Claude skills and agent skills

Hugging FaceSkill

optimum-model-enabler

huggingface/optimum-intel/.github/skills/optimum-model-enabler/SKILL.md

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

Hugging FaceSkill

tiny-model-creator

huggingface/optimum-intel/.github/skills/tiny-model-creator/SKILL.md

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

Hugging FacePipes a download into a shellSkill

hf-cli

huggingface/ai-deadlines/.agents/skills/hf-cli/SKILL.md

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 scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.

Often used withhuggingface-local-models

Hugging FaceReads credentialsSkill

update-conferences

huggingface/ai-deadlines/.agents/skills/update-conferences/SKILL.md

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 FaceReads credentialsSkill

add-benchmark

huggingface/paperswithcode/.agents/skills/add-benchmark/SKILL.md

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 benchmark, or fix benchmark source links. This is repo-specific and should trigger even if the user only names benchmark titles and a task page.

Hugging FaceReads credentialsSkill

add-conference

huggingface/paperswithcode/.agents/skills/add-conference/SKILL.md

Add or update conference rows in the paperswithcode repo database. Use this whenever the user asks to add a conference, create a conference page, import accepted papers for a venue (CVPR, ICLR, NeurIPS, ICML, ICRA, etc.), set conference dates/venue/description/hero image, or link papers to a conference edition. Trigger even for casual phrasing like "add CVPR 2027", "create the ICLR 2026 page", "import conference papers from this CSV", or "set the conference thumbnail".

Hugging FaceReads credentialsSkill

add-evals

huggingface/paperswithcode/.agents/skills/add-evals/SKILL.md

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 metric already configured. Use this whenever the user shares a Hugging Face model URL or repo id, an arXiv id / HF paper / PapersWithCode paper, a GitHub repository URL, or a model release page and asks to add evaluation results, benchmark scores, or leaderboard entries — even when phrased casually like "add scores from this model", "import benchmarks from <hf url>", "ingest the eval table from this paper into the DB", "grab the benchmarks from this repo's README", or "add evaluations for this release". By default import only the model(s) the source paper introduces — never post comparison-table baselines onto other papers as external evals unless the user explicitly asks. If the source is not backed by an arXiv paper, first use the add-external-paper skill to create or update the `source = 'external'` paper row, then attach evaluation rows to that paper. This skill must skip source-only metrics, component scores, subscores, and alternate metric labels unless that metric is already configured for the exact leaderboard; use it instead of generic ingestion guidance whenever the user is trying to populate evaluation results from a model, paper, or repository source.

Hugging FaceReads credentialsSkill

add-external-paper

huggingface/paperswithcode/.agents/skills/add-external-paper/SKILL.md

Add non-ArXiv external papers/resources to the paperswithcode repo database. Use this whenever the user asks to add, index, submit, or populate a paper/resource/release that does not have an arXiv paper, including Hugging Face model cards, GitHub repositories, blog posts, vendor technical reports, external PDFs, project pages, or release announcements. This skill should trigger even when the user casually says "add this repo/model/blog to paperswithcode" or gives a URL such as GitHub, Hugging Face, a company blog, a PDF, or a technical report and wants it to appear as a paper page with tasks, methods, code, project links, HF artifacts, and a thumbnail.

Hugging FaceReads credentialsSkill

add-method

huggingface/paperswithcode/.agents/skills/add-method/SKILL.md

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 phrases like "make GPT-4 a method", "add X as a method", "similarly add this method", "link papers that cite the source paper", or "update the method source to point to the paper page"; or gives a method name plus a paper, arXiv id, PapersWithCode URL, Hugging Face report, GitHub explainer, blog post, or image for the method thumbnail.

Hugging FaceReads credentialsSkill

add-task

huggingface/paperswithcode/.agents/skills/add-task/SKILL.md

Add or update AI/ML task taxonomy rows in the paperswithcode repo database. Use this whenever the user asks to add a task, create a new benchmark category, add a subtask under an existing task, set task description/keywords/area/image, or expand task coverage; says phrases like "add X as a task", "create a task page for", "similarly add this task", or "we need a task for"; or names a research area (ASR, object detection, agents, tabular learning) that should become a browsable /tasks/ page with keywords and a hero image. After adding the task, recommend standard benchmarks to link next (use add-benchmark when the user wants them ingested).

Hugging FaceReads credentialsSkill

complete-task-based-on-hf

huggingface/paperswithcode/.agents/skills/complete-task-based-on-hf/SKILL.md

Complete or audit a Papers With Code task using Hugging Face model popularity as the discovery signal. Use this whenever a user asks to scan the most downloaded or trending Hugging Face models for a pipeline/task, resolve their canonical technical reports, and add missing paper-native evaluations to existing Papers With Code benchmarks. Trigger for requests like "complete this task from HF", "check popular Hub models for missing evals", or a Hugging Face models URL with `pipeline_tag` and `sort`; use `add-evals` instead for one already-known model or paper, and `complete-task-v2` for a holistic field survey beyond Hugging Face.

