npx claudepluginhub plurigrid/asi --plugin asiThis skill uses the workspace's default tool permissions.
**Status**: ✅ Production Ready
Implements Schmidhuber's curiosity-driven RL via compression progress as intrinsic reward. Guides agents to seek learnable states over novelty.
Compresses conversation history in long-running agent sessions exceeding context limits, using anchored iterative summarization, opaque compression, and regenerative summaries to optimize tokens-per-task.
Compresses conversation history in long-running agent sessions using anchored summarization, opaque compression, or regenerative summaries to optimize tokens-per-task and prevent information loss.
Share bugs, ideas, or general feedback.
Status: ✅ Production Ready Trit: +1 (PLUS - generator) Color: #D82626 (Red) Principle: Learning = Compression improvement Frame: Compressor improvement rate as reward signal
Compression Progress measures the derivative of compression ability over time. When a learner compresses data better than before, that improvement is intrinsic reward—the formal theory of curiosity and creativity.
r(t) = |C(t-1)(data)| - |C(t)(data)|
Curiosity reward = compression improvement rate
Boredom = zero progress (already compressed or incompressible)
def compression_progress(compressor_old, compressor_new, data) -> float:
"""Intrinsic reward from model improvement."""
old_bits = len(compressor_old.compress(data))
new_bits = len(compressor_new.compress(data))
return old_bits - new_bits # positive = learned something
class CuriousAgent:
def __init__(self):
self.world_model = Compressor()
self.history = []
def intrinsic_reward(self, observation) -> float:
old_len = self.world_model.compressed_length(observation)
self.world_model.update(observation)
new_len = self.world_model.compressed_length(observation)
return old_len - new_len # curiosity signal
def should_explore(self, state) -> bool:
"""Explore where compression progress is expected."""
return self.expected_progress(state) > self.threshold
def generate_interesting(compressor) -> Data:
"""Generate data that maximizes expected compression progress."""
candidates = sample_latent_space()
return max(candidates,
key=lambda x: expected_progress(compressor, x))
def select_next_task(tasks, compressor) -> Task:
"""Choose task with maximum learning potential."""
progress_estimates = [
estimate_compression_progress(compressor, task)
for task in tasks
]
# Not too easy (zero progress), not too hard (negative/zero)
return tasks[argmax(progress_estimates)]
# Measure compression progress
just compression-progress before.model after.model data/
# Generate curiosity curriculum
just curiosity-curriculum tasks.json
# Visualize learning trajectory
just compression-trajectory log.json
yoneda-directed (-1) ⊗ cognitive-superposition (0) ⊗ compression-progress (+1) = 0 ✓ [Riehl-Schmidhuber]
kolmogorov-compression (-1) ⊗ turing-chemputer (0) ⊗ compression-progress (+1) = 0 ✓ [Formal Learning]
Skill Name: compression-progress Type: Curiosity Generator Trit: +1 (PLUS) Color: #D82626 (Red)
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.