# Antigravity Workflows > Implements intelligent antigravity workflows with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/antigravity-workflows` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/antigravity-workflows` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/antigravity-workflows/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/antigravity-workflows --- # 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 ```python 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 ```python 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 --- --- ## 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 | |