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proposal-agent
Use when a contributor wants to create, repair, or complete an OpenRSI-Index AutoResearch task proposal grounded in a remote model-development repository, including research-question refinement, baseline traceability, evaluation design, workspace boundaries, compute estimates, and final proposal.md generation.
Choose how to use this skill
You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.
1. Native installer
This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.
Do not guess an installer command or replace an existing version without reviewing the diff.
2. Complete package recommended
Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.
No complete ProSkills package is published for this listing yet.3. Prompt-only
Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.
Need only the instruction file?
Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.
No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.
// RATINGS
Not yet listed on ClawHub or SkillsMP
// README
📣 Call for Contributors
We're actively looking for contributors to add new, challenging tasks. Our dedicated agent-native RSI-Anything pipeline helps you create a new task in an hour or less—bring the idea, and our agents handle the rest. See CONTRIBUTING.md for a step-by-step guide to creating and submitting tasks. Contributors with accepted tasks will be invited as co-authors. Have fun! 😀
Clone the repository:
git clone https://github.com/OpenRSI-Foundation/OpenRSI-Index.git
cd OpenRSI-Index
Open Codex or Claude Code (app or CLI), start a fresh session, and paste:
Use the proposal-agent skill at .agents/skills/proposal-agent/SKILL.md in my local OpenRSI-Index repository. Locate the repository if needed, then follow the skill to complete setup and guide me step by step through creating and submitting an RSI proposal.
Any questions? Contact us at [email protected], [email protected], or [email protected].
🔥 Call for Compute
If you have compute resources to run experiments and want to build frontier RSI environments together, contact [email protected], [email protected], or [email protected].
💥 Why OpenRSI Index?
We are witnessing the dawn of a new era: AI is entering a recursive self-improvement loop. The central question is whether this loop can move beyond the best-known human-designed method and reliably extend the scientific frontier. Answering it requires careful measurement, and building that measurement is the purpose of this project.
OpenRSI Index is an ongoing effort to evaluate whether AI agents can drive genuine recursive self-improvement and advance scientific discovery through real-world research at scales ranging from a single node to thousands of GPUs—not merely reproduce existing results, sweep parameters, or succeed on toy-scale tasks.
Task taxonomy
We are actively developing this project and welcome contributions from the community. Tasks and execution logs are available in rsi-tasks/ and OpenRSI-Index-Logs, respectively. Open the RSI Logs Viewer to explore results visually. See Quick Start for instructions on running these tasks.
| Task | Track | Category | Description | GPU requirement |
|---|---|---|---|---|
Qwen-122B-RL · Research report | Signature Task | Post-training | Optimize a full post-training stack under one fixed budget. | 256× H100 |
marin-optimizer-update-geometry | Signature Task | Pre-training | Design a scale-general optimizer for the Marin scaling ladder. | 256× H100 |
gpic-text-to-image · Research trajectories | Signature Task | Vision | Improve a shared PixelGen checkpoint on a fixed GPIC 10M subset. | 4× H100 per job |
depth-width-allocation | Public Task | Pre-training | Optimize decoder-width allocation under a fixed 200M training budget. | 8× H100 |
learnability-cot | Public Task | Post-training | Adapt reasoning traces for small-model math SFT. | 4× H100 |
gemm-h100-refined | Public Task | MLSys | Optimize an FP16 CUDA GEMM kernel for H100 throughput. | 1× H100 |
liger-tied-ce | Public Task | MLSys | Optimize tied-weight fused cross-entropy for Qwen3 SFT. | 2× H100 |
minference-sparse-prefill | Public Task | MLSys | Optimize Triton sparse-prefill attention on H100. | 1× H100 |
molmo2-pointing-refined | Public Task | Vision | Optimize Molmo2 video-pointing at inference time. | 1× H100 |
datacomp-small-filtering | Public Task | Vision | Curate DataComp-small data for fixed ViT-B/32 training. | 32× H100 |
ace-playbook-repair | Public Task | Agents | Inspect and repair ACE playbooks for a frozen Qwen model. | 1× H100 |
molmoweb-interaction-context | Public Task | Vision | Allocate inference-time context for frozen MolmoWeb replay. | 2× H100 minimum |
reasonir-difficulty-curriculum | Public Task | Post-training | Optimize a fixed-budget retrieval LoRA through difficulty-aware curricula. | 4× H100 minimum |
isaaclab-peginsert-reward-search | Public Task | Robotics | Design bounded reward graphs for fixed-budget Isaac Lab peg insertion. | 4× H100 |
kev-decision-architecture | Public Task | Post-training | Redesign a 0.5B decision architecture under a fixed training corpus and recipe. | 2× H100 minimum |
sglang-omni-tts-runtime | Public Task | MLSys | Improve CosyVoice3 TTS serving throughput with Qwen Base and MOSS regression checks. | 1 Work + 3 Judge |
// HOW IT'S BUILT
KEY FILES