npx claudepluginhub plurigrid/asi --plugin asiThis skill uses the workspace's default tool permissions.
```yaml
Trains interaction predictors and extracts temporal, topic, and behavioral patterns from DuckDB interaction data using Python. Discovers latent skills for cognitive surrogate systems.
Distills public figures' cognitive frameworks, mental models, decision heuristics, and expression patterns into runnable Claude Code SKILL.md perspective skills via Nuwa methodology.
Guides ACT-R cognitive model construction: chunk types, production rules, subsymbolic parameters, fitting to behavioral data, and validation. For pyactr (Python) or ACT-R 7.x (Lisp) implementations.
Share bugs, ideas, or general feedback.
name: cognitive-surrogate
description: Layer 6 Barton Cognitive Surrogate System - build, train, and validate psychological models that predict and generate authentic responses matching a subject's cognitive patterns
version: 1.0.0
trit: 0 # Ergodic/coordinator role in GF(3) triadic system
"We are building cognitive infrastructure for the next trillion minds" — Plurigrid: the story thus far
Active Inference Foundation: Cognitive surrogates implement the core Active Inference in String Diagrams pattern where an agent maintains a generative model of itself and others. The surrogate IS the generative model — it predicts responses by minimizing free energy between expected and observed behavior.
Self-Model as Markov Blanket: The surrogate defines the statistical boundary (Markov blanket) between self and world. The model's internal states predict the subject's behavior while remaining conditionally independent of external states given sensory input.
Reafference in Surrogates: When the surrogate predicts a response that matches actual behavior, this is reafference — self-caused sensation that confirms identity. Mismatches are exafference — signals that the model needs updating.
Autopoietic Surrogates: Following Maturana & Varela's autopoiesis concept, the surrogate is a self-maintaining system that reproduces its own patterns. It embodies bmorphism's autopoietic ergodicity — self-sustaining systems that explore all accessible cognitive states.
The Cognitive Surrogate skill enables construction of high-fidelity psychological models from interaction patterns. It extracts values, predicts intellectual trajectories, and generates authentic responses that preserve the subject's voice with >90% fidelity.
Core Principle: A surrogate is not an imitation but a derivational continuation - the model learns the generative grammar of cognition, not surface patterns.
NEW (Langevin/Gibbs Integration): Predictions now use Gibbs distribution from Langevin analysis. Confidence scores reflect mixing time and temperature parameters.
Extract structured psychological profile from interaction corpus:
profile = build_psychological_profile(
corpus=[threads, emails, writings],
dimensions=[
"values", # Core beliefs, ethical framework
"interests", # Topic affinities, curiosity patterns
"communication", # Tone, formality, humor markers
"reasoning", # Deductive vs intuitive, uncertainty handling
"social", # Collaboration style, conflict patterns
],
seed=gay_seed # Deterministic via gay-mcp
)
Output ACSets Schema:
@present SchProfile(FreeSchema) begin
Value::Ob; Interest::Ob; Pattern::Ob
name::Attr(Value, String)
weight::Attr(Value, Float64)
topic::Attr(Interest, String)
frequency::Attr(Interest, Int)
exemplar::Attr(Pattern, String)
end
Train next-topic and next-response predictors:
predictor = train_predictor(
profile=profile,
corpus=corpus,
model_type="markov_transformer", # or "hmm", "lstm", "gpt_finetune"
context_window=8, # conversation turns
validation_split=0.15
)
# Predict likely next topics given conversation state
next_topics = predictor.predict_topics(
context=current_thread,
top_k=5,
temperature=0.7
)
Measure surrogate fidelity against held-out data:
fidelity = validate_fidelity(
surrogate=predictor,
test_corpus=held_out_threads,
metrics=[
"topic_prediction_accuracy", # Target: >0.85
"response_semantic_sim", # Target: >0.90
"style_consistency", # Target: >0.88
"value_alignment", # Target: >0.92
],
threshold=0.90 # Overall fidelity threshold
)
assert fidelity.overall >= 0.90, f"Fidelity {fidelity.overall} below threshold"
Fidelity Report:
┌─────────────────────────┬────────┬────────┐
│ Metric │ Score │ Target │
├─────────────────────────┼────────┼────────┤
│ topic_prediction │ 0.87 │ 0.85 │
│ semantic_similarity │ 0.91 │ 0.90 │
│ style_consistency │ 0.89 │ 0.88 │
│ value_alignment │ 0.94 │ 0.92 │
├─────────────────────────┼────────┼────────┤
