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 jaccen/Awesome-Gaussian-Skills --skill super-agentgit clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-SkillsWrote 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/jaccen/awesome-gaussian-skills/super-agent)<a href="https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/super-agent"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/super-agent/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/jaccen/awesome-gaussian-skills/super-agent"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/super-agent.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 | $0.00122 | $0.01367 |
| Opus 5.5 | $0.00049 | $0.00547 |
| Sonnet 5.5 | $0.00024 | $0.00273 |
| Haiku 4.5 | $0.00012 | $0.00137 |
Grade A, and why
super-agent 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 2d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Super Agent & Multi-Agent System Design
Scope: LLM-agent architecture, multi-agent orchestration, and the 3D/spatial agent cross-section. Knowledge base: 20 arXiv-verified methods in the Super Agents & Agentic AI category (
data/methods.json, single source of truth — always re-query, never answer from memory).
1. Architecture Pattern Selection
| Pattern | When to use | Library exemplar |
|---|---|---|
| On-device super agent | Edge deployment, privacy, low latency function calling | Octopus v2 [arXiv:2404.01744] — 2B params beating GPT-4 on function-calling accuracy/latency |
| Agentic pipeline with physical feedback | Asset generation needing physics validity | Artisan [arXiv:2610.13947] — multi-agent + simulation feedback for articulated 3D assets |
| Decentralized swarm | Emergent coordination without assigned roles | SwarmWorld [arXiv:2608.26081] — stigmergic self-organization of LLM agents |
| Safe execution harness | Enterprise policy compliance, partial observability | E-Ledger [arXiv:2610.11552] — code approval layer + world ledger of verified rules |
| Paper-to-code research agent | Reimplementation pipelines | SPLATIFY [arXiv:2610.09116] — 3DGS papers → trainable gsplat code via grammar-constrained multi-agent synthesis |
2. 3D / Spatial Agent Integration
| Task | Method | Key mechanism |
|---|---|---|
| Multi-agent 3D reconstruction | COLMAR [arXiv:2607.13524] | Cooperative view-policy learning, overlap-aware coverage |
| Multi-agent GS SLAM | GRAND-SLAM [arXiv:2506.18885] | Local-optimization tracking, global consistency outdoors |
| Agentic 3D object reconstruction | GATOR [arXiv:2610.11215] | Generative + agentic recovery from casual images, occlusion inference |
| Autonomous CAD reconstruction | CADFather [arXiv:2610.09127] | VLM assistant coordinates complementary tools, decides finish |
| Interactive 3D scene editing | DesignAgent3D [arXiv:2608.21438] | Designer-like multimodal reasoning, intent disambiguation |
| Embodied-simulation environment building | GASE [arXiv:2606.17520] | Automated GS reconstruction for sim scenes, minimal sim-to-real gap |
| VLN generation-evaluation-policy | GN0 [arXiv:2606.03682] | Unified paradigm on a 3DGS simulation engine (GN-Matrix / GN-Bench) |
| Robotic photography | PhotoAgent [arXiv:2603.22796] | LMM CoT → geometric constraints, reflection in internal world model |
| Urban planning agent | CityPlanner [arXiv:2609.09578] | Sandbox-agent: inspect files, generate plans, run evaluators, revise |
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.
- 2d ago First seen · 63 lines · 122 tokens per session scan A c214cfdb822e
super-agent is a skill published in the GitHub repository jaccen/Awesome-Gaussian-Skills (162 stars, last pushed today), licensed Apache-2.0. It adds 122 tokens to every session and 1,367 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-10-14.
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