Three-Layer Wisdom Extraction

Most knowledge capture stops at "what worked and what didn't." But the most valuable insights — principles that transfer across entirely different domains — require deliberate abstraction. This skill provides a structured process for that abstraction.

The three layers build on each other: you can't abstract universal principles from thin air, you need the concrete timeline first, then the domain insights, then the philosophical leap.

When This Skill is Worth Using

Not every task warrants three-layer extraction. It's worth the effort when:

• The experience involved a non-obvious breakthrough that required >30 minutes of investigation
• A failed approach revealed something surprising about the problem structure
• The solution involved a counterintuitive choice (e.g., making things worse before better)
• Multiple attempts followed a recognizable pattern (workaround trap, local optimum, etc.)

If the experience was straightforward ("followed docs, it worked"), skip this skill.

The Three Layers

Layer 1: Breakthrough Path

The raw timeline. What was tried, what happened, in what order.

Gather by reviewing conversation history, commit logs, or asking the user. Focus on decision points — moments where a different choice would have led to a different outcome.

Output: A chronological list of attempts with outcomes and key metrics.

Layer 2: Domain Knowledge

Field-specific insights. What patterns, diagnostics, and anti-patterns emerged?

This is what claudeception captures as skills. If claudeception has already run, reuse its output here. The key additions: boundary conditions (when does the insight NOT apply?) and diagnostics (what signal would have revealed this earlier?).

Output: Structured domain knowledge (decision trees, comparison tables, or skill files).

Layer 3: Universal Principles

Cross-domain patterns extracted via the five abstraction questions below. These are the highest-value output because they transfer to completely different contexts.

Output: 3-7 named principles, each with: one-sentence statement, explanation of why it holds across domains, and concrete examples from at least 2 different fields.

The Five Abstraction Questions (Core Tool)

For each Layer 2 insight, apply these questions systematically:

1. INVERSION: What is the OPPOSITE of this insight, and when would that opposite be correct? This reveals context-dependency. If the opposite is never correct, the insight is trivial.
2. GENERALIZATION: Strip away domain-specific terms. What is the abstract structure? "Stale lag features cause underprediction" → "Training-inference distribution mismatch causes systematic bias toward the training distribution."
3. TRANSFER: Where else does this abstract pattern appear? Find at least 2 other domains. "Same tool has opposite effects in different contexts" → medicine (drug interactions), cooking (ingredient combinations), economics (policy interactions).
4. PARADOX: What apparent contradiction does this insight resolve? "Geo blend helps one model type but destroys another" → "Effectiveness depends on interaction with the system, not just the intervention itself."
5. META: What does this reveal about the problem-solving process itself? "The workaround that works blocks the fundamental fix" → "Local optima in solution space are more dangerous than failures, because they satisfy."

Multi-Perspective Analysis

Before finalizing Layer 3 principles, analyze from multiple perspectives (adapted from Common Wisdom Model, Grossmann 2020, and Multi-Actor Insight Extraction, Nature 2025):

• Pragmatist: Is this principle actually actionable, or just intellectually satisfying?
• Skeptic: What evidence would DISPROVE this principle? If none exists, it may be unfalsifiable.
• Cross-domain validator: Does this truly transfer, or are the "other domain" examples forced analogies?

If a principle doesn't survive all three perspectives, it's probably still Layer 2.

Validation Checklist

A Layer 3 principle passes quality checks when ALL of these hold:

• Domain independence: Explainable to someone outside the field without jargon
• Predictive: Would knowing this have changed your approach earlier?
• Multi-domain: At least 2 other domains where it applies
• Non-trivial: Not something a reasonable person would already assume
• Actionable: Suggests a concrete change in approach
• Falsifiable: Can describe evidence that would disprove it

If a principle fails any check, it's probably still Layer 2. Try generalizing further or discard it.

Extraction Process

Step 1: Gather Layer 1

Review the conversation or project history. List each major attempt with:

• What was tried
• Result (success / failure / partial)
• Key metric or signal
• What prompted the next attempt

Step 2: Extract Layer 2

For each breakthrough or significant failure in Layer 1:

• What domain-specific insight enabled or caused it?
• What diagnostic would catch this earlier next time?
• What are the boundary conditions (when does this NOT apply)?

If claudeception skills already exist for this domain, reference them rather than duplicating.

Step 3: Abstract Layer 3

Take the 2-3 most impactful Layer 2 insights and apply all five abstraction questions to each. Run multi-perspective analysis on each candidate principle. Discard any that don't pass the validation checklist. Keep 3-7 principles max.

Step 4: Format and Present

Present the three layers to the user in this format:

## Layer 1: Breakthrough Path
[timeline]

## Layer 2: Domain Knowledge
[references to existing skills or new insights]

## Layer 3: Universal Principles
[each principle with: name, one-line statement, why it transfers, domain examples]

Ask the user to review and challenge the Layer 3 principles. Their pushback often reveals whether a principle is genuinely universal or still domain-specific.

Integration with Existing Skills

When running this skill, check for existing claudeception skills first — they provide ready-made Layer 2 content. See references/principles-from-competitions.md for examples extracted from past projects.

Anti-Patterns

• Over-extraction: Not every experience yields wisdom. If you can't find genuine cross-domain transfer, stop at Layer 2. Forcing Layer 3 produces vacuous platitudes.
• Renaming is not abstracting: "Stale features cause underprediction" → "Bad features cause bad predictions" is NOT a Layer 3 abstraction. It's just vaguer. Real abstraction changes the structure: "Training-inference distribution mismatch causes systematic bias."
• Too many principles: More than 7 suggests you haven't abstracted enough. Look for meta-principles that subsume several specific ones.
• Forced analogies: "This is like X in domain Y" where the analogy only works on the surface. Real transfer requires structural similarity, not just surface resemblance.

References

• Academic basis: Reflexion (Shinn 2023), ExpeL (Zhao 2024), Generative Agents (Park 2023)
• Cognitive science: Common Wisdom Model (Grossmann 2020) — wisdom as perspectival meta-cognition
• Multi-actor validation: Multi-Actor Insight Extraction (Nature Scientific Reports, 2025)
• Analogical transfer: Structure-Mapping Theory (Gentner 1983), Transferable Meta-Learning (Kang 2023)
• Charlie Munger's "latticework of mental models" framework
• Self-Evolving Agents Survey (arXiv 2026) — reward-based, imitation, and population-based evolution strategies
• SPIRAL (2026) — Planner-Simulator-Critic for proactive lookahead
• Mem0 / Memory-R1 — active memory consolidation patterns
• See references/principles-from-competitions.md for worked examples