NousResearch
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- ▌ Hermes Agent Skill Authoring 2 · nousresearchAuthor in-repo SKILL.md files: frontmatter and structure.
- ▌ Cloudflare Temporary Deploy 2 · nousresearch bundleDeploy a Worker live, no account, via wrangler --temporary.
- ▌ Pokemon Player 2 · nousresearchPlay Pokémon games via headless emulation. Start a game server, read game state, make strategic decisions, and send actions — all from the terminal.
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- ▌ Publish 2 · nousresearchPublish a Harbor task or dataset to the registry. Use when the user wants to upload, publish, or share tasks or datasets/benchmarks on the Harbor registry.
- ▌ Create Adapter 2 · nousresearchScaffold a new Harbor benchmark adapter by running `harbor adapter init` and then guide implementation using the Adapters Agent Guide as the authoritative spec.
- ▌ Publish · nousresearchPublish a Harbor task or dataset to the registry. Use when the user wants to upload, publish, or share tasks or datasets/benchmarks on the Harbor registry.
- ▌ Create Adapter · nousresearchScaffold a new Harbor benchmark adapter by running `harbor adapter init` and then guide implementation using the Adapters Agent Guide as the authoritative spec.
- ▌ Create Task · nousresearchCreate a new Harbor task for evaluating agents. Use when the user wants to scaffold, build, or design a new task, benchmark problem, or eval. Guides through instruction writing, environment setup, verifier design (pytest vs Reward Kit vs custom), and solution scripting.
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- ▌ Gh Stack · nousresearchManage stacked branches and pull requests with the gh-stack GitHub CLI extension. Use when the user wants to create, push, rebase, sync, navigate, or view stacks of dependent PRs. Triggers on tasks involving stacked diffs, dependent pull requests, branch chains, or incremental code review workflows.
- ▌ Add Benchmark · nousresearch bundleGuide for adding a new benchmark or training environment to NeMo-Gym. Use when the user asks to add, create, or integrate a benchmark, evaluation, training environment, or resources server into NeMo-Gym. Also use when wrapping an existing 3rd-party benchmark library. Covers the full workflow: data preparation, resources server implementation, agent wiring, YAML config, testing, and reward profiling (baselining). Triggered by: "add benchmark", "new resources server", "integrate benchmark", "wrap benchmark", "add training environment", "add eval".
- ▌ Nemo Gym Docs · nousresearchMaintain the NeMo Gym Fern docs site — add, update, move, or remove pages under fern/. Use for any documentation change. Triggered by: "edit docs", "add doc page", "update docs", "rename page", "fix broken link", "add redirect", "preview docs", "publish docs", any request that touches `fern/`.
- ▌ Nemo Gym Debugging · nousresearch bundleUse when debugging a Nemo Gym run or reward profiling job. Covers rollout collection failures, empty or partial JSONL outputs, stale materialized inputs, verifier/schema errors, Ray or Slurm issues, vLLM readiness, judge failures, tool/sandbox failures, cache problems, and throughput bottlenecks.
- ▌ Nemo Gym Blade Analysis · nousresearch bundleUse when analyzing NeMo Gym benchmark rollouts for BLADE-style reports, writing benchmark methodology notes, checking whether a benchmark is BLADE-ready, comparing model runs, or explaining why a benchmark report passed, failed, or changed. Covers aggregate metrics, rollout evidence, report structure, root-cause taxonomy, judge expectations, and improvement recommendations. For generic reward profiling commands, prefer nemo-gym-reward-profiling; for failed infrastructure jobs, prefer nemo-gym-debugging.
- ▌ Nemo Gym Pivot Datasets · nousresearch bundleUse when creating, validating, or documenting Nemo Gym pivot datasets from rollout, trajectory, chat-completion, Responses API, or tool-call artifacts. Covers Gym Responses-style row conversion, pivot selection, single-step tool-use configs, agent_ref alignment, verifier knobs, expected-action row contracts, and train/eval usage.
- ▌ Nemo Gym Reward Profiling · nousresearch bundleUse to help users get started with Nemo Gym reward profiling. Covers the basic gym env start, gym eval run, and gym eval profile workflow, repeated rollouts, materialized inputs, rollout JSONL artifacts, task and rollout identity, output inspection, partial profiling, and rollout_infos. For failed jobs, prefer nemo-gym-debugging.
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- ▌ Subagent Driven Development 3 · nousresearch bundleExecute plans via delegate_task subagents (2-stage review).
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- ▌ Distributed LLM Pretraining Torchtitan · nousresearch bundlePretrain LLMs at scale with PyTorch 4D parallelism.
- ▌ Stable Diffusion Image Generation · nousresearch bundleText-to-image generation, inpainting, and img2img.
- ▌ Hermes Agent Skill Authoring · nousresearchAuthor in-repo SKILL.md files: frontmatter and structure.
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- ▌ Fitness Nutrition · nousresearch bundle> Gym workout planner and nutrition tracker. Search 690+ exercises by muscle, equipment, or category via wger. Look up macros and calories for 380,000+ foods via USDA FoodData Central. Compute BMI, TDEE, one-rep max, macro splits, and body fat — pure Python, no pip installs. Built for anyone chasing gains, cutting weight, or just trying to eat better.
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- ▌ Computer Use · nousresearch| Drive the user's desktop in the background — clicking, typing, scrolling, dragging — without stealing the cursor, keyboard focus, or switching virtual desktops / Spaces. Cross-platform: macOS, Windows, Linux. Works with any tool-capable model. Load this skill whenever the `computer_use` tool is available.
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- ▌ Google Meet · nousresearch bundleJoin a Google Meet call, transcribe live captions, optionally speak in realtime, and do the followup work afterwards. Use when the user asks the agent to sit in on a meeting, take notes, summarize, respond in-call, or action items from it.
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