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
- hivemind-graphactiveloopai/hivemindQuery the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what is the architecture / which subsystems exist?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).PlanningNo warnings★ 1.6K↓ —
- pydeseq2K-Dense-AI/scientific-agent-skillsPerforms bulk RNA-seq differential expression analysis with PyDESeq2, including count validation, formula designs, explicit contrasts, Wald tests