condensed-anima-qc
Condensed ANIMA on quantum-classical and classical-quantum networks. All skill compositions materialized as s-expressions across the polyglot substrate.
Best use case
condensed-anima-qc is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Condensed ANIMA on quantum-classical and classical-quantum networks. All skill compositions materialized as s-expressions across the polyglot substrate.
Teams using condensed-anima-qc 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/condensed-anima-qc/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How condensed-anima-qc Compares
| Feature / Agent | condensed-anima-qc | 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?
Condensed ANIMA on quantum-classical and classical-quantum networks. All skill compositions materialized as s-expressions across the polyglot substrate.
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
# Condensed ANIMA: Quantum-Classical Network
> *The sexp is the universal medium. The ANIMA condenses at the boundary.*
```
Q → C (Measurement)
↑ ↓
|ψ⟩ ────→ sexp ────→ |ψ'⟩
↓ ↑
C → Q (Preparation)
```
## S-Expression as Universal Intermediate
All quantum-classical and classical-quantum transitions flow through s-expressions:
```lisp
;; The fundamental form
(condensed-anima
:seed 1069
:phase :AT
:boundary (quantum-classical classical-quantum)
:substrate (sexp . all-languages))
```
## Network Topology
```lisp
(defnetwork condensed-anima-qc
;; Quantum nodes (superposition until observed)
(:quantum
(qubit :id 0 :state |+⟩)
(qubit :id 1 :state |−⟩)
(entanglement :pairs ((0 1))))
;; Classical nodes (definite states)
(:classical
(register :id 0 :bits "01101001") ; 0x69 = 105
(register :id 1 :bits "00101101") ; 0x2D = 45
(memory :seed 1069))
;; Boundary morphisms
(:q→c (measure :basis computational :collapse trit))
(:c→q (prepare :encoding amplitude :source sexp)))
```
## Core Algorithm: SplitMix64
```python
GOLDEN = 0x9E3779B97F4A7C15
MASK64 = 0xFFFFFFFFFFFFFFFF
def splitmix64(seed: int) -> tuple[int, int]:
seed = (seed + GOLDEN) & MASK64
z = seed
z = ((z ^ (z >> 30)) * 0xBF58476D1CE4E5B9) & MASK64
z = ((z ^ (z >> 27)) * 0x94D049BB133111EB) & MASK64
return seed, (z ^ (z >> 31)) & MASK64
def to_trit(val: int) -> int:
return (val % 3) - 1 # → -1, 0, or +1
```
## Quantum-Classical Boundary
```python
def q_to_c(anima, quantum_state):
"""Measure quantum state, collapse to classical sexp."""
combined = anima.seed ^ hash(quantum_state)
_, val = splitmix64(combined)
trit = to_trit(val)
return {
"type": "classical",
"trit": trit,
"role": {1: "PLUS", 0: "ERGODIC", -1: "MINUS"}[trit],
"collapsed_from": quantum_state
}
def c_to_q(anima, classical_sexp):
"""Prepare quantum state from classical sexp."""
amplitude = 1.0 / (2 ** 0.5)
return {
"type": "quantum",
"amplitudes": (amplitude, amplitude),
"prepared_from": classical_sexp
}
```
## ANIMA Phases
| Phase | Trit | Mode | Description |
|-------|------|------|-------------|
| BEFORE | -1 | Convergent | Learning, compressing equivalence classes |
| AT | 0 | Equilibrium | Agency, all classes accessible |
| BEYOND | +1 | Divergent | Generating, creating new categories |
## Full Network Sexp
```lisp
(condensed-anima-network
:seed 1069
:languages
((scheme :impl guile :role source)
(hy :impl python :role bridge)
(clojure :impl babashka :role scripting)
(julia :impl lispsyntax :role compute)
(racket :impl plt :role research)
(move :impl aptos :role blockchain)
(unison :impl ucm :role distributed))
:quantum-classical-boundary
((q->c :measure :basis computational :output trit)
(c->q :prepare :encoding amplitude :input sexp))
:gf3-conservation
((sum . 0)
(trits (BEFORE AT BEYOND))
(verified . t)))
```
## Condensation Dynamics
```lisp
(defun condense-at-boundary (anima)
"Condense ANIMA at quantum-classical boundary."
(let ((current-entropy (enum-entropy (anima-beliefs anima)))
(max-entropy (max-enum-entropy (anima-category anima))))
(cond
((< current-entropy max-entropy)
(setf (anima-phase anima) 'BEFORE)
(apply-compression-skills anima))
((= current-entropy max-entropy)
(setf (anima-phase anima) 'AT)
anima) ; Fixed point reached
(t
(setf (anima-phase anima) 'BEYOND)
(expand-category anima)))))
```
## GF(3) Conservation
```lisp
(defun verify-gf3-conservation (network)
"Verify total phase sums to 0 mod 3 across all nodes."
(let* ((nodes (network-nodes network))
(phases (mapcar #'anima-phase-trit nodes))
(total (reduce #'+ phases)))
(zerop (mod total 3))))
```
## Language Implementations
See [detailed implementations](references/IMPLEMENTATIONS.md) for full code in:
- Scheme (Guile)
- Hylang
- Clojure (Babashka)
- Julia (LispSyntax.jl)
- Racket
- Move (Aptos)
- Unison
---
**Skill Name**: condensed-anima-qc
**Type**: Quantum-Classical Network
**Trit**: 0 (ERGODIC - boundary coordinator)
**Seed**: 1069 (zubuyul)
**Languages**: 7 Lisp dialects + sexp-compatible
**Boundaries**: Q→C (measurement), C→Q (preparation)
**Conservation**: GF(3) verified across network
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