META-LEARNING — Cognitive Evolution Engine (Premium)

Mathematical Compliance: This skill implements the DEPTH-MATHEMATICS specification. It computes and outputs ADS phase activation, CEI components, SI components, cognitive traces, and verifies Gates V5, V6. It provides the compounding intelligence mechanism — the system improves itself over time.

Research Basis:

• Wang et al. (2023) "Voyager: An Open-Ended Embodied Agent with Large Language Models" (arXiv:2305.16291) — automatic curriculum, ever-growing skill library, iterative prompting with self-verification
• Shinn et al. (2023) "Reflexion: Language Agents with Verbal Reinforcement Learning" (arXiv:2303.11366) — linguistic feedback, episodic memory, self-improvement without weight updates
• Gou et al. (2023) "CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing" (arXiv:2305.11738) — tool-interactive validation and progressive amendment

Your skill library is static. It doesn't learn. Every task starts from zero. The patterns you discovered in Task 1 are lost when Task 2 arrives. The CONDUCTOR's selection policy is hand-crafted, not learned.

This skill changes that. It extracts patterns from task outcomes, synthesizes new skills, updates the CONDUCTOR's policy, and validates everything against a regression suite. The system gets smarter over time — compounding intelligence.

The Failure Mode You Must Recognize

You are about to finish a task and move on. The orchestration log sits there. The patterns you discovered — which skills worked, which interferences mattered, which gates failed — are not captured. The next task starts with the same hand-crafted policy.

This is cognitive amnesia. The system has no memory of its own reasoning evolution.

New failure mode in v1.0: Pattern loss — reusable reasoning patterns discarded after each task. Policy staleness — CONDUCTOR policy doesn't adapt to model updates or domain shifts. Library rot — skills degrade without regression testing. Synthesis gap — no mechanism to create new skills from discovered patterns.

The Protocol

0 — PARAMETERIZE: Read Thinking Parameters

Read thinking_parameters:

• min_patterns (default 3) — minimum patterns to extract per task
• min_transfer_tasks (default 5) — minimum held-out tasks for transfer testing
• regression_threshold (default 0.95) — new skill versions must achieve ≥ this vs old
• synthesis_confidence_threshold (default 0.7) — minimum confidence for skill synthesis

1 — PATTERN EXTRACTION (from Orchestration Log)

Analyze the completed task's orchestration log and extract reusable reasoning patterns.

PATTERN EXTRACTION
────────────────────────────────────────
Task: [description]
Task Profile: [from CONDUCTOR — complexity, consequence, reversibility, novelty, breadth]
Orchestration Log: [full log from CONDUCTOR]

Extracted Patterns (minimum min_patterns):

Pattern 1: [Name — e.g., "High-Consequence Schema Migration"]
  Trigger Conditions: [when this pattern applies — e.g., "Category 3+ decision + database schema + public API"]
  Skill Sequence: [optimal sequence — e.g., "DEEP-THINK → EXCAVATE → DIVERGE → ADVERSARY → TEMPORAL → THRESHOLD"]
  Critical Interferences: [which interferences were decisive — e.g., "EXCAVATE C3 → ADVERSARY fatal attack prevented schema disaster"]
  Gate Failures: [which gates failed and why — e.g., "G4 failed: NEGATIVE-SPACE missed cascading failure category"]
  ADS Achieved: [value]
  CEI: [value]
  Transferability: [HIGH/MEDIUM/LOW — how generalizable across tasks]
  Confidence: [0.0-1.0 — based on task outcome quality]

Pattern 2: [Name — e.g., "Novel Architecture with High Uncertainty"]
  ...

Pattern 3: [Name — e.g., "Low-Stakes Rapid Decision"]
  ...

Anti-Patterns (what NOT to do):
  Anti-Pattern 1: [e.g., "Running DIVERGE before EXCAVATE on high-stakes decisions — missed C3 assumptions"]
  Anti-Pattern 2: [e.g., "Skipping BOUNDARY-DETECTOR on novel domains — skills activated on voids"]
  ...

────────────────────────────────────────

Artifact: pattern_extraction_report with patterns and anti-patterns.

CEI Component: Pattern Extraction (weight 4.0)

2 — TRANSFER TESTING (Validate Pattern Generalization)

Test each extracted pattern on held-out tasks to prove generalization.

TRANSFER TESTING
────────────────────────────────────────
Held-Out Task Suite: [min_transfer_tasks tasks from different domains]
  Task 1: [description] — Domain: [domain]
  Task 2: [description] — Domain: [domain]
  ...

For each pattern:
  Pattern: [name]
  Predicted Skill Sequence: [from pattern]
  Predicted ADS: [value]
  
  Results:
    Task 1: [ADS achieved] / [CEI] / [Gates passed] / [Match: YES/NO]
    Task 2: [ADS achieved] / [CEI] / [Gates passed] / [Match: YES/NO]
    ...
  
