 3 insights
- 1 immediate action item
Level 2 (Standard - 30 min)
Add: Critical Thinking + Inversion
Load and apply:
-
references/critical_thinking.md:- Argument quality assessment
- Logic flaw identification
- Evidence evaluation
- Alternative perspectives
-
references/inversion_thinking.md:- How to ensure failure? (reverse the advice)
- What assumptions if wrong?
- Missing risks
- Pre-mortem analysis
## Critical Analysis
### Argument Strength: [X/10]
Strengths:
- [Point 1]
Weaknesses:
- [Point 1]
Logical fallacies detected:
- [If any]
## Inversion Analysis
🚨 How this could fail:
1. [Failure mode 1] → Mitigation: [...]
2. [Failure mode 2] → Mitigation: [...]
Missing risk factors:
- [Risk 1]
Level 3 (Deep - 60 min)
Add: Mental Models + First Principles + Systems + Six Hats
Load and apply:
-
references/mental_models.md:- Select 3-5 relevant models from different disciplines
- Apply each lens to the content
- Identify cross-model insights
-
references/first_principles.md:- Strip to fundamental truths
- Identify core assumptions
- Rebuild understanding from base
-
references/systems_thinking.md:- Map relationships and feedback loops
- Identify leverage points
- See the big picture
-
references/six_hats.md:- White (facts), Red (feelings), Black (caution)
- Yellow (benefits), Green (creativity), Blue (process)
## Multi-Model Analysis
### Mental Models Applied:
1. **[Model 1 from X discipline]**
Insight: [...]
2. **[Model 2 from Y discipline]**
Insight: [...]
3. **[Model 3 from Z discipline]**
Insight: [...]
Cross-model pattern: [Key insight from combining models]
### First Principles Breakdown:
Core assumptions:
1. [Assumption 1] → Valid: [Yes/No/Conditional]
2. [Assumption 2] → Valid: [Yes/No/Conditional]
Fundamental truth: [What remains after stripping assumptions]
### Systems Map:
[Variable A] ──reinforces──> [Variable B] ↑ | | | balances reinforces | | └─────────<────────────────┘
Leverage point: [Where small change = big impact]
### Six Hats Perspective:
🤍 Facts: [Objective data]
❤️ Feelings: [Intuitive response]
🖤 Cautions: [Risks and downsides]
💛 Benefits: [Positive aspects]
💚 Ideas: [Creative alternatives]
💙 Process: [Meta-thinking]
Level 4 (Research - 120 min+)
Add: Cross-source comparison via web_search
Use web_search to find 2-3 related sources, then:
- Load
references/comparison_matrix.md - Compare SCQA across sources
- Identify consensus vs. divergence
- Synthesize integrated perspective
## Multi-Source Analysis
### Source 1: [This article]
S-C-Q-A: [Summary]
Key claim: [...]
### Source 2: [Found article]
S-C-Q-A: [Summary]
Key claim: [...]
### Source 3: [Found article]
S-C-Q-A: [Summary]
Key claim: [...]
## Synthesis
**Consensus**: [What all agree on]
**Divergence**: [Where they differ]
**Unique value**: [What each contributes]
**Integrated view**: [Your synthesis]
Step 4: Synthesis & Output
Generate based on user goal:
For Problem-Solving:
## Applicable Solutions
[Extract 2-3 methods from content]
## Application Plan
Problem: [User's specific issue]
Relevant insights: [From analysis]
Action steps:
1. [Concrete action with timeline]
2. [Concrete action with timeline]
3. [Concrete action with timeline]
Success metrics: [How to measure]
## Risk Mitigation (from Inversion)
Potential failure points:
- [Point 1] → Prevent by: [...]
- [Point 2] → Prevent by: [...]
For Learning:
## Learning Notes
Core concepts (explained simply):
1. **[Concept 1]**: [Definition + Example]
2. **[Concept 2]**: [Definition + Example]
Mental models gained:
- [Model 1]: [How it works]
Connections to prior knowledge:
- [Link to something user already knows]
## Deeper Understanding (First Principles)
Fundamental question: [...]
Core principle: [...]
## Verification Questions
1. [Question to test understanding]
2. [Question to test application]
3. [Question to test evaluation]
For Writing Reference:
## Key Arguments & Evidence
[Structured extraction with page/paragraph numbers]
## Quotable Insights
"[Quote 1]" — Context: [...]
"[Quote 2]" — Context: [...]
## Critical Analysis Notes
Strengths: [For citing]
Limitations: [For balanced discussion]
## Alternative Perspectives (from Mental Models)
[What other disciplines would say about this]
## Gaps & Counterfactuals
What the article doesn't address:
- [Gap 1]
- [Gap 2]
For Decision-Making:
## Decision Framework
Options presented: [A / B / C]
Multi-model evaluation:
- Economic lens: [...]
- Risk lens (Inversion): [...]
- Systems lens: [...]
## Six Hats Decision Analysis
🤍 Facts: [Objective comparison]
🖤 Risks: [What could go wrong]
💛 Benefits: [Upside potential]
💚 Alternatives: [Other options not considered]
💙 Recommendation: [Synthesized advice]
## Scenario Analysis (from Inversion)
Best case: [...]
Worst case: [...]
Most likely: [...]
Step 5: Knowledge Activation
Always end with:
## 🎯 Immediate Takeaways (Top 3)
1. **[Insight 1]**
Why it matters: [Personal relevance]
One action: [Specific, time-bound]
2. How to use deep-reading-analyst on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add deep-reading-analyst
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches deep-reading-analyst from GitHub repository ovachiever/droid-tings and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate deep-reading-analyst. Access the skill through slash commands (e.g., /deep-reading-analyst) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★25 reviews- ★★★★★Lucas Nasser· Dec 4, 2024
Registry listing for deep-reading-analyst matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Zara Singh· Nov 23, 2024
Useful defaults in deep-reading-analyst — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ava Sanchez· Oct 22, 2024
Useful defaults in deep-reading-analyst — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ava Ramirez· Oct 14, 2024
I recommend deep-reading-analyst for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ishan Bansal· Sep 5, 2024
Solid pick for teams standardizing on skills: deep-reading-analyst is focused, and the summary matches what you get after install.
- ★★★★★Yash Thakker· Sep 1, 2024
deep-reading-analyst has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Tariq Singh· Aug 24, 2024
deep-reading-analyst has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Dhruvi Jain· Aug 20, 2024
Solid pick for teams standardizing on skills: deep-reading-analyst is focused, and the summary matches what you get after install.
- ★★★★★Ira Abebe· Jul 15, 2024
deep-reading-analyst fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Oshnikdeep· Jul 11, 2024
We added deep-reading-analyst from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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