Agent skills you can check before you install
Public SKILL.md skills from GitHub repositories with 50+ stars and from the vendors themselves, scanned and scored. Install any of them with one command in Claude Code, Cursor, Codex, GitHub Copilot or Gemini CLI.
48,622 skills from 2,355 repositories · updated 20 hours ago
47,548 skills
- signup-flow-croalirezarezvani/claude-skillsUse when signup, registration or trial-start conversion is low or a signup flow is being designed. Finds the drop-off step with funnel_drop_analyzer.py and removes friction field by field.MarketingNo warnings★ 29.1K↓ —
- 01-auto-arenaagentscope-ai/OpenJudgeAutomatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style evaluation.TestingNo warnings★ 1K↓ —
- minutes-mcp-recallsilverstein/minutesRoute meeting-recall questions to the right Minutes MCP tool.OtherNo warnings★ 1.6K↓ —Also in 1 other repo
- planning-with-filesOthmanAdi/planning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; agent instructions read selected project planning context when invoked. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. This adapter registers no lifecycle or Stop hook, never requests continuation, and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.PlanningNo warnings★ 27.8K↓ —
- senior-backendalirezarezvani/claude-skillsUse for backend work: REST or GraphQL APIs, auth, microservices, PostgreSQL queries, migrations or load tests. Decides with backend_decision_engine.py, scaffolds with api_scaffolder.py, migrates with database_migration_tool.py and load-tests with api_load_tester.py.TestingNo warnings★ 29.1K↓ —
- public-plugin-packagingagentlas-ai/Agentlas-OSUse when packaging this meta-agent for public Codex plugin registration, Claude Code installation, GitHub release, one-line terminal install, or open-source distribution.EngineeringNo warnings★ 1.6K↓ —
- report-issuemicrosoft/power-platform-skillsUse this skill when the user wants to "report a bug", "file an issue", "report an issue", "submit a bug report", or report any problem with the power-automate plugin to the GitHub repository.OtherNo warnings★ 1.1K↓ —
- setup-datamodelmicrosoft/power-platform-skillsUse when the user wants to design or redesign the Dataverse schema and connector plan for an existing mobile app, or has an ER diagram (image, Mermaid, or text) to apply. Skip when the user is creating a brand-new app — /create-mobile-app handles the data model inline.DesignNo warnings★ 1.1K↓ —
- add-sample-datamicrosoft/power-platform-skillsUse when the user wants to seed Dataverse tables with realistic sample records so a freshly-scaffolded code app shows real-looking data on first launch. Generates contextually appropriate rows from each table's schema and inserts them in dependency order. Mirrors microsoft/power-platform-skills/power-pages/add-sample-data, adapted for mobile apps.EngineeringNo warnings★ 1.1K↓ —
- install-verificationagentlas-ai/Agentlas-OSUse when verifying that a generated agent package can be installed, discovered by runtimes, and checked without private dependencies.OtherNo warnings★ 1.6K↓ —
- agentlas-oneagentlas-ai/Agentlas-OSUse when the user types /agentlas-one on|off, /agentlas one on|off, or asks to enable/disable the persistent personal agent mode.OtherNo warnings★ 1.6K↓ —Also in 1 other repo
- train-sentence-transformershuggingface/skillsTrain 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 two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.OfficialProductivityNo warnings★ 11.2K↓ —
- colt-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when deciding whether a COLT (Conference on Learning Theory) paper needs numerical content at all — COLT has no experiments requirement — and, when numerics genuinely help, designing small illustrative simulations that visualize a proved bound, a separation, or a phase transition without diluting the theory.MediaNo warnings★ 1.3K↓ —
- huggingface-paper-publisherhuggingface/skillsPublish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.OfficialScienceNo warnings★ 11.2K↓ —
- huggingface-community-evalshuggingface/skillsRun evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.OfficialOtherNo warnings★ 11.2K↓ —
- huggingface-vision-trainerhuggingface/skillsTrains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.OfficialMediaNo warnings★ 11.2K↓ —
- hf-cloud-python-env-setuphuggingface/skillsSet up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.OfficialEngineeringNo warnings★ 11.2K↓ —
- add-datasourcemicrosoft/power-platform-skillsUse when adding an unspecified data source to an Expo/React Native Power Apps mobile app; routes to Dataverse, SharePoint, or another connector.EngineeringNo warnings★ 1.1K↓ —
- warranty-claimmohitagw15856/pm-claude-skillsGet a broken product repaired, replaced, or refunded under warranty — with the claim message written, the proof to attach, and the consumer-law backstop for when 'out of warranty' isn't the whole story. Use when asked to make a warranty claim, my [product] broke and it's still under warranty, the manufacturer won't honor the warranty, or how do I get this fixed for free. Produces a ready-to-send claim message, the exact evidence to include, the repair-vs-replace-vs-refund position, and an escalation ladder for when the first answer is no.LegalNo warnings★ 1.5K↓ —
- stakeholder-communicationrampstackco/claude-skillsCommunicate effectively with stakeholders across functions and seniority levels. Use this skill when writing status updates, preparing executive reviews, sharing technical decisions with non-technical audiences, managing up, communicating bad news, or designing the communication cadence for a project. Triggers on stakeholder update, status report, executive summary, exec review, manage up, communicate bad news, project comms, status meeting, weekly update. Also triggers when a project is going off track and the team needs to communicate it.WritingNo warnings★ 1K↓ —
- ase-submissionbrycewang-stanford/Awesome-Journal-SkillsUse when auditing an ASE (IEEE/ACM Automated Software Engineering) research-track submission for HotCRP readiness, covering the ACM acmart sigconf template and the 10+2 page budget, double-anonymous review, the mandatory Data Availability Statement, the early-rejection stage before rebuttal, and desk-reject triage before the deadline.ScienceNo warnings★ 1.3K↓ —
- image-to-uihashgraph-online/awesome-codex-pluginsRecreate an image, screenshot, or mockup as a 1:1 UI implementation. Match the reference rather than redesigning it, then render and compare the result to correct visible differences.DesignNo warnings★ 1.5K↓ —Also in 1 other repo
- vldb-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of a VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single points, tail-latency and throughput reporting, ablations that isolate the mechanism, and the loss-case disclosure PVLDB reviewers look for first.ScienceNo warnings★ 1.3K↓ —
- wacv-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing WACV experiments, covering Applications-track systems evidence (latency, power, robustness under real constraints) versus Algorithms-track matched-baseline novelty, comparative assessment under the deployed condition, uncertainty over seeds and sessions, ablations, and evidence that survives the two-round review.DevOpsNo warnings★ 1.3K↓ —
pip-responder
mohitagw15856/pm-claude-skills
WritingUpdated yesterday
From the author
Respond to a performance improvement plan strategically — decode what the PIP really is, decide fight-vs-land-softly with clear eyes, build the evidence file, and run the parallel job search the situation demands. Use when asked I was just put on a PIP what do I do, help me respond to a performance improvement plan, is my PIP survivable, or write my PIP check-in updates. Produces the honest read of the PIP, the two-track plan (perform + search), the documentation system, and templates for check-ins and the written response.
Install
npx skills add mohitagw15856/pm-claude-skills --skill pip-responder -a claude-code
Installs to .claude/skills/pip-responder/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Download .zip
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
What it can touch
Instructions onlyNo network use seenLicence: MIT
Safety checks on SKILL.md
No warnings.
Why 88
Security100
Provenance78
Maintenance100
Licence100
Adoption51
Files (1)
- SKILL.md