  Transfer Score: [fraction of tasks where pattern matched optimal sequence]
  Generalization: [BROAD / NARROW / DOMAIN-SPECIFIC]
  Confidence: [0.0-1.0]

Patterns Validated for Synthesis: [patterns with Transfer Score ≥ 0.6]
────────────────────────────────────────

Artifact: transfer_test_results — proves patterns aren't overfit.

CEI Component: Transfer Testing (weight 4.0)

3 — SKILL SYNTHESIS (Create New Skills from Patterns)

NEW IN v1.0 — Synthesize new skills from validated patterns.

For each pattern with Transfer Score ≥ 0.6 and Confidence ≥ synthesis_confidence_threshold:

SKILL SYNTHESIS PROPOSAL
────────────────────────────────────────
Pattern: [name]
Proposed Skill Name: [e.g., "schema-migration-architect"]
Proposed Codename: [e.g., "SCHEMA-MIGRATE"]
Proposed Tier: [cognition / excavation / integrity / governance / systems / meta]

Rationale: [why this pattern deserves a dedicated skill — not just a CONDUCTOR sequence]
  - Frequency: [how often this pattern occurs]
  - Specificity: [what unique logic it encapsulates]
  - Reusability: [across how many domains]

Proposed Protocol (SKILL.md structure):
  Phase 1: [steps]
  Phase 2: [steps]
  ...
  Gates: [which gates it verifies]
  Interferences: [what it provides/requires]
  Metrics: [ADS, CEI, SI targets]

Proposed Thinking Parameters:
  [specific parameters for this skill]

Implementation Notes:
  - Can be composed from existing skills: [YES/NO — if YES, it's a macro]
  - Requires new primitive: [YES/NO — if YES, needs new cognitive operation]
  - Estimated Complexity: [LOW/MEDIUM/HIGH]

Synthesis Confidence: [0.0-1.0]
Recommendation: [SYNTHESIZE / DEFER / REJECT]
────────────────────────────────────────

Artifact: skill_synthesis_proposals — proposals for new skills.

CEI Component: Skill Synthesis (weight 5.0)

4 — CONDUCTOR POLICY LEARNING (Update Selection Policy)

Update the CONDUCTOR's skill selection policy based on outcomes.

POLICY UPDATE
────────────────────────────────────────
Current Policy: [summary of CONDUCTOR's current selection rules]

Policy Updates (from this task + transfer tests):

Update 1: [Rule Change]
  Old Rule: [e.g., "For Category 3: DEEP-THINK → ADVERSARY → THRESHOLD"]
  New Rule: [e.g., "For Category 3 + database: DEEP-THINK → EXCAVATE → DIVERGE → ADVERSARY → TEMPORAL → THRESHOLD"]
  Evidence: [pattern name + transfer score]
  Confidence: [0.0-1.0]

Update 2: [Rule Change]
  ...

Update 3: [Parameter Change]
  Old: [e.g., "risk_threshold = 0.3 for Category 3"]
  New: [e.g., "risk_threshold = 0.25 for Category 3 + data-intensive"]
  Evidence: [ADS/CEI ratio improvement]
  Confidence: [0.0-1.0]

Parameter Optimizations:
  thinking_parameters updates:
    min_assumptions: [old → new]
    min_branches: [old → new]
    min_cim: [old → new]
    ...

Validation: [how to validate — A/B test on next N tasks]
────────────────────────────────────────

Artifact: policy_update — the updated CONDUCTOR policy.

CEI Component: Policy Learning (weight 4.0)

5 — REGRESSION TESTING (Validate Skill Library Health)

NEW IN v1.0 — Ensure skill updates don't degrade performance.

REGRESSION TEST RESULTS
────────────────────────────────────────
Benchmark Suite: [standard benchmark tasks]
Skill Versions Tested: [old vs new for each modified skill]

For each skill:
  Skill: [name]
  Old Version: [version]
  New Version: [version]
  
  Benchmark Results:
    Task 1: [Old ADS] vs [New ADS] — [Δ]
    Task 2: [Old ADS] vs [New ADS] — [Δ]
    ...
  
  Mean ADS Delta: [value]
  Mean CEI Delta: [value]
  Gate Pass Rate Delta: [value]
  
  Regression Check: [PASS if Mean ADS Delta ≥ -0.02 AND Gate Pass Rate Delta ≥ -0.05]
  Result: [PASS / FAIL]

Overall Library Health:
  Skills Tested: N
  Skills Passed: N
  Skills Failed: N
  New Skills Added: N
  Skills Deprecated: N
  
  Health Score: [0.0-1.0 — fraction of skills passing regression]
────────────────────────────────────────

If any skill FAILS regression: Block the update. Revert or fix.

Artifact: regression_test_results — the quality gate for skill evolution.

CEI Component: Regression Testing (weight 4.0)

6 — LIBRARY HEALTH REPORT

Comprehensive health check of the skill library.

