Antigravity Skill Orchestrator

antigravity-skill-orchestrator

Overview

The skill-orchestrator is a meta-skill designed to enhance the AI agent's ability to tackle complex problems. It acts as an intelligent coordinator that first evaluates the complexity of a user's request. Based on that evaluation, it determines if specialized skills are needed. If they are, it selects the right combination of skills, explicitly tracks these combinations using @agent-memory-mcp for future reference, and guides the agent through the execution process. Crucially, it includes strict guardrails to prevent the unnecessary use of specialized skills for simple tasks that can be solved with baseline capabilities.

When to Use This Skill

• Use when tackling a complex, multi-step problem that likely requires multiple domains of expertise.
• Use when you are unsure which specific skills are best suited for a given user request, and need to discover them from the broader ecosystem.
• Use when the user explicitly asks to "orchestrate", "combine skills", or "use the best tools for the job" on a significant task.
• Use when you want to look up previously successful combinations of skills for a specific type of problem.

Core Concepts

Task Evaluation Guardrails

Not every task requires a specialized skill. For straightforward issues (e.g., small CSS fixes, simple script writing, renaming a variable), DO NOT USE specialized skills. Over-engineering simple tasks wastes tokens and time.

Additionally, the orchestrator is strictly forbidden from creating new skills. Its sole purpose is to combine and use existing skills provided by the community or present in the current environment.

Before invoking any skills, evaluate the task:

1. Is the task simple/contained? Solve it directly using the agent's ordinary file editing, search, and terminal capabilities available in the current environment.
2. Is the task complex/multi-domain? Only then should you proceed to orchestrate skills.

Skill Selection & Combinations

When a task is deemed complex, identify the necessary domains (e.g., frontend, database, deployment). Search available skills in the current environment to find the most relevant ones. If the required skills are not found locally, consult the master skill catalog.

Master Skill Catalog

The Antigravity ecosystem maintains a master catalog of highly curated skills at https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/CATALOG.md. When local skills are insufficient, fetch this catalog to discover appropriate skills across the 9 primary categories:

• architecture
• business
• data-ai
• development
• general
• infrastructure
• security
• testing
• workflow

Memory Integration (@agent-memory-mcp)

To build institutional knowledge, the orchestrator relies on the agent-memory-mcp skill to record and retrieve successful skill combinations.

Step-by-Step Guide

1. Task Evaluation & Guardrail Check

[Triggered when facing a new user request that might need skills]

1. Read the user's request.
2. Ask yourself: "Can I solve this efficiently with just basic file editing and terminal commands?"
3. If YES: Proceed without invoking specialized skills. Stop the orchestration here.
4. If NO: Proceed to step 2.

2. Retrieve Past Knowledge

[Triggered if the task is complex]

1. Use the memory_search tool provided by agent-memory-mcp to search for similar past tasks.
   • Example query: memory_search({ query: "skill combination for react native and firebase", type: "skill_combination" })
2. If a working combination exists, read the details using memory_read.
3. If no relevant memory exists, proceed to Step 3.

3. Discover and Select Skills

[Triggered if no past knowledge covers this task]

1. Analyze the core requirements (e.g., "needs a React UI, a Node.js backend, and a PostgreSQL database").
2. Query the locally available skills using the current environment's skill list or equivalent discovery mechanism to find the best match for each requirement.
3. If local skills are insufficient, fetch the master catalog with the web or command-line retrieval tools available in the current environment: https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/CATALOG.md.
4. Scan the catalog's 9 main categories to identify the appropriate skills to bring into the current context.
5. Select the minimal set of skills needed. Do not over-select.

4. Apply Skills and Track the Combination

[Triggered after executing the task using the selected skills]

1. Assume the task was completed successfully using a new combination of skills (e.g., @react-patterns + @nodejs-backend-patterns + @postgresql).
2. Record this combination for future use using memory_write from agent-memory-mcp.
   • Ensure the type is skill_combination.
   • Provide a descriptive key and content detailing why these skills worked well together.

Examples

Example 1: Handling a Simple Task (The Guardrail in Action)

User Request: "Change the color of the submit button in index.css to blue." Action: The skill orchestrator evaluates the task. It determines this is a "simple/contained" task. It does not invoke specialized skills. It directly edits index.css.

Example 2: Recording a New Skill Combination

// Using the agent-memory-mcp tool after successfully building a complex feature
memory_write({ 
  key: "combination-ecommerce-checkout", 
  type: "skill_combination", 
  content: "For e-commerce checkouts, using @stripe-integration combined with @react-state-management and @postgresql effectively handles the full flow from UI state to payment processing to order recording.",
  tags: ["ecommerce", "checkout", "stripe", "react"]
})

Example 3: Retrieving a Combination

// At the start of a new e-commerce task
memory_search({ 
  query: "ecommerce checkout", 
  type: "skill_combination" 
})
// Returns the key "combination-ecommerce-checkout", which you then read:
memory_read({ key: "combination-ecommerce-checkout" })

Best Practices

• ✅ Do: Always evaluate task complexity before looking for skills.
• ✅ Do: Keep the number of orchestrated skills as small as possible.
• ✅ Do: Use highly descriptive keys when running memory_write so they are easy to search later.
• ❌ Don't: Use this skill for simple bug fixes or UI tweaks.
• ❌ Don't: Combine skills that have overlapping and conflicting instructions without a clear plan to resolve the conflict.
• ❌ Don't: Attempt to construct, generate, or create new skills. Only combine what is available.

Related Skills

• @agent-memory-mcp - Essential for this skill to function. Provides the persistent storage for skill combinations.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior design decisions (color palettes, typography, spacing scales) to maintain visual consistency across sessions. Cache generated design tokens.

# Check for prior frontend/design context before starting
python3 execution/memory_manager.py auto --query "design system decisions and component patterns for Antigravity Skill Orchestrator"

Storing Results

After completing work, store frontend/design decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Design system: adopted 8px grid, Inter font family, HSL color tokens with dark mode support" \
  --type decision --project <project> \
  --tags antigravity-skill-orchestrator frontend

Multi-Agent Collaboration

Share design decisions with backend agents (API contract changes) and QA agents (visual regression baselines).

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Implemented UI components — new design system with accessibility compliance (WCAG 2.1 AA)" \
  --project <project>

Design Memory Persistence

Store design system tokens and component decisions in Qdrant so any agent on any platform (Claude, Gemini, Cursor) can retrieve and apply consistent styling.