subagent-orchestrator
Has your agent coordinate multiple agents for large tasks involving multiple files. It manages resources to optimize work. Useful for extensive software projects.
What it does
This agent organizes smaller agents to handle large tasks involving many files. It breaks down the work, manages costs, and ensures everything works together without using too many resources.
When to use it
- ✓When a task involves more than 3 files.
- ✓When you want multiple agents working at the same time.
- ✓When you've had issues with usage limits (quota) before.
Example of what to ask your AI
“Break down this task of creating a new feature across multiple files, ensuring agents work in parallel and respect the quota.”
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 plans how to divide the large task into smaller parts, defining which agents will do what and on which files. Then, it runs these agents in parallel, checking if everything is working correctly before combining the parts.
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 subagent-orchestratorOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/subagent-orchestrator/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Subagent Orchestrator
A quota-aware, parallel subagent coordination skill for Antigravity 2.0. Turns one big task into a set of isolated, efficient agent missions — without burning your weekly quota.
Use this skill when
- A task spans 3+ files or components
- You want multiple agents working at the same time
- You've hit quota issues mid-task before
- The task involves both planning AND building
- You need browser agent + code agent + terminal agent running together
Do not use this skill when
- Editing a single file or fixing one bug
- Writing a quick script under 50 lines
- Asking a question or generating a plan only
Phase 1 — DECOMPOSE (before any agent runs)
Before spawning any subagent, the orchestrator MUST produce a Mission Brief. Announce:
"Running subagent-orchestrator skill. Decomposing task into isolated missions."
Then output a Mission Brief in this format:
MISSION BRIEF
─────────────────────────────────────────
Goal: [one sentence, what done looks like]
Total Agents: [N]
Quota Strategy: [FLASH / SONNET / MIXED]
Expected Token Cost: [LOW / MEDIUM / HIGH]
AGENTS:
[1] ID: agent-001
Role: [e.g. Planner / Builder / Tester / Browser]
Scope: [exact files or URLs this agent touches]
Model: [Gemini Flash / Claude Sonnet]
Input: [what it receives]
Output: [what it produces]
Depends on: [none / agent-001]
[2] ...
─────────────────────────────────────────
Wait for user to approve the Mission Brief before proceeding. If the user edits it, update and re-confirm. Never skip this step.
Phase 2 — QUOTA ROUTING
Before assigning models, apply this decision tree:
Is this task > 20 files OR > 500 lines of new code?
YES → Use Gemini Flash for all agents. Reserve Sonnet for final review only.
NO → Is this task creative UI / complex logic / API design?
YES → Use Sonnet for builder agent, Flash for all others.
NO → Use Gemini Flash for everything.
Model cost rules (never violate these):
- Claude Opus → NEVER use in subagents. Too expensive.
- Claude Sonnet → Max 1 subagent per mission.
- Gemini Flash → Default for all subagents. Fast, cheap, separate quota pool.
- Browser subagent → Always runs on its own pool. Use sparingly (1 per mission max).
Phase 3 — CONTEXT ISOLATION
Each subagent gets a scoped context packet. Never give all agents the full codebase.
For each agent, prepare:
AGENT CONTEXT PACKET — agent-[ID]
Files to read: [list only what this agent needs]
Files to write: [list only what this agent will create/edit]
Do NOT read: [explicitly exclude irrelevant files]
Knowledge: [paste only the relevant section of GEMINI.md]
Rule: If an agent doesn't need node_modules, package-lock.json, .next/, or dist/ — add them to a .antigravityignore before the agent runs.
Phase 4 — PARALLEL EXECUTION
Spawn agents in dependency order:
Round 1 (no dependencies): Run agents in parallel
Round 2 (depends on Round 1): Wait for all Round 1 outputs, then run
Round 3 (final): Integrate + verify
Between rounds, the orchestrator MUST:
- Collect each agent's output artifact
- Run a 3-point spot check:
- Did the agent stay within its assigned scope?
- Are there any import/export conflicts with other agents' outputs?
- Did any agent produce a placeholder ("TODO", "implement later")?
- If any check fails → re-run that agent with corrected context. Do NOT continue.
Phase 5 — ERROR RECOVERY
If a subagent fails or produces broken output:
RECOVERY PROTOCOL
─────────────────────────────────────────
1. Do NOT re-run the full mission.
2. Identify the exact failure point.
3. Spawn a single repair agent with:
- Only the broken file(s) as scope
- The error message as context
- Model: Gemini Flash (cheapest for repairs)
4. Validate the repair before continuing.
─────────────────────────────────────────
Never cascade a broken output to the next agent. Always fix before moving forward.
Phase 6 — INTEGRATION CHECK
After all agents complete, run a final integration sweep:
- All imports resolve correctly
- No duplicate function/variable names across files
- No hardcoded values that should be env variables
- No
console.logleft in production files - Types are consistent across components (TypeScript)
- Build would succeed (
npm run buildmentally verified)
…
FAQ
What is 'quota'?
It's a limit on how much you can use the AI service within a period (like a week). This agent helps you avoid exceeding that limit.
What are 'smaller agents'?
They are like simpler AI helpers, each focused on a specific part of the task, such as writing code, testing, or browsing the web.
Why use agents in parallel?
To speed up the work. Multiple agents working on different parts of the task simultaneously will finish faster than a single agent doing everything sequentially.
Details
Original description by the author
Coordinate quota-aware parallel subagents for large, multi-file Antigravity tasks.
Popularity
- GitHub stars
- 48k
- Ranking position
- #4265
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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