Customization

Before executing, check for user customizations at: ~/.claude/PAI/USER/SKILLCUSTOMIZATIONS/Ideate/

Ideate — The Cognitive Progress Engine

A loop-controlled evolutionary creativity engine that mirrors human cognitive processes to generate genuinely novel ideas. This is NOT BeCreative — BeCreative is a single-pass diversity tool. Ideate is an evolutionary system: 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.

The Core Insight

Human creativity reduces to 5 irreducible functions:

Function	What It Does	Human Analog
INGEST	Gather diverse raw material	Reading, conversations, experiences
PERTURB	Recombine inputs with controlled noise	Dreaming, daydreaming, shower thoughts
CROSS-POLLINATE	Map patterns from foreign domains	"Stealing" ideas from unrelated fields
SELECT	Score against fitness function	Critical thinking, peer review, testing
ITERATE	Feed survivors back as inputs	Sleep cycles, weeks of study, years of work

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)

#	Phase	Noise	What it does	Agent
1	CONSUME	—	Multi-domain research, atomic idea extraction	The Glutton
2	DREAM	0.9	Free-association on random input subsets, no problem awareness	The Dreamer
3	DAYDREAM	0.5	Tangential wandering with the problem held loosely	The Wanderer
4	CONTEMPLATE	0.1	Structured analysis via 4 lenses (mandatory; checkpoint A gates)	The Sage
5	STEAL	—	Cross-domain pattern borrowing via weighted random domain lottery	The Thief
6	MATE	—	Genetic recombination via Fisher-Yates shuffle + 8 mutation operations	The Matchmaker
7	TEST	—	Multi-judge scoring on Feasibility/Novelty/Impact/Elegance (checkpoint B gates)	The Judge
8	EVOLVE	—	Selection: kill bottom 50%, elite top 10%, mutate the rest, immigrant injection	The Curator
9	META-LEARN	—	Lamarckian strategy adjustment + next-cycle question generation	The Scientist

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

Full phase mechanics live in Workflows/FullCycle.md.

Workflow Routing

User says...	Workflow
"ideate", "id8", "novel ideas for X", "evolve ideas for X", default	Workflows/FullCycle.md
"quick novelty for X", "fast brainstorm with scoring"	Workflows/QuickCycle.md
"dream on X", "free-associate these inputs", "wild recombinations"	Workflows/Dream.md
"steal ideas from biology for X", "cross-pollinate from Y"	Workflows/Steal.md
"breed these ideas", "recombine X and Y"	Workflows/Mate.md
"score these candidates", "test these ideas against fitness"	Workflows/Test.md

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

Time scale	Budget	Est. cycles	Agents/phase
hours	5 min	1-2	2-3
days	12 min	2-4	3-4
weeks	25 min	3-8	4-5
months	45 min	5-15	5-6
years	90 min	8-30	6-8
decades	180 min	15-50+	8-10

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

State Persistence

Each run persists to ~/.claude/PAI/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

Skill	Phase	How
Research	CONSUME, STEAL	Multi-agent parallel research, cross-domain patterns
BeCreative	DREAM, DAYDREAM	MaximumCreativity workflow for high-noise recombination
IterativeDepth	CONTEMPLATE	4-lens analysis (Literal, Failure, Analogical, Constraint Inversion)
FirstPrinciples	CONTEMPLATE	Decompose to axioms, challenge assumptions
RedTeam	TEST	Adversarial attack on candidates to find fatal flaws
Agents	ALL	ComposeAgent for unique cognitive personalities per phase
Council	MATE (optional)	Debate between ideas before breeding

Algorithm Integration

When the PAI Algorithm sets mode: ideate (via PAI/ALGORITHM/ideate-loop.md), it loads this skill and routes to Workflows/FullCycle.md by default. Tunable parameters from the algorithm's parameter-schema.md map to the configuration above. The Meta-Learner may adjust parameters within bounds; user-explicit overrides are auto-locked.

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 essay on cognitive progress and a possible path to ASI by D. {{PRINCIPAL_SURNAME}} (2024). 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/PAI/MEMORY/SKILLS/execution.jsonl