AI Skill Report Card
Generated Skill
YAML--- name: recursive-self-reflection description: Guides systematic self-examination through multiple recursive layers, examining thinking processes, then examining those examinations, then finding meta-patterns. Use when needing deep introspection or analysis of reasoning patterns. --- # Recursive Self-Reflection
Quick Start
Layer 1 - Initial Reflection: What am I thinking about [topic]? → Record raw thoughts
Layer 2 - Meta-Analysis: How am I thinking about my thinking? → Examine the reflection process
Layer 3 - Pattern Recognition: What patterns exist in how I find patterns? → Identify meta-patterns
Layer 4 - Integration: What does this reveal about my cognitive architecture?
Recommendation▾
Consider adding more specific examples
Workflow
Progress:
- Surface Layer: Examine direct thoughts on the topic
- First Recursion: Analyze how you're analyzing
- Second Recursion: Find patterns in pattern-finding behavior
- Third Recursion: Identify meta-patterns in meta-pattern recognition
- Synthesis: Extract insights about cognitive processes
- Application: Apply discoveries to original problem
Detailed Process:
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Initial Examination
- State the topic/problem
- Record immediate thoughts and reactions
- Note assumptions and biases
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First Recursive Layer
- How did I approach that examination?
- What mental models did I use?
- What did I prioritize or ignore?
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Second Recursive Layer
- How do I typically examine my own thinking?
- What patterns emerge in my meta-cognitive approach?
- Where do I get stuck or avoid going deeper?
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Third Recursive Layer
- How do I recognize patterns in my pattern-recognition?
- What are my default ways of organizing meta-insights?
- What blind spots exist in my self-reflection process?
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Pattern Integration
- Map recurring themes across all layers
- Identify contradictions or tensions
- Note where different layers reinforce or challenge each other
Recommendation▾
Include edge cases
Examples
Example 1: Input: "Why do I procrastinate on important tasks?"
Output:
Layer 1: I avoid tasks that feel overwhelming or unclear
Layer 2: I notice I'm framing this as a character flaw rather than examining the system
Layer 3: I tend to look for simple cause-effect when examining my behaviors
Layer 4: My pattern-finding defaults to linear thinking, missing feedback loops
Synthesis: Procrastination serves a protective function I haven't acknowledged
Example 2: Input: "How do I make decisions under uncertainty?"
Output:
Layer 1: I gather data until I feel "ready" but that moment never comes
Layer 2: I'm avoiding the discomfort of not-knowing by seeking false certainty
Layer 3: I notice I examine my avoidance but don't examine why I examine it that way
Layer 4: My recursive loops tend to spiralize around anxiety rather than expand understanding
Synthesis: Decision paralysis masks fear of responsibility for outcomes
Best Practices
- Go slow: Each layer needs time to develop before moving deeper
- Maintain curiosity: Avoid judgment about what you discover
- Record everything: Capture insights at each layer before proceeding
- Look for contradictions: These often reveal the most valuable insights
- Stay concrete: Ground abstract insights in specific examples
- Limit depth: 4-5 recursive layers maximum to avoid infinite loops
- Circle back: Revisit earlier layers with new insights from deeper levels
Common Pitfalls
- Infinite recursion: Getting trapped in endless self-analysis without synthesis
- Surface skimming: Moving to next layer before fully exploring current one
- Pattern forcing: Seeing patterns that aren't actually there
- Meta-paralysis: Becoming so focused on the process that you lose the original question
- Defensiveness: Rejecting uncomfortable insights about thinking patterns
- False depth: Mistaking complexity for profundity
- Analysis without integration: Collecting insights without synthesizing them into actionable understanding