Best use case
ward-identity-checker is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Ward Identity Checker
Teams using ward-identity-checker 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/ward-identity-checker/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ward-identity-checker Compares
| Feature / Agent | ward-identity-checker | 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?
Ward Identity Checker
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
# Ward Identity Checker
Verify GF(3) conservation as Ward identities across RG flow with Markov blanket separation.
## Seed
```
741086072858456200
```
## Core Principle
Ward identities express symmetry conservation: **Σ trit = 0 (mod 3)** at every renormalization group (RG) level. Violations indicate "relevant operators" that break the symmetry.
## MCP Calibration Data
```yaml
markov_blanket:
internal_states: ["#3FF1A7", "#10B99D", "#DF9811"]
sensory_indices: [1, 2, 3]
active_inference:
prediction_error: 0.5692
free_energy: 0.6692
recommendation: perceptual_inference
reafference:
prediction: "#F7E182"
sensation: "#3FF1A7"
result: identity_mismatch
```
## Predicates
### GF3Conserved(level)
```
GF3Conserved(L) := Σᵢ trit(cᵢ) ≡ 0 (mod 3)
where cᵢ ∈ colors_at_level(L)
```
### BlanketIntact(state)
```
BlanketIntact(s) := ∀ internal ∈ s.internal_states,
∃ blanket ∈ s.sensory_states ∪ s.active_states
such that internal ⊥ external | blanket
```
### NoLeakage(flow)
```
NoLeakage(f) := GF3Conserved(f.source) ∧ GF3Conserved(f.target)
∧ Σ trit(f.source) = Σ trit(f.target)
```
## Ward Identity Check Protocol
```python
def check_ward_identity(colors: list[str], level: int) -> dict:
"""Verify Σ trit = 0 at RG level."""
trits = [hex_to_trit(c) for c in colors]
total = sum(trits) % 3
return {
"level": level,
"trit_sum": total,
"conserved": total == 0,
"violation_type": None if total == 0 else "relevant_operator",
"correction_needed": (3 - total) % 3
}
def hex_to_trit(hex_color: str) -> int:
"""Map hex to GF(3) via hue angle."""
r, g, b = int(hex_color[1:3], 16), int(hex_color[3:5], 16), int(hex_color[5:7], 16)
hue = compute_hue(r, g, b)
return int(hue / 120) % 3 # 0-119 → 0, 120-239 → 1, 240-359 → 2
```
## Markov Blanket Separation
```
┌─────────────────────────────────────────────┐
│ EXTERNAL │
│ (exafference: world-caused sensations) │
├─────────────────────────────────────────────┤
│ BLANKET STATES │
│ Sensory: idx [1,2,3] → colors observed │
│ Active: predictions emitted │
├─────────────────────────────────────────────┤
│ INTERNAL │
│ #3FF1A7 (trit 1) ─┐ │
│ #10B99D (trit 1) ─┼─ Σ = 3 ≡ 0 (mod 3) ✓ │
│ #DF9811 (trit 1) ─┘ │
└─────────────────────────────────────────────┘
```
## Violation Detection
When reafference check shows identity mismatch (prediction ≠ sensation):
| Condition | Diagnosis | Action |
|-----------|-----------|--------|
| `GF3Conserved ∧ ¬BlanketIntact` | Boundary leak | Reseal blanket |
| `¬GF3Conserved ∧ BlanketIntact` | Relevant operator | Add counterterm |
| `¬GF3Conserved ∧ ¬BlanketIntact` | Full symmetry break | RG flow unstable |
## Usage
```bash
# Via Gay.jl MCP
gay_seed 741086072858456200
gay_markov_blanket --internal-seed 741086072858456200 --sensory-indices "1,2,3"
# Check conservation
just ward-check --level 0 --colors "#3FF1A7,#10B99D,#DF9811"
```
## Integration
- **cybernetic-immune**: Use Ward violations as non-self markers
- **active-inference**: Free energy ≈ Ward violation magnitude
- **unworld**: Color chain derivations must preserve Ward identity
## References
- Ward-Takahashi identities in QFT
- Friston's Markov blanket formalism
- GF(3) trit arithmetic for color conservation
## 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
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