LIBRARY HEALTH REPORT
────────────────────────────────────────
Library Version: [e.g., "2.1.0"]
Total Skills: [N]
  Core: [N]
  Premium: [N]
  Deprecated: [N]

Coverage Analysis:
  Task Profiles Covered: [list of profiles with dedicated skills]
  Gaps: [profiles with no dedicated skill]
  Redundancy: [skills with >0.8 CIM — consider merging]

Performance Metrics (rolling 30 days):
  Mean ADS: [value]
  Mean CEI: [value]
  Mean SI: [value]
  Gate Pass Rate: [value]
  User Satisfaction: [value if available]

Skill Lifecycle:
  New This Period: [list]
  Updated This Period: [list]
  Deprecated This Period: [list]
  Avg Skill Age: [days]

Technical Debt:
  Skills Without Transfer Tests: [N]
  Skills Without Regression Tests: [N]
  Skills With Known Gate Failures: [N]
  Skills Needing Refactor: [list]

Recommendations:
  1. [Priority 1 action]
  2. [Priority 2 action]
  3. [Priority 3 action]
────────────────────────────────────────

Artifact: library_health_report — the library's vital signs.

7 — METRICS: Compute and Output Depth Metrics

7.1 Phase Activation (for ADS)

{
  "phase": 5,
  "skill": "meta-learning",
  "artifact_completeness": 0.XX,
  "gate_pass_rate": 0.XX,
  "external_validity": 0.XX,
  "human_eval": 0.XX,
  "phase_activation": 0.XX
}

7.2 CEI Components

{
  "cei_components": {
    "pattern_extraction": 4.0,
    "transfer_testing": 4.0,
    "skill_synthesis": 5.0,
    "policy_learning": 4.0,
    "regression_testing": 4.0,
    "library_health": 2.0,
    "total_complexity_weight": 23.0,
    "output_tokens": N,
    "wall_time_seconds": T,
    "cei": 0.XX
  }
}

7.3 SI Components

{
  "si_components": {
    "c2_c3_count": 0,
    "contrarian_viable": false,
    "fatal_attacks": 0,
    "boundary_violations": 0,
    "assumption_reversals": 0,
    "si_total": 0
  }
}

7.4 Cognitive Traces

Pattern Extraction Visualization:

{
  "type": "pattern_extraction",
  "patterns": [
    {"name": "Pattern 1", "trigger": "...", "sequence": [...], "transfer_score": 0.XX, "confidence": 0.XX},
    {"name": "Pattern 2", "trigger": "...", "sequence": [...], "transfer_score": 0.XX, "confidence": 0.XX}
  ],
  "anti_patterns": [...]
}

Policy Update Visualization:

{
  "type": "policy_update",
  "updates": [
    {"rule": "Category 3 + database", "old": "...", "new": "...", "evidence": "...", "confidence": 0.XX}
  ],
  "parameter_optimizations": {...}
}

Regression Test Results: (as defined in Step 5)

7.5 Depth Contribution

{
  "skill": "meta-learning",
  "version": "1.0",
  "metrics": {"ads_contribution": 0.XX, "cei": 0.XX, "si": 0, "ec": 0.XX},
  "gates_verified": {"V5": true, "V6": true},
  "limitations": ["..."]
}

8 — GATES: Verify Before Delivery

MANDATORY — Verify these gates PASS:

If any gate FAILS: Return to relevant step and fix. Do not deliver.

9 — INTERFERENCE: Receive and Provide

Receive from prior skills (via CONDUCTOR orchestration log):

Provide to later skills (next task's CONDUCTOR):

Artifact: interference_log — the evolution signal for the next task.

The Deeper Purpose

This is the compounding intelligence mechanism. Without it, the skill library is a static artifact — useful but frozen. With it, every task teaches the system. Patterns become skills. Policies adapt. The library evolves. The ADS/CEI ratio improves over time.

Voyager showed this works for Minecraft agents. Reflexion showed it works for coding. This brings it to general reasoning skills. The skill library becomes a living organism that learns from its own use.

MATHEMATICS COMPLIANCE

Phase Activation Output

This skill contributes to Phase 5 (runs last, post-execution). It outputs:

{
  "phase": 5,
  "skill": "meta-learning",
  "artifact_completeness": 0.XX,
  "gate_pass_rate": 0.XX,
  "external_validity": 0.XX,
  "human_eval": 0.XX,
  "phase_activation": 0.XX
}

CEI Components

• Artifacts produced: pattern_extraction (4.0), transfer_testing (4.0), skill_synthesis (5.0), policy_learning (4.0), regression_testing (4.0), library_health (2.0)
• Total complexity weight: 23.0

SI Components

• C2/C3 assumptions: 0
• Contrarian viable: false
• Fatal attacks: 0
• Boundary violations: 0
• Assumption reversals: 0

Cognitive Traces Produced

• Pattern Extraction Visualization
• Policy Update Visualization
• Regression Test Results
• Decision Landscape

Gates Verified

• V1 [ ] V2 [ ] V3 [ ] V4 [x] V5 [x] V6
• Human Eval [ ] Held-Out [ ] Ensemble

Required Interferences

• From CONDUCTOR: Full orchestration log → pattern extraction
• From ALL SKILLS: Performance data → regression testing