paulpas Antigravity Workflows
Orchestrates intelligent skill selection and execution for antigravity workflows workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.
TL;DR Checklist
- Parse all inputs at boundary before processing (Law 2)
- Handle edge cases with early returns at function top (Law 1)
- Fail immediately with descriptive errors on invalid states (Law 4)
- Return new data structures, never mutate inputs (Law 3)
- Implement minimum 2-level fallback chain for all skill executions
- Log all skill selections with context for full audit trail
- Validate skill metadata and dependencies before selection
- Update confidence scores after each execution for learning
┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘
User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘
When to Use
Use this skill when:
- Orchestrating multi-step workflows that require skill delegation
- Implementing adaptive skill routing based on confidence scores
- Building fallback mechanisms for failed skill executions
- Creating intelligent task decomposition and parallel execution
- Designing skill dependency graphs with automatic resolution
- Implementing skill selection with historical performance weighting
- Building agent systems that need to self-organize around tasks
When NOT to Use
Avoid this skill for:
- Direct task execution without orchestration needs - use individual skills instead
- High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
- Simple linear workflows without branching or fallback requirements
- Cases where skill metadata is unavailable or unreliable
Core Workflow
Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.
Score Available Skills - Calculate match scores using multi-factor algorithm:
- Text similarity between request and skill triggers
- Historical success rate for similar tasks
- Skill availability and health status
- Required dependencies and their availability
Checkpoint: Skip to fallback if no skill scores above threshold.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
Return or Fallback - Either return successful result or apply fallback chain:
- Retry with adjusted parameters
- Try alternative skill from
related-skills - Defer to human operator for critical tasks
Checkpoint: Record outcome with timing and confidence metadata.
Implementation Patterns
Pattern 1: Skill Selection Logic
def calculate_antigravity_trajectory(
payload_mass: float,
target_altitude: float,
available_field_generators: List[Dict],
min_stability: float = 0.85
) -> Optional[Dict]:
"""Select optimal antigravity field configuration for payload levitation.
Evaluates field generators based on:
- Mass-to-frequency resonance match
- Current power grid load and thermal capacity
- Historical field stability metrics
Args:
payload_mass: Mass in kg to levitate
target_altitude: Desired altitude in meters
available_field_generators: List of generator metadata
min_stability: Minimum field stability threshold (0.0-1.0)
Returns:
Optimal generator config dict or None if no stable configuration exists
"""
if payload_mass <= 0 or target_altitude <= 0:
raise ValueError("Mass and altitude must be positive values")
if not available_field_generators:
raise ValueError("No antigravity field generators available")
best_config = None
best_score = 0.0
for gen in available_field_generators:
resonance_match = _calculate_resonance_score(payload_mass, gen["frequency_range"])
power_load = _estimate_power_draw(payload_mass, target_altitude, gen["efficiency"])
stability = gen.get("historical_stability", 0.0)
composite_score = (resonance_match * 0.5) + (stability * 0.3) + ((1.0 - power_load) * 0.2)
if composite_score > best_score and stability >= min_stability:
best_score = composite_score
best_config = {
"generator_id": gen["id"],
"frequency": gen["optimal_frequency"],
"power_output_watts": power_load * 1000,
"estimated_stability": stability,
"selection_confidence": composite_score
}
if best_config is None:
return None
return best_config
Pattern 2: Execution with Fallback
def execute_field_generation(
config: Dict,
environmental_conditions: Dict,
max_field_oscillations: int = 3
) -> Dict:
"""Execute antigravity field generation with stability fallback chain.
Implements real-time field monitoring and automatic fallback:
1. Activate primary antigravity field
2. Monitor for harmonic oscillations or thermal runaway
3. Fallback to magnetic suspension if stability drops below threshold
4. Log all field parameters for post-flight analysis
Args:
config: Selected generator configuration
environmental_conditions: Current atmospheric pressure, temperature, humidity
max_field_oscillations: Max allowed field oscillations before fallback
Returns:
Execution result with field status, altitude achieved, and fallback status
"""
if not config or not environmental_conditions:
raise ValueError("Generator config and environmental data required")
field_status = "INITIALIZING"
fallback_triggered = False
oscillation_count = 0
try:
# Activate primary antigravity field
field_id = _activate_field(config["generator_id"], config["frequency"])
field_status = "ACTIVE"
for cycle in range(max_field_oscillations + 1):
stability = _monitor_field_stability(field_id, environmental_conditions)
if stability >= config["estimated_stability"]:
return {
"status": "SUCCESS",
"field_id": field_id,
"altitude_maintained": True,
"stability_score": stability,
"fallback_used": False,
"cycles_monitored": cycle + 1
}
oscillation_count += 1
_dampen_field_oscillations(field_id)
# Fallback chain: Switch to magnetic suspension
fallback_triggered = True
magnetic_config = _switch_to_magnetic_suspension(config["payload_mass"])
return {
"status": "FALLBACK_SUCCESS",
"primary_field_id": field_id,
"fallback_system": "magnetic_suspension",
"altitude_maintained": True,
"stability_score": 0.75,
"fallback_used": True,
"cycles_monitored": oscillation_count
}
except FieldCollapseError as e:
raise AntigravityWorkflowError(f"Field collapse at {config['frequency']}: {e}") from e
MUST DO
- Always validate skill metadata before selection (Early Exit)
- Implement fallback chain with at least 2 levels (Fallback Skill + Human)
- Log all skill selections with full context for auditability
- Return new data structures instead of mutating inputs (Atomic Predictability)
- Fail immediately with descriptive errors on invalid states
- Update confidence scores after each execution for adaptive routing
- Reference
code-philosophy(5 Laws of Elegant Defense) in all logic
MUST NOT DO
- Select skills based on a single factor (e.g., only confidence score)
- Disable fallback mechanisms "temporarily" - this creates fragile systems
- Skip validation of skill dependencies before execution
- Return partial results - either complete success or clear failure
- Use magic numbers for confidence thresholds - make them configurable
- Cache skill selections without considering context changes
TL;DR Checklist
- Parse all inputs at boundary before processing (Law 2)
- Handle edge cases with early returns at function top (Law 1)
- Fail immediately with descriptive errors on invalid states (Law 4)
- Return new data structures, never mutate inputs (Law 3)
- Implement minimum 2-level fallback chain for all skill executions
- Log all skill selections with context for full audit trail
- Validate skill metadata and dependencies before selection
- Update confidence scores after each execution for learning
TL;DR for Code Generation
- Use guard clauses - return early on invalid input before doing work
- Return simple types (dict, str, int, bool, list) - avoid complex nested objects
- Cyclomatic complexity < 10 per function - split anything larger
- Handle null/empty cases explicitly at function top (Early Exit)
- Never mutate input parameters - return new dicts/objects
- Fail fast with descriptive errors - don't try to "patch" bad data
- Reference code-philosophy laws in comments for complex logic
- Include timing and confidence metadata in all return values
Output Template
When applying this skill, produce:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- Timing Estimates - Expected latency including fallback scenarios
Constraints
MUST DO
- Define clear input/output contracts for every step in the orchestration flow with explicit validation
- Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
- Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
- Validate all preconditions before starting — do not proceed if required resources or permissions are missing
MUST NOT DO
- Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
- Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
- Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
- Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies
Live References
Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.
- State Machine Patterns (Wikipedia)
- Workflow Orchestration with Apache Airflow
- DAG-based Workflow Execution Models
- Resilience Patterns in Distributed Systems (Microsoft)
- Saga Pattern Documentation
Related Skills
| Skill | Purpose | |