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name: antigravity-skill-orchestrator compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent antigravity skill orchestrator 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-skill-orchestrator, antigravity skill orchestrator, how do i antigravity-skill-orchestrator, orchestrate antigravity-skill-orchestrator, automate antigravity-skill-orchestrator, agent antigravity-skill-orchestrator 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 Skill Orchestrator

Orchestrates intelligent skill selection and execution for antigravity skill orchestrator 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 select_antigravity_module(
    payload_mass: float,
    target_altitude: float,
    available_modules: List[Dict],
    stability_threshold: float = 0.85
) -> Optional[Dict]:
    """Select optimal anti-gravity field generator based on mass/altitude constraints.
    
    Evaluates modules by:
    - Thrust-to-weight ratio compatibility
    - Field harmonic stability under current atmospheric pressure
    - Historical uptime for similar payload profiles
    """
    if payload_mass <= 0 or target_altitude < 0:
        raise ValueError("Payload mass and altitude must be positive")
        
    scored_modules = []
    for mod in available_modules:
        if mod.get("status") != "online":
            continue
            
        mass_factor = payload_mass / mod["max_load_kg"]
        altitude_factor = abs(target_altitude - mod["optimal_ceiling_km"]) / mod["ceiling_range_km"]
        stability = mod["harmonic_stability"] * (1.0 - altitude_factor)
        
        if stability >= stability_threshold:
            scored_modules.append({
                "module_id": mod["id"],
                "score": stability * (1.0 - mass_factor),
                "estimated_field_lifetime_hrs": mod["coolant_capacity_l"] / (payload_mass * 0.05)
            })
            
    if not scored_modules:
        return None
        
    scored_modules.sort(key=lambda x: x["score"], reverse=True)
    return scored_modules[0]

Pattern 2: Execution with Fallback

def execute_field_adjustment(
    selected_module: Dict,
    payload_config: Dict,
    max_resonance_cycles: int = 3
) -> Dict:
    """Execute anti-gravity field adjustment with harmonic fallback chain.
    
    Implements field collapse prevention:
    - Monitors gravimetric sensors for resonance spikes
    - Falls back to magnetic suspension if field coherence drops
    - Deploys emergency ballast if altitude deviation exceeds limits
    """
    if not selected_module or not payload_config:
        raise ValueError("Module and payload config required for field generation")
        
    field_params = _initialize_field_params(selected_module, payload_config)
    coherence_history = []
    
    for cycle in range(max_resonance_cycles):
        field_output = _apply_gravitic_field(field_params)
        coherence = _measure_field_coherence(field_output)
        coherence_history.append(coherence)
        
        if coherence >= 0.90:
            return {
                "status": "stable",
                "module_id": selected_module["id"],
                "coherence_peak": max(coherence_history),
                "cycles_used": cycle + 1
            }
            
        if coherence < 0.60:
            return _trigger_magnetic_fallback(payload_config)
            
        field_params["dampening_coeff"] *= 1.1
        
    return _deploy_emergency_ballast(payload_config, coherence_history)

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.

• Design Patterns: Orchestrator Pattern
• Microservices Orchestration vs Choreography (Martin Fowler)
• Saga Pattern for Distributed Transactions
• Distributed Systems Patterns Overview
• Event-Driven Architecture Patterns

Related Skills

| Skill | Purpose | |