huggingface

(Also known as pwc-reply-issue.) Handle unread GitHub notifications for "Verify evals on Papers with Code" issues end-to-end. Apply author feedback to the catalog database, verify changes live, reply to authors, mark handled notifications read, and summarize…

Thoroughly audit the health of every Papers With Code production CRON job shown on the admin dashboard, including scheduler execution, logs, locks, durable data freshness, backlog completeness, semantic correctness, and bounded comparisons with upstream…

Resolve one trending Hugging Face model repository, or one release-radar signal, to a canonical paper or reject it as non-novel repackaging.

Safely curate one Papers With Code paper from a remote Hugging Face Job without direct database access.

Add or update benchmark datasets/leaderboards in the paperswithcode repo database. Use this whenever the user asks to add a benchmark, add a leaderboard, set default/featured benchmarks for a task, link benchmarks to tasks, add Accuracy or other metrics for a…

Add evaluation results to the paperswithcode database from any source — Hugging Face model cards, arXiv/HF/PapersWithCode paper text, or GitHub repository READMEs — restricted to benchmarks that already exist with a task association and the exact leaderboard…

Add or update AI/ML method taxonomy rows in the paperswithcode repo database. Use this whenever the user asks to add a method, technique, architecture, attention mechanism, training method, model-family method, or source/visual metadata for a method; says…

Process the user's X (Twitter) bookmarks into Papers with Code: find every bookmarked post that announces a paper, index missing arXiv papers, add non-arXiv releases as external papers, enrich each paper (code, project page, HF artifacts, tags, thumbnail),…

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or…

Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.

Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided…

Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`,…

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM",…

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for…

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding…

Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on…

Use when the user has written (or wants to write) a `ModularPipelineBlocks` subclass in a local Python file and needs to package it into a Hub-uploadable directory. Covers the workflow from a single `block.py` file to a published custom-block repo that…

Use when the user wants to run a diffusers pipeline from a terminal (one-off generation, batch jobs, smoke-testing a new model), run on HF Sandbox hardware via `--remote`, introspect a pipeline's input schema before calling it, or attach a LoRA at inference…

Use when adding a new model or pipeline to diffusers, setting up file structure for a new model, converting a pipeline to modular format, or converting weights for a new version of an already-supported model.

Use before opening a PR, or whenever asked to self-review a diffusers contribution. Applies the same rubric as the `@claude` CI (checks the diff against references/review-rules.md, traces call paths for dead code). Reports findings grouped by severity,…

Provides guidance for writing, optimizing, and benchmarking Triton kernels for Intel XPU GPUs (Battlemage/Arc Pro B50) using the Xe-Forge optimization framework. Includes an LLM-driven trial-loop workflow (analyze, validate, benchmark, profile, finalize),…

Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCH_LIBRARY_EXPAND…

Provides guidance for writing, optimizing, and benchmarking C++ CPU kernels with SIMD intrinsics (AVX2/AVX512) for the Hugging Face kernels ecosystem. Includes a two-phase workflow: Phase 1 correctness (generic → AVX2) and Phase 2 performance exploration…

Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle,…

Use ML Claw's HF Broker for protected Hugging Face repository and bucket reads, writes, temporary grants, recovery, and revocation. Use instead of asking for or exposing a Hugging Face access token.

Use when setting up, operating, migrating, repairing, or explaining an OpenClaw deployment on Hugging Face with the ML Claw `mlclaw` CLI. Covers the browser Space gateway, Hugging Face OAuth, local gateway mode, private Storage Buckets, Telegram as an…

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or…

Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on…

Scaffold a PhysicsIntern research workspace in the current folder. Run only when the user explicitly invokes it to set up a new physics/maths research workspace.

Scaffold a PhysicsIntern research workspace in the current folder.

Post-mortem analysis of a PhysicsIntern workspace run (Claude Code, Pi, Codex, or OpenCode host). Reconstructs the trajectory from the session record — JSONL file(s) for Claude/Pi/Codex, the SQLite store for OpenCode — audits methodology adherence against the…

Add a new TRF rule to the mlinter. Checks for duplicates, creates the rule module and TOML entry, runs against all models, and handles violations (fix or allowlist).

Retire a TRF rule from the mlinter. Marks the rule deprecated in rules.toml, deletes its module and tests, and records the removal in the CHANGELOG. Use when asked to remove, retire, drop, or delete a rule.

Cut a new transformers-mlinter release. Bumps the version across all pinned locations, updates the CHANGELOG, runs local checks and a packaged smoke test, drives the tag-based GitHub Actions publish to PyPI, then opens the next development version on main.…

Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.

Add or review a paper entry in TRL's paper index. Use when a PR implements a method, algorithm, or training approach from a research paper, or when reviewing such a PR.

Add and validate support for a Hugging Face model architecture in the Optimum Intel OpenVINO backend, including exporter configuration, patching, repository tests, and documentation.

Create a small random Hugging Face model that preserves the original architecture and can serve as a local Optimum Intel repository test fixture.

Prioritize and run Modal conference-deadline agents on high-ROI conferences only. Use when updating conference YAML via agents/modal_agent.py, choosing which conferences to refresh, running deadline agents, or avoiding expensive all-conference Modal runs.

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or…

Papers With Code MCP tools for searching and reading AI/ML papers, discovering recent and trending research, finding related work and paper lineage, browsing tasks, methods, conferences, organizations, and frameworks, and inspecting benchmark leaderboards…

Papers With Code CLI (`pwc`) for searching and reading AI/ML papers, discovering recent and trending research, finding related work and paper lineage, browsing tasks, methods, conferences, organizations, frameworks, and benchmark leaderboards, and submitting…

Investigates an Autophysicist workspace run. Use to understand what went wrong and could be improved in the single-agent iterative research process.

Investigates PhysicsIntern workspace run. Use to understand what went wrong and could be improved in the multi-agent research process.