AI Skills Studio

The AI Skills Studio provides pre-built, operator-verified skill templates for 12 industries. Each skill chains specific operators from the 1,576-operator registry with HulyaPulse synchronization at 1.287 Hz (Zeqond = 0.777s), delivering reproducible, precision-checked results.

Twelve Industry Templates

Every template defines a curated operator chain, default parameters, and a precision target. Select one as a starting point, then customize for your domain.

#	Template	Key Operators	Example Use Case
1	Healthcare & Medicine	MED1-3, QM1-2, KO42	Pharmacokinetics dosing, radiation therapy planning
2	Aerospace & Aviation	HOHMANN_TRANSFER, ORBIT_VELOCITY, ESCAPE_VELOCITY, GR35-37	Mission planning, orbital mechanics with relativistic corrections
3	Quantum Computing	QM1-5, QM11-12	Circuit simulation, error-correction syndrome extraction
4	AI & Machine Learning	CS43-47, ON0, QL1	Neural architecture analysis, entropy-based model evaluation
5	Finance & Economics	FIN1-2, CS47, NM30	Options pricing (Black-Scholes), portfolio risk (VaR)
6	Biotechnology	BIO1-2, QM1, QM8	Molecular dynamics, protein folding binding-affinity estimates
7	Energy Systems	ENERGY1-2, NM22-25	Solar panel optimization, grid load balancing
8	Manufacturing & Industry	CS43, CS46, NM19, NM22	Quality control, entropy-based process optimization
9	Environmental Science	ENV1-2, GR40	Climate modeling (RCP scenarios), CO2 projections
10	Defense & Security	CS87, CS47, NM19, NM26-27	Trajectory analysis, FFT spectral signal processing
11	Education & Research	QM1-2, NM18-21, GR33	Interactive physics demonstrations, reproducible experiments
12	Custom Industry	User-defined	Any operator chain from the full 1,576-operator registry

Template Details

• Healthcare: MED1-3 cover drug absorption, distribution, and elimination. QM1-2 add quantum-level molecular interaction corrections. KO42 provides kinetic-order differential evaluation for dosimetry.
• Aerospace: HOHMANN_TRANSFER computes minimum-energy transfer orbits. GR35-37 apply general-relativistic corrections for high-precision ephemeris and deep-space navigation.
• Quantum Computing: QM1-5 cover Hamiltonian evolution, measurement, and entanglement. QM11-12 handle decoherence modeling and error-correction syndrome extraction.
• AI & ML: CS43-47 span information-theoretic metrics (cross-entropy, KL divergence, mutual information). ON0 normalizes operators. QL1 provides quantum-inspired loss landscape analysis.
• Finance: FIN1 implements Black-Scholes pricing. FIN2 covers Value-at-Risk. NM30 provides Monte Carlo numerical integration for simulation-based methods.
• Biotechnology: BIO1 handles molecular force-field computation. BIO2 covers cellular reaction kinetics. QM1/QM8 add quantum corrections for electron orbital interactions in binding-site analysis.
• Energy: ENERGY1 models photovoltaic efficiency under variable irradiance. ENERGY2 covers thermodynamic cycle optimization. NM22-25 supply ODE solvers, interpolation, and optimization.
• Manufacturing: CS43 provides entropy-based anomaly detection. CS46 covers information gain. NM19 handles regression for process modeling. NM22 supplies ODE solvers for dynamic simulation.
• Environmental: ENV1 implements RCP scenario modeling. ENV2 covers carbon cycle dynamics. GR40 provides atmospheric density corrections for satellite remote sensing.
• Defense: CS87 evaluates cryptographic entropy. CS47 provides signal-to-noise metrics. NM26-27 supply FFT-based spectral analysis and filtering.
• Education: QM1-2 enable quantum mechanics visualizations. NM18-21 provide a complete numerical methods toolkit. GR33 covers spacetime curvature visualization.
• Custom: Select any combination of operators, define your own schemas and precision targets.

Creating a Custom Skill

A skill is a reusable computation template: a named chain of operators with configuration, input/output schemas, and precision targets.

Skill Definition (JSON)

{
  "name": "pharmacokinetics-dosing",
  "version": "1.0.0",
  "description": "Compute optimal drug dosing intervals using two-compartment PK model",
  "template": "healthcare",
  "operators": ["MED1", "MED2", "KO42"],
  "sync": {
    "frequency_hz": 1.287,
    "zeqond_s": 0.777,
    "phase_coherence_target": 0.1
  },
  "input_schema": {
    "drug_name": "string",
    "dose_mg": "number",
    "patient_weight_kg": "number",
    "renal_clearance_ml_min": "number"
  },
  "output_schema": {
    "half_life_h": "number",
    "peak_concentration_mg_l": "number",
    "trough_concentration_mg_l": "number",
    "recommended_interval_h": "number",
    "master_sum": "number",
    "phase_coherence": "number"
  },
  "precision": {
    "target_tolerance": 0.001,
    "max_phase_coherence": 0.1
  }
}

Python Example

from zeq_os import SkillStudio, HulyaSync

studio = SkillStudio()
sync = HulyaSync()

skill = studio.create_skill(
    name="solar-optimizer",
    description="Optimize PV panel configuration for maximum annual yield",
    operators=["ENERGY1", "NM22", "NM24"],
    sync_config={
        "frequency_hz": 1.287,
        "zeqond_s": 0.777,
        "phase_coherence_target": 0.1,
    },
    input_schema={
        "latitude_deg": "number",
        "panel_area_m2": "number",
        "efficiency_pct": "number",
    },
    output_schema={
        "optimal_tilt_deg": "number",
        "annual_yield_kwh": "number",
        "master_sum": "number",
    },
    precision={"target_tolerance": 0.01},
)

result = skill.execute({
    "latitude_deg": 34.05,
    "panel_area_m2": 50.0,
    "efficiency_pct": 21.5,
})

print(f"Optimal tilt: {result.optimal_tilt_deg:.1f} deg")
print(f"Annual yield: {result.annual_yield_kwh:.0f} kWh")
print(f"Master sum: {result.master_sum:.6f}")
print(f"Phase coherence: {result.phase_coherence:.4f}")
print(f"Zeqond: {sync.get_zeqond():.3f}s")

JavaScript Example

import { SkillStudio, HulyaSync } from '@zeq-os/sdk';

const studio = new SkillStudio();
const sync = new HulyaSync();

const skill = studio.createSkill({
  name: 'portfolio-risk',
  description: 'Compute VaR and options pricing for a portfolio',
  operators: ['FIN1', 'FIN2', 'CS47', 'NM30'],
  syncConfig: {
    frequencyHz: 1.287,
    zeqondS: 0.777,
    phaseCoherenceTarget: 0.1,
  },
  inputSchema: {
    portfolioValue: 'number',
    confidenceLevel: 'number',
    holdingPeriodDays: 'number',
    volatility: 'number',
  },
  outputSchema: {
    varAmount: 'number',
    optionPrice: 'number',
    masterSum: 'number',
  },
  precision: { targetTolerance: 0.0001 },
});

const result = await skill.execute({
  portfolioValue: 1_000_000,
  confidenceLevel: 0.99,
  holdingPeriodDays: 10,
  volatility: 0.25,
});

console.log(`VaR (99%): $${result.varAmount.toFixed(2)}`);
console.log(`Master sum: ${result.masterSum.toFixed(6)}`);
console.log(`Zeqond: ${sync.getZeqond().toFixed(3)}s`);

Skill Execution

Once a skill is defined or loaded from a template, execute it through any Zeq OS interface.

SDK (Python)

from zeq_os import SkillStudio

studio = SkillStudio()
skill = studio.load("healthcare/pharmacokinetics-dosing")

result = skill.execute({
    "drug_name": "Metformin",
    "dose_mg": 500,
    "patient_weight_kg": 70,
    "renal_clearance_ml_min": 90,
})

print(result.to_dict())

SDK (JavaScript)

import { SkillStudio } from '@zeq-os/sdk';

const studio = new SkillStudio();
const skill = await studio.load('healthcare/pharmacokinetics-dosing');

const result = await skill.execute({
  drugName: 'Metformin',
  doseMg: 500,
  patientWeightKg: 70,
  renalClearanceMlMin: 90,
});

console.log(JSON.stringify(result, null, 2));

REST API (curl)

curl -X POST http://localhost:4000/v1/skills/execute \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $ZEQ_API_KEY" \
  -d '{
    "skill": "healthcare/pharmacokinetics-dosing",
    "inputs": {
      "drug_name": "Metformin",
      "dose_mg": 500,
      "patient_weight_kg": 70,
      "renal_clearance_ml_min": 90
    }
  }'

Response:

{
  "skill": "healthcare/pharmacokinetics-dosing",
  "version": "1.0.0",
  "result": {
    "half_life_h": 6.2,
    "peak_concentration_mg_l": 1.85,
    "trough_concentration_mg_l": 0.42,
    "recommended_interval_h": 12,
    "master_sum": 0.999847,
    "phase_coherence": 0.0023
  },
  "timing": {
    "zeqond": 0.777,
    "frequency_hz": 1.287,
    "execution_ms": 34
  },
  "precision": {
    "target_tolerance": 0.001,
    "actual_coherence": 0.0023,
    "status": "verified"
  }
}

CLI

# Execute a skill from the command line
zeq skill run healthcare/pharmacokinetics-dosing \
  --input drug_name=Metformin \
  --input dose_mg=500 \
  --input patient_weight_kg=70 \
  --input renal_clearance_ml_min=90 \
  --format json

# List available skill templates
zeq skill list

# Inspect a skill definition
zeq skill inspect healthcare/pharmacokinetics-dosing

# Validate a custom skill file before registering
zeq skill validate ./my-custom-skill.json

Batch Skill Execution

Process multiple skill invocations in parallel for high-throughput workloads.

Python Batch

from zeq_os import SkillStudio, BatchExecutor

studio = SkillStudio()
skill = studio.load("finance/portfolio-risk")

inputs = [
    {"portfolioValue": 1_000_000, "confidenceLevel": 0.95, "holdingPeriodDays": 1},
    {"portfolioValue": 1_000_000, "confidenceLevel": 0.99, "holdingPeriodDays": 1},
    {"portfolioValue": 1_000_000, "confidenceLevel": 0.99, "holdingPeriodDays": 10},
    {"portfolioValue": 5_000_000, "confidenceLevel": 0.99, "holdingPeriodDays": 10},
]

executor = BatchExecutor(max_workers=8)
results = executor.run(skill, inputs)

for i, result in enumerate(results):
    print(f"Run {i+1}: VaR=${result.var_amount:,.2f}  coherence={result.phase_coherence:.4f}")

JavaScript Batch

import { SkillStudio, BatchExecutor } from '@zeq-os/sdk';

const studio = new SkillStudio();
const skill = await studio.load('aerospace/mission-planner');

const inputs = [
  { originBody: 'Earth', targetBody: 'Mars', departureEpoch: '2026-09-15' },
  { originBody: 'Earth', targetBody: 'Mars', departureEpoch: '2026-10-01' },
  { originBody: 'Earth', targetBody: 'Mars', departureEpoch: '2026-10-15' },
];

const executor = new BatchExecutor({ maxConcurrency: 4 });
const results = await executor.run(skill, inputs);

results.forEach((r, i) => {
  console.log(`Window ${i + 1}: dV=${r.deltaV.toFixed(1)} m/s  ToF=${r.timeOfFlightDays} days`);
});

REST API Batch

curl -X POST http://localhost:4000/v1/skills/batch \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $ZEQ_API_KEY" \
  -d '{
    "skill": "energy/solar-optimizer",
    "inputs": [
      { "latitude_deg": 25.0, "panel_area_m2": 100, "efficiency_pct": 20.0 },
      { "latitude_deg": 35.0, "panel_area_m2": 100, "efficiency_pct": 20.0 },
      { "latitude_deg": 45.0, "panel_area_m2": 100, "efficiency_pct": 20.0 }
    ],
    "options": { "max_concurrency": 4, "timeout_ms": 30000 }
  }'

Batch Execution Notes

• Concurrency: Each worker maintains its own HulyaPulse phase lock. Default limit is 4; increase to 8 or 16 for CPU-bound workloads.
• Error handling: Failed invocations return an error object in the results array without stopping the batch.
• Ordering: Results are returned in the same order as inputs regardless of completion order.
• Rate limits: The API Gateway (port 4000) enforces 100 req/s per API key. For higher throughput, use the SDK batch executor which manages connection pooling internally.