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.
49,492 skills from 2,391 repositories · updated 8 hours ago
49,492 skills
- get-available-resourcesK-Dense-AI/scientific-agent-skillsDetects host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits when a user asks for resource-aware planning or before a clearly resource-sensitive local workload. Produces a redacted JSON snapshot and conservative planning helpers without stress tests or assuming visible host hardware is usable.TestingNo warnings★ 56.1K↓ —
- transformersK-Dense-AI/scientific-agent-skillsHugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Applies when working with AutoModel, pipelines, tokenizers, generation configs, or TrainingArguments within Transformers.MediaNo warnings★ 56.1K↓ —
- relsa-severity-assessmentK-Dense-AI/scientific-agent-skillsSupports multivariate severity assessment and exploratory endpoint-time score forecasting for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one severity score per animal per day, when asking which animals are at risk of reaching a humane endpoint at a specified future observation time, when defining attention/danger zones or thresholds on a severity scale by kernel density estimation, or when reporting severity for a 3Rs, refinement, animal-welfare, or EU Directive 2010/63/EU severity-assessment context. Covers directionality ("turned" variables), baseline normalization, reference sets, RELSA weights, ARIMA prediction intervals, and RMSE/PICP/MPIW evaluation.ScienceNo warnings★ 56.1K↓ —
- bigquery-migration-sql-translatorgoogle/skillsTranslates SQL queries, DDL schemas, and stored procedures from legacy database dialects (Teradata, Oracle, Redshift, Snowflake, SparkSQL, HiveQL) to BigQuery GoogleSQL using the BigQuery Migration Service (BQMS) MCP server. Use when translating legacy SQL queries, migrating schemas or metadata zip archives, or configuring BigQuery Migration Service (BQMS) YAML translation settings. Don't use for executing BigQuery queries or managing datasets and tables (use bigquery-basics), diagnosing runtime job errors (use bigquery-troubleshooting), or tuning slot reservations (use bigquery-optimization).OfficialDataNo warnings★ 21.9K↓ —
- astropysickn33/agentic-awesome-skillsAstropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.DataNo warnings★ 49.1K↓ —
- scenario-brand-kitscenario-labs/skillsUse when building a visual identity or brand kit with Scenario: a logo, wordmark, or icon as editable SVG, a color palette and typography spec, logo variants (mono, reversed, favicon), and brand applications like social templates, avatars, banners, or merchandise mockups. Also for a rebrand or refreshing an existing mark. Keywords: brandkit, brand kit, visual identity, logo, monogram, wordmark, SVG, vectorize, color palette, typography, brand guidelines, brand board, launch kit.DesignNo warnings★ 2.2K↓ —
- astropyK-Dense-AI/scientific-agent-skillsCore Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.DataNo warnings★ 56.1K↓ —Also in 2 other repos
- scenario-luma-imagescenario-labs/skillsUse when generating or editing images with Luma Uni-1 models on Scenario via MCP: text-to-image, prompt-based editing of an existing image, style or character reference images with named roles, web search grounding for real-world subjects, rendering exact title text into posters, aspect ratio control, or choosing between Uni-1 Max and Uni-1. Keywords: Luma Labs, Uni-1, Photon, txt2img, img2img, image edit, reference images, webSearch, reasoning image model.MediaNo warnings★ 2.2K↓ —
- skeallLeoYeAI/agent-master-skillsAgent Skills (SKILL.md) builder, auditor, and improver for cross-platform LLM agents. Use for "skeall", "build a skill", "create skill", "improve skill", "audit skill", "skill review", or any SKILL.md question. Follows agentskills.io standard.EngineeringNo warnings★ 2.2K↓ —
- nature-citationYuan1z0825/nature-skillsFind and verify Nature/CNS-family literature supporting manuscript claims, with claim-to-source mapping and reference-manager export. Use for Nature系列引用、CNS支撑文献、分段补引用 when this journal scope is requested; use broader literature search for unrestricted sources.OtherNo warnings★ 52.2K↓ —
- typeuibergside/typeuiUse TypeUI with Grok to access project design skills, brand kits, UI prompts, and layout variations through the TypeUI MCP server when creating or refining user interfaces.DesignNo warnings★ 2.2K↓ —
- 007sickn33/agentic-awesome-skillsSecurity audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.SecurityNo warnings★ 49.1K↓ —
- opcodeLeoYeAI/agent-master-skillsZero-token execution layer for AI agents. Define workflows once, run them free forever — persistent, scheduled, deterministic. 6 MCP tools over SSE. Supports DAG-based execution, 6 step types (action, condition, loop, parallel, wait, reasoning), 26 built-in actions, ${{}} interpolation, reasoning nodes for human-in-the-loop decisions, and secret vault. Use when defining workflows, running templates, checking status, sending signals, querying workflow history, or visualizing DAGs.ProductivityNo warnings★ 2.2K↓ —
- scenario-texturesscenario-labs/skillsUse when a task involves game textures or materials through the Scenario MCP: seamless or tileable textures, themed texture packs (brick, wood, stone, floors, hand-painted), PBR materials (albedo, metallic, roughness, normal) on 3D assets, retexturing a mesh, material iteration from reference images, texture upscaling that must preserve tiling, or sizing textures for game engines. Keywords: seamless texture, tileable, PBR, material, retexture, texture upscale, surface.ProductivityNo warnings★ 2.2K↓ —
- toobitLeoYeAI/agent-master-skillsTrade crypto on Toobit exchange via natural language. Spot & USDT-M futures trading, market data queries, wallet management. Use when user mentions Toobit, or wants to trade/query crypto on Toobit.FinanceNo warnings★ 2.2K↓ —
- mandateLeoYeAI/agent-master-skillsUse when enforcing spend limits on AI agent wallets, validating transactions before signing, configuring allowlists or approval workflows, detecting prompt injection in agent reasoning, scanning codebases for unprotected wallet calls, or auditing agent transaction history. Works with OpenClaw, Claude Code, GOAT, AgentKit, ElizaOS. Supports Bankr, Locus, Sponge, CDP wallets. Non-custodial: private keys never leave your machine.EngineeringNo warnings★ 2.2K↓ —
- expo-uisickn33/agentic-awesome-skillsBuild native UI with the @expo/ui package: real SwiftUI on iOS and Jetpack Compose on Android rendered from React in an Expo or React Native app.DesignNo warnings★ 49.1K↓ —
- scenario-mai-imagescenario-labs/skillsUse when generating or editing images with Microsoft MAI Image models on Scenario via MCP: photoreal or stylized text-to-image with legible in-image typography, posters, packaging, magazine covers, ads, key art, or instruction-based editing such as text swaps, recoloring, object removal or replacement, background swaps, lighting changes, and full restyles. Keywords: MAI Image 2.5, 2.5 Pro, 2.5 Edit, 2.5 Pro Edit, Microsoft, txt2img, img2img, typography, text rendering, image edit.MediaNo warnings★ 2.2K↓ —
- cloud-monitoring-notification-channels-configurationgoogle/skillsGenerates Terraform HCL configurations for Cloud Monitoring notification channels (`google_monitoring_notification_channel`) and adds notification channels to alerting policies (`google_monitoring_alert_policy`) in Google Cloud projects. Use when creating or configuring email, SMS, Slack, PagerDuty, Pub/Sub, or webhook notification channels, verifying Pub/Sub topic compatibility (including cross-project topics, message storage policies, and in-transit enforcement) and publishing permissions for the Cloud Monitoring Service Agent, or adding notification channels to existing alert policies. Don't use for authoring or editing alerting policy conditions, selecting metric descriptors, or writing PromQL or Monitoring Filter queries.OfficialDevOpsNo warnings★ 21.9K↓ —
- pyhealthK-Dense-AI/scientific-agent-skillsBuilds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.ScienceNo warnings★ 56.1K↓ —Also in 1 other repo
- webapp-testinggithub/awesome-copilotToolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.TestingNo warnings★ 40.8K↓ —
- histolabK-Dense-AI/scientific-agent-skillsExtracts and preprocesses whole-slide histology image tiles with Histolab. Use for WSI inspection, tissue masks, random/grid/score-based tile extraction, H&E stain normalization, and tile dataset preparation. For multiplexed imaging or deep learning inference pipelines, use pathml.DocumentsNo warnings★ 56.1K↓ —Also in 1 other repo
- live_database_ingestKaelio/ktxCapture semantic-layer and knowledge updates from a live database schema snapshot.DataNo warnings★ 1.6K↓ —
- historic_sql_patternsKaelio/ktxIdentify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection.DataNo warnings★ 1.6K↓ —
dbt-sf-to-bq-translator
google/skills
OfficialDataUpdated 16 hours ago
From the author
Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to BigQuery on Google Cloud. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.
Install
npx skills add google/skills --skill dbt-sf-to-bq-translator -a claude-code
Installs to .claude/skills/dbt-sf-to-bq-translator/ · 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
Runs bundled scriptsNo network use seenLicence: APACHE-2.0
Safety checks on SKILL.md
- Ships scripts we have not reviewedLow
Why 89
Security85
Provenance100
Maintenance100
Licence100
Adoption67
Files (5)
- SKILL.md
- references/attribution.md
- references/dbt_migration_patterns.md
- scripts/bulk_translate_via_gcloud.py
- scripts/dbt_translator.py