Customization

Before executing, check for user customizations at: ~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/Ideate/

Ideate — The Cognitive Progress Engine

What It Does

Ideate is a loop-controlled evolutionary creativity engine. It runs multiple cycles of consuming, dreaming, stealing, breeding, and testing ideas over simulated time scales from hours to decades, driven by a first-class Loop Controller and a Lamarckian Meta-Learner. It produces ranked novel solution candidates with full provenance — where each idea came from and how it evolved.

The Problem

A single-pass brainstorm collapses fast. Ask a model for ideas and it converges on the obvious handful, biased toward its training distribution, because soft temperature tweaks just reshuffle the same probability mass. You get variations on one theme, not genuinely different directions. Hard problems need ideas that came from somewhere else — a foreign domain, an unexpected recombination, a constraint flipped on its head — and they need a way to kill the weak ones and breed the strong ones across many rounds. One pass can't do that.

How It Works

This is an evolutionary system, not a single-pass tool. This is NOT BeCreative — BeCreative is a single-pass diversity tool. Ideate runs multiple cycles driven by a Loop Controller and a Lamarckian Meta-Learner.

The Core Insight

Human creativity reduces to 5 irreducible functions:

The 9 workflow phases expand these into a richer human-legible system. DREAM, DAYDREAM, and CONTEMPLATE are PERTURB at different noise levels. MATE is PERTURB on existing ideas. META-LEARN adds the Lamarckian advantage — analyzing WHY ideas worked and steering future generation.

The 9 Phases (Summary)

Post-loop: the Insight Extractor runs for cross-cycle pattern analysis.

Full phase mechanics live in Workflows/FullCycle.md.

Workflow Routing

The Loop Controller

Owns inter-cycle state and makes continue/pivot/stop decisions after each cycle's META-LEARN phase. State tracked:

{
  "cycle_count": 0,
  "max_cycles": null,
  "budget_seconds_remaining": 600,
  "fitness_history": [{"cycle": 1, "avg_score": 52.3, "top_score": 68.1, "diversity_index": 0.91}],
  "stagnation_counter": 0,
  "strategy_version": 1,
  "strategy_adjustments": {},
  "loop_decision_log": []
}

Loop Gate logic:

IF budget_seconds_remaining <= 0:        STOP (budget exhausted)
ELIF stagnation_counter >= 3:
    IF strategy_pivots_remaining > 0:    PIVOT (shift domains/noise/agents)
    ELSE:                                STOP (exhausted strategies)
ELIF diversity_index < 0.3:              PIVOT (collapse — inject immigrants)
ELIF top_score >= target_score:          STOP (target reached)
ELSE:                                    CONTINUE

Structural Randomness Engine

LLM "temperature" is soft probability redistribution biased toward the training distribution. Ideate uses structural randomness at the data level instead:

• Input subsetting (DREAM): Fisher-Yates shuffle picks each agent's input subset
• Domain lottery (STEAL): weighted random sampling from the 50+ candidate domain pool
• Pairing shuffle (MATE): Fisher-Yates pairs adjacent items; 20% slots forced cross-phase
• Mutation dice (EVOLVE): roll an 8-sided die, apply that mutation operation:
  1. Flip one assumption
  2. Invert the constraint
  3. Change the scale (10× bigger or smaller)
  4. Change the time horizon
  5. Merge with a random killed idea's best element
  6. Apply a constraint from a random domain
  7. Remove the most complex component
  8. Add an adversarial requirement

Implementation: crypto.getRandomValues() with seed = cycle number + problem hash.

External Validation Hooks (TEST extension)

Optional pluggable interface that adds real-world signal to internal scoring:

interface ValidationHook {
  name: string;
  validate(idea: Idea, problem: Problem): Promise<{ modifier: number; evidence: string }>;
}

Built-in hooks: MarketSearch (existing implementations), FeasibilityCheck (technical blockers), ExpertPanel (async human review), PrototypeSimulation (generate + test prototype).

Time-Scale Configuration

Loop Controller decides actual cycle count adaptively, not a fixed count.

State Persistence

Each run persists to ~/.claude/LIFEOS/MEMORY/WORK/{slug}/ideate/:

ideate/
  config.json           # Problem, time_scale, domains, hooks
  loop-state.json       # Loop Controller (fitness_history, strategy, decisions)
  domain-pool.json      # Weighted domain pool (expanded across cycles)
  cycle-NNN/            # Per-cycle artifacts: input-pool, dreams, daydreams,
                        # analyses, checkpoint-a, stolen, offspring, scores,
                        # checkpoint-b, survivors, meta-learning, summary
  insights.md           # Insight Extractor output (post-loop)
  final-output.md       # Ranked candidate list with full provenance

Idea Data Structure

{
  "id": "idea-042",
  "text": "...",
  "provenance": {
    "parents": ["idea-017", "idea-023"],
    "operation": "crossover",
    "mutation_type": "scale_change",
    "mutation_die_roll": 3,
    "cycle": 3, "phase": "MATE",
    "source_domains": ["mycology", "distributed-systems"],
    "randomness_seed": "a7f3c9..."
  },
  "scores": {
    "feasibility": 72, "novelty": 88, "impact": 65, "elegance": 81,
    "composite": 76.5, "confidence": 0.82, "judge_variance": 8.3,
    "external_validation": {"market_search": {"modifier": -5, "evidence": "..."}},
    "adjusted_composite": 74.5
  },
  "arguments": {"supporting": "...", "counter": "..."}
}

Final Output Format

# Ideate Results: [Problem]

**Time scale:** [scale] | **Budget used:** X of Y min | **Cycles:** N (adaptive)
**Strategy pivots:** M | **Total ideas:** X | **Survived:** Y | **Kill rate:** Z%

## Top Candidates (ranked by adjusted composite score)

### 1. [Title] — Score: 85.2/100 (confidence: 0.91)

**The idea:** [2-3 sentences]
**Scores:** Feasibility: 78 | Novelty: 92 | Impact: 84 | Elegance: 87
**External validation:** [hook results]
**Provenance:** Born in cycle N from [operation] of [parents]. Mutation: [type].
**For it:** [supporting argument]
**Against it:** [counterargument]

## Evolution Summary
| Cycle | Ideas In | Survived | Top Score | Diversity | Strategy | Decision |
|-------|----------|----------|-----------|-----------|----------|----------|

## Meta-Learning Trajectory
- [How strategy evolved across cycles]

## Evolutionary Insights (from The Historian)
- [Dominant lineages, fertile combinations, fitness landscape, problem revelations]

Configuration

{
  "problem": "...",
  "time_scale": "weeks",
  "domains": ["primary", "adjacent-1", "adjacent-2"],
  "scoring_weights": {"feasibility": 1.0, "novelty": 1.0, "impact": 1.0, "elegance": 1.0},
  "convergence_prevention": {
    "cross_phase_breeding_min": 0.2,
    "immigrant_ideas_per_cycle": 3,
    "kill_threshold": 0.5,
    "forced_new_domain_per_cycle": true
  },
  "loop_control": {
    "mode": "adaptive",
    "target_score": null,
    "max_stagnation_cycles": 3,
    "max_strategy_pivots": 2,
    "diversity_floor": 0.3
  },
  "external_validation": {"enabled": false, "hooks": ["MarketSearch"]},
  "randomness": {"seed": null, "subset_ratio": 0.33, "mutation_operations": 8}
}

Integration with Other Skills

Algorithm Integration

When the Algorithm runs an ideation cycle it loads this skill and routes to Workflows/FullCycle.md by default. Tunable parameters from the algorithm's archived LIFEOS/ALGORITHM/archive/parameter-schema.md (historical — the mode system retired 2026-07-11) map to the configuration above. The Meta-Learner may adjust parameters within bounds; user-explicit overrides are auto-locked.

Examples

• "id8 on retention strategies for the newsletter" → FullCycle: all 9 phases, Loop Controller decides cycle count, ranked candidates with provenance.
• "quick novelty pass on these three feature ideas" → QuickCycle: one compressed cycle with fitness scoring.
• "steal ideas from biology for cache invalidation" → Steal: cross-domain borrowing only, no full evolution loop.

Gotchas

• Ideate is for multi-cycle evolutionary ideation — not quick brainstorming. For fast divergent ideas, use BeCreative.
• The Loop Controller manages cycle count — don't override it manually. Trust the budget-based cycling.
• Meta-learner adjustments happen automatically within parameter bounds. Don't manually tune mid-cycle.
• CONTEMPLATE is mandatory. Skipping it degrades MATE quality because STEAL operates on disconnected material.
• Structural randomness defeats LLM bias. Don't substitute "interesting pairs picked by the LLM" for Fisher-Yates — the bias is the problem.

Citations

• The 9-phase decomposition and the path-to-ASI mapping derive from a publicly published 2024 essay on cognitive progress and a possible path to ASI. The framework name Cognitive Progress Workflow refers to that essay.
• The Lamarckian advantage framing (Phase 9 META-LEARN) borrows from research on auto-research loops and meta-learning in agent systems (cf. Karpathy auto-research pattern).
• Structural randomness as a defeat for LLM-bias is empirical — see internal experiments comparing LLM-picked pairings vs Fisher-Yates pairings on diversity metrics.

Execution Log

After completing any workflow, append a single JSONL entry:

echo '{"ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","skill":"Ideate","workflow":"WORKFLOW_USED","input":"8_WORD_SUMMARY","status":"ok|error","duration_s":SECONDS}' >> ~/.claude/LIFEOS/MEMORY/SKILLS/execution.jsonl