Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Demerzels-lab/elsamultiskillagent --skill antigravity-swarmgit clone --depth 1 https://github.com/Demerzels-lab/elsamultiskillagentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/demerzels-lab/elsamultiskillagent/antigravity-swarm)<a href="https://agentmods.dev/skills/demerzels-lab/elsamultiskillagent/antigravity-swarm"><img src="https://agentmods.dev/badge/skills/demerzels-lab/elsamultiskillagent/antigravity-swarm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/demerzels-lab/elsamultiskillagent/antigravity-swarm"><img src="https://agentmods.dev/badge/skills/demerzels-lab/elsamultiskillagent/antigravity-swarm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.01106 |
| Opus 5 | $0.00018 | $0.00553 |
| Sonnet 5 | $0.00007 | $0.00221 |
| Haiku 4.5 | $0.00004 | $0.00111 |
Grade A, and why
antigravity-swarm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Antigravity Subagents Skill
This skill allows you to dispatch autonomous sub-agents to perform tasks.
It features a Manager layer (planner.py) that can automatically design a team of agents for a complex mission, and an Orchestrator (orchestrator.py) to run them visually.
Both scripts include a Plan Mode (confirmation step) by default to prevent accidental usage limits consumption.
[!WARNING] Do NOT modify files in this directory while the Orchestrator is running. The system actively reads and writes to
task_plan.md,findings.md, andsubagents.yaml. Manual edits during execution may cause race conditions or inconsistent agent behavior.
🛠 Tools
dispatch_subagent
Runs a sub-agent with a specific task.
Usage: Use this when you have a parallelizable task or need to offload a specific job (e.g., "Write a test file", "Analyze this directory").
Arguments:
task: A clear, self-contained description of what the sub-agent should do.
Implementation Details:
The sub-agent is powered by the gemini CLI. A Python wrapper intercepts specific output patterns to perform file system operations and command executions.
Syntax used by Sub-Agent (Handled Automatically):
<<WRITE_FILE path="...">>...<<END_WRITE>><<RUN_COMMAND>>...<<END_COMMAND>>
run_mission (Dynamic Orchestration)
Analyzes a high-level goal, hires a custom team of sub-agents, and creates a configuration for them.
Usage: Use this for complex, multi-step projects where you don't want to manually define every sub-agent.
Arguments:
mission: A description of the overall project (e.g., "Create a Snake game in Python").
How it works:
- Calls
scripts/planner.pyto generatesubagents.yaml. (Will prompt for confirmation unless--yesis used). - (Optional) You can then run
scripts/orchestrator.pyto execute the team. (Will prompt for confirmation unless--yesis used).
Usage Modes
Mode 1: CLI User (Terminal Visualization)
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- _meta.json 301 B
- GEMINI_example.md 455 B
- gemini-extension.json 210 B
- package.json 360 B
- README_KO.md 9.2 KB
- README.md 7.7 KB
- requirements.txt 25 B
- scripts/compactor.py 2.9 KB runs code
- scripts/dispatch_agent.py 7.0 KB runs code
- scripts/orchestrator.py 10 KB runs code
- scripts/planner.py 10 KB runs code
- scripts/ultrawork_loop.py 2.3 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 117 lines · 37 tokens per session scan A da1d5c3608ca
antigravity-swarm is a skill published in the GitHub repository Demerzels-lab/elsamultiskillagent (10 stars, last pushed 4mo ago), licensed MIT. It adds 37 tokens to every session and 1,106 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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