Best use case
obstruction-learning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Obstruction Learning Skill
Teams using obstruction-learning should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/obstruction-learning/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How obstruction-learning Compares
| Feature / Agent | obstruction-learning | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Obstruction Learning Skill
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Obstruction Learning Skill
Learn topological ASI via random walk obstruction detection and Čech H⁰ cohomology.
## Metadata
| Property | Value |
|----------|-------|
| **Name** | obstruction-learning |
| **Trit** | -1 (VALIDATOR) |
| **Category** | Topological Verification |
| **Dependencies** | sheaf-cohomology, ramanujan-expander, gay-mcp |
## Core Concept
**Obstructions are H⁰ generators** - irreducible elements that block global consistency from local patches.
```
Čech Cohomology: H⁰(U, F) = ker(d₀: F(U) → ∏ᵢⱼ F(Uᵢ ∩ Uⱼ))
Obstruction detected when:
- GF(3) conservation violated (sum ≢ 0 mod 3)
- Voice triads don't harmonize
- Skill compositions conflict
- Local patches fail to glue globally
```
## Random Walk Reconstruction
### The 69-Skill Walk
Sample 69 skills from the 181-skill manifold:
```bash
# Execute random walk
just random-walk-69
# Verify GF(3) conservation
just verify-gf3
# Track cumulative obstructions
just random-walk-obstruction 69
```
### Obstruction Detection
```sql
-- Find unbalanced cells in 23³ grid
SELECT cell_id, skill_count, trit_sum, gf3_status
FROM cell_density
WHERE gf3_status = 'UNBALANCED';
-- H⁰ generators by trit class
SELECT trit, COUNT(*) as generators
FROM skills
GROUP BY trit;
```
## Mathematical Foundations
### Čech Cohomology
For a covering U = {Uᵢ} of skill space:
```
H⁰(U, F) = { s ∈ F(U) | d₀(s) = 0 }
where d₀: F(U) → ∏ F(Uᵢ ∩ Uⱼ)
maps global sections to intersection restrictions
```
**Obstruction** = element of H⁰ that prevents gluing.
### GF(3) as Cohomology
The GF(3) conservation law is a discrete cohomology:
```
Trit assignment: skill → {-1, 0, +1}
Coboundary: d(triad) = sum of trits mod 3
H⁰ = { triads | d(triad) = 0 } = balanced triads
Obstruction = triad with d ≠ 0
```
### Ramanujan Mixing
Random walks on Ramanujan expanders mix optimally:
```
λ₂ ≤ 2√(d-1) [Alon-Boppana bound]
gap = d - λ₂ [Spectral gap]
τ_mix = O(log n / gap) [Mixing time]
```
## Workflow
### 1. Pre-Interaction Sync
```bash
just pre-interaction
# Syncs plurigrid/asi arena + hdresearch/duck
# Loads GF(3) skill triad
# Computes spectral awareness
```
### 2. Random Walk Sampling
```bash
# Sample without replacement (maximal coverage)
just random-walk-69
# Sample with replacement (GF(3) conservation)
just random-walk 23
```
### 3. Obstruction Detection
```bash
# Find H⁰ generators
just obstruction-h0
# Detect unbalanced cells
just obstruction-detect
# Balance with complementary skill
just obstruction-balance -1 # Find validators to add
just obstruction-balance +1 # Find generators to add
```
### 4. Audio Generation
Convert obstruction traces to sound:
```bash
just audio-from-trace
```
## Integration Patterns
### With Voice Enforcement
```toml
# voice-enforcement.toml
[triads.obstruction]
validator = "Milena (Enhanced)" # -1: detects obstruction
coordinator = "Petra (Premium)" # 0: mediates resolution
generator = "Federica (Premium)" # +1: proposes fix
sum = 0
```
### With Dune Orthogonalization
The 23×23×23 grid maps skills to:
| Axis | Dimensions |
|------|------------|
| DATA | chain_indexing → real_time_streaming |
| INTERFACE | sql_query_engine → ai_copilot |
| INFRASTRUCTURE | kubernetes → multi_tenant_isolation |
### With World Extractable Value
```
WEV = PoA - 1 = extractable coordination loss
Obstruction → WEV > 0
Resolution → WEV → 0
Global consistency → Optimal coordination
```
## Commands
```bash
# Full ASI learning loop
just asi-learn
# Spectral bounds
just spectral-bounds
# World Extractable Value
just wev-compute
# Voice obstruction analysis
just voice-obstructions
```
## Skill Triad
This skill belongs to the **topological verification** triad:
| Role | Skill | Trit |
|------|-------|------|
| VALIDATOR | **obstruction-learning** | -1 |
| COORDINATOR | sheaf-cohomology | 0 |
| GENERATOR | persistent-homology | +1 |
**Sum = 0** ✓ GF(3) conserved
## References
- Bott & Tu, *Differential Forms in Algebraic Topology*
- Lurie, *Higher Topos Theory*
- Riehl-Shulman, *Synthetic ∞-categories*
- QRI, *Symmetry Theory of Valence*
## Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
### Graph Theory
- **networkx** [○] via bicomodule
- Universal graph hub
### Bibliography References
- `general`: 734 citations in bib.duckdb
## Cat# Integration
This 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
```
### GF(3) Naturality
The skill participates in triads satisfying:
```
(-1) + (0) + (+1) ≡ 0 (mod 3)
```
This ensures compositional coherence in the Cat# equipment structure.Related Skills
forward-forward-learning
Hinton's Forward-Forward algorithm for local learning without backpropagation. Use for biologically plausible learning, on-chip training, memory-efficient networks, or parallel layer-wise training.
Feedforward Learning Local
**Category:** Phase 3 Core - Alternative Learning Paradigms
zx-calculus
Coecke's ZX-calculus for quantum circuit reasoning via string diagrams with Z-spiders (green) and X-spiders (red)
zulip-cogen
Zulip Cogen Skill 🐸⚡
zls-integration
zls-integration skill
zig
zig skill
zig-syrup-bci
Multimodal BCI pipeline in Zig: DSI-24 EEG, fNIRS mBLL, eye tracking IVT, LSL sync, EDF read/write, GF(3) conservation
zig-programming
zig-programming skill
zeroth-bot
Zeroth Bot - 3D-printed open-source humanoid robot platform for sim-to-real and RL research. Affordable entry point for humanoid robotics.
xlsx
Comprehensive spreadsheet creation, editing, and analysis with support
wycheproof
Google's Wycheproof test vectors for cryptographic implementation testing.
Writing Hookify Rules
This skill should be used when the user asks to "create a hookify rule", "write a hook rule", "configure hookify", "add a hookify rule", or needs guidance on hookify rule syntax and patterns.