Hugging FaceReads credentialsSkill

complete-task

huggingface/paperswithcode/.agents/skills/complete-task/SKILL.md

Complete the benchmark, paper, and evaluation data for a single Papers With Code task page end-to-end, using the legacy PwC dump as the source of truth for what the major benchmarks and papers are. Use this whenever the user wants to "complete", "fill out", "build out", "populate", "flesh out", "round out", or "finish" a task page; audit a task for missing benchmarks/evals; or says things like "make the motion-generation task complete", "add the top benchmarks and papers for object tracking", "populate evals for this task", or "bring the <task> leaderboards up to date". It mines the legacy evaluation-tables.json for the top benchmarks (by leaderboard entries) and their top papers, then orchestrates the add-benchmark, enrich-paper, add-evals, and verify-evals skills (one focused subagent each) to index papers, create missing benchmarks, import evaluations, and verify every leaderboard is apples-to-apples. Prefer this orchestration skill over running those skills ad hoc when the goal is whole-task completion rather than a single benchmark/paper/eval. When data already exists, it degrades gracefully into verifying what is there. Be memory-aware: the legacy dump is ~250 MB and this runs on a small VPS, so always stream it with the bundled miner rather than loading it.

Hugging FaceReads credentialsSkill

complete-task-v2

huggingface/paperswithcode/.agents/skills/complete-task-v2/SKILL.md

Holistically complete a Papers With Code task page. Map the field from the live task page, local DB, legacy PwC dump, Semantic Scholar, trending papers, Hugging Face artifacts, popular GitHub repos, and major-paper comparison tables; reconcile the top 100 live papers; add missing major benchmarks and paper-native evals; verify each changed leaderboard for apples-to-apples comparability; and enrich evaluated and benchmark-source papers with official links, task/method tags, and healthy thumbnails. Use whenever asked to complete, populate, audit, round out, or thoroughly review a whole task's papers, benchmarks, evaluations, metadata, or visual coverage rather than perform a narrow single-paper or single-benchmark import.

Hugging FaceReads credentialsSkill

create-thumbnail

huggingface/paperswithcode/.agents/skills/create-thumbnail/SKILL.md

Create, replace, audit, and publish Papers With Code paper thumbnails for one paper or every paper on a benchmark page. Use this whenever a user asks to add a thumbnail, fix an "Untitled resource" card, use artwork from a source/project/GitHub README, regenerate a paper preview, or find papers on a benchmark with missing, generic, stale, or broken thumbnails. This is the repo-specific end-to-end workflow for source selection, portrait composition, cache-safe publication, and live visual verification.

Hugging FaceSkill

create-trends

huggingface/paperswithcode/.agents/skills/create-trends/SKILL.md

Research, write, and optionally publish exactly three field-direction bullets for a Papers With Code task page's `research_trends`, synthesizing recurring themes from recent major papers, current trending work, and widely reported benchmarks. Use this whenever the user asks where an AI/ML field is headed, wants key trends or techniques extracted from recent papers, asks to add/update the three bullets below a task description, or wants a concise AlphaXiv-style classic-versus-recent overview—even if they only provide a task-page URL or task name.

Hugging FaceReads credentialsSkill

curate-daily-papers

huggingface/paperswithcode/.agents/skills/curate-daily-papers/SKILL.md

Curate Papers With Code papers from a Hugging Face daily-paper import: enrich official URLs and existing task/method tags, remove false-positive tags, add paper-native evaluation results only for existing benchmark/task/metric leaderboards, verify ranks and duplicates, and report every change. Use this whenever the user asks to curate yesterday's papers, run daily paper curation, enrich a daily import, process a specific Hugging Face paper date, or repeat the recent weekly paper-enrichment workflow for a day or date range.

Hugging FaceReads credentialsSkill

enrich-paper

huggingface/paperswithcode/.agents/skills/enrich-paper/SKILL.md

Enrich an existing paperswithcode paper with its official GitHub repository, project page, Hugging Face artifacts, existing-taxonomy task/method tags, and a recognizable thumbnail through `create-thumbnail`. Use whenever the user asks to enrich a paper, attach its code or project page, improve its tags, fill missing links, replace Gemini URL extraction, or complete an arXiv/external paper page. Finds authentic URLs from cheap authoritative evidence, preserves existing tags and HF normalization, rejects false-positive repos, and fixes thumbnails that are missing, generic, “Untitled resource,” stale, broken, wrongly versioned, squat, letterboxed, or otherwise unsuitable.

Hugging FaceSkill

grill-me

huggingface/paperswithcode/.agents/skills/grill-me/SKILL.md

Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".

Often used withbrief-to-tasks·writing-beats·request-refactor-plan

Hugging FaceSkill

handoff

huggingface/paperswithcode/.agents/skills/handoff/SKILL.md

Compact the current conversation into a handoff document for another agent to pick up.

Often used withdecision-mapping·setup-ts-deep-modules·to-questionnaire

Hugging FacePipes a download into a shellSkill

hf-cli

huggingface/paperswithcode/.agents/skills/hf-cli/SKILL.md

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 scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.

Often used withhuggingface-local-models