│ OVERALL │ 0.9025 │ 0.90 │
└─────────────────────────┴────────┴────────┘
Generate text matching subject's voice:
reply = generate_authentic_reply(
surrogate=predictor,
context=conversation_history,
prompt=incoming_message,
constraints={
"max_tokens": 500,
"preserve_uncertainty": True, # Don't overclaim knowledge
"style_lock": True, # Enforce style consistency
},
gay_seed=thread_seed # Reproducible generation
)
# Reply includes confidence and divergence markers
print(reply.text)
print(f"Confidence: {reply.confidence}")
print(f"Style divergence: {reply.style_delta}")
Use Gibbs distribution from Langevin dynamics for predictions:
# Instead of generic pattern matching
# Use p(pattern | θ) ∝ exp(-L(θ)/T) from Langevin
prediction = gibbs_based_prediction(
state=current_state,
temperature=0.01, # From Langevin analysis
loss_fn=pattern_energy,
mixing_time=500 # Estimated convergence time
)
# Adaptive confidence based on equilibration
confidence = estimate_equilibrium(
steps_so_far=n,
mixing_time=tau_mix,
temperature=T
)
# Returns:
# - prediction: Most likely next state
# - confidence: P(equilibrium reached)
# - temperature_influence: How much T affected prediction
Predict intellectual/professional trajectory:
trajectory = project_trajectory(
profile=profile,
horizon="6_months",
dimensions=[
"research_interests",
"skill_acquisition",
"collaboration_patterns",
"publication_topics",
],
monte_carlo_samples=1000,
seed=gay_seed
)
# Returns probability distributions over future states
for dim, distribution in trajectory.items():
print(f"{dim}: {distribution.mode} (p={distribution.confidence:.2f})")
All stochastic operations use gay-mcp deterministic seeding:
from gay_mcp import GaySeed, derive_color
# Create reproducible surrogate session
session_seed = GaySeed.from_thread(thread_id)
profile_color = derive_color(session_seed, "profile") # trit=0
training_color = derive_color(session_seed, "train") # trit=1
validation_color = derive_color(session_seed, "valid") # trit=2
# GF(3) conservation: 0 + 1 + 2 ≡ 0 (mod 3)
assert (profile_color.trit + training_color.trit + validation_color.trit) % 3 == 0
Profile data stored as attributed C-sets:
using ACSets
# Define cognitive schema
@acset_type CognitiveProfile(SchProfile)
# Populate from extraction
profile = CognitiveProfile()
add_part!(profile, :Value, name="intellectual_honesty", weight=0.95)
add_part!(profile, :Interest, topic="category_theory", frequency=47)
add_part!(profile, :Pattern, exemplar="tends to use analogies from music")
# Query patterns
high_values = incident(profile, x -> x.weight > 0.8, :Value)
| Component | Trit | Role |
|---|---|---|
| cognitive-surrogate | 0 | Coordinator/ergodic - orchestrates profile building |
| gay-mcp | 1 | Generator - provides deterministic randomness |
| acsets | 2 | Storage - structured data persistence |
Conservation: 0 + 1 + 2 ≡ 0 (mod 3) — closed triadic system.
# Complete surrogate construction workflow
from cognitive_surrogate import (
build_psychological_profile,
train_predictor,
validate_fidelity,
generate_authentic_reply,
project_trajectory
)
from gay_mcp import GaySeed
# Initialize with deterministic seed
seed = GaySeed.from_string("barton_surrogate_v1")
# 1. Build profile from corpus
profile = build_psychological_profile(
corpus=load_interaction_corpus("barton_threads/"),
seed=seed
)
# 2. Train predictor
predictor = train_predictor(profile, model_type="markov_transformer")
# 3. Validate fidelity
fidelity = validate_fidelity(predictor, test_corpus, threshold=0.90)
print(f"Fidelity: {fidelity.overall:.2%}")
# 4. Generate replies
reply = generate_authentic_reply(
predictor,
context=current_conversation,
prompt="What do you think about applying category theory to social systems?"
)
# 5. Project trajectory
trajectory = project_trajectory(profile, horizon="1_year")
print(f"Predicted focus: {trajectory['research_interests'].mode}")
skills/cognitive-surrogate/
├── SKILL.md # This file
├── src/
│ ├── profile_builder.py # Psychological extraction
│ ├── predictor.py # Training and prediction
│ ├── fidelity.py # Validation metrics
│ ├── generator.py # Authentic reply generation
│ └── trajectory.py # Future projection
├── schemas/
│ └── cognitive_acset.jl # ACSets schema definitions
└── tests/
└── test_fidelity.py # >90% threshold tests
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
general: 734 citations in bib.duckdbThis skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
The skill participates in triads satisfying:
(-1) + (0) + (+1) ≡ 0 (mod 3)
This ensures compositional coherence in the Cat# equipment structure.