---

name: antigravity-workflows compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent antigravity workflows with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: antigravity-workflows, antigravity workflows, how do i antigravity-workflows, orchestrate antigravity-workflows, automate antigravity-workflows, agent antigravity-workflows archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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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

1. 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.

2. 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.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. 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:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

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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

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