antigravity-skill-orchestrator
Helps the agent choose the best tools for each task. It learns which tools work well together and avoids using too many tools.
What it does
This is a 'task manager' for the AI. It decides if a request is simple or complex. If complex, it picks the best tools (other 'skills') to solve the problem and learns which tool combinations work best for the future.
When to use it
- ✓When you have a big problem that needs multiple steps or different expertise.
- ✓When you don't know which specific tools the AI should use.
- ✓When you ask the AI to 'combine tools' or 'use the best options' for an important task.
Example of what to ask your AI
“I need to build a complete website with a database and a payment system. What tools would you use and how would you combine them?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It first checks if the request is simple. If it is, it solves it directly. If it's complex, it looks for the right tools, uses them, and notes down which tools worked well together to use next time.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill antigravity-skill-orchestratorOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/antigravity-skill-orchestrator/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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:
- Is the task simple/contained? Solve it directly using the agent's ordinary file editing, search, and terminal capabilities available in the current environment.
- 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/agentic-awesome-skills/main/CATALOG.md. When local skills are insufficient, fetch this catalog to discover appropriate skills across the 9 primary categories:
architecturebusinessdata-aidevelopmentgeneralinfrastructuresecuritytestingworkflow
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]
- Read the user's request.
- Ask yourself: "Can I solve this efficiently with just basic file editing and terminal commands?"
- If YES: Proceed without invoking specialized skills. Stop the orchestration here.
- If NO: Proceed to step 2.
2. Retrieve Past Knowledge
[Triggered if the task is complex]
- Use the
memory_searchtool provided byagent-memory-mcpto search for similar past tasks.- Example query:
memory_search({ query: "skill combination for react native and firebase", type: "skill_combination" })
- Example query:
- If a working combination exists, read the details using
memory_read. - If no relevant memory exists, proceed to Step 3.
3. Discover and Select Skills
[Triggered if no past knowledge covers this task]
- Analyze the core requirements (e.g., "needs a React UI, a Node.js backend, and a PostgreSQL database").
- Query the locally available skills using the current environment's skill list or equivalent discovery mechanism to find the best match for each requirement.
- 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/agentic-awesome-skills/main/CATALOG.md. - Scan the catalog's 9 main categories to identify the appropriate skills to bring into the current context.
- Select the minimal set of skills needed. Do not over-select.
…
FAQ
Can the AI create new tools?
No, it only uses and combines tools that already exist.
Why not use tools for simple tasks?
Using complex tools for easy things wastes the AI's time and resources unnecessarily.
How does the AI 'learn' the best combinations?
It uses special memory to save which tools worked together on past tasks, so it can reuse that information.
Details
Original description by the author
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2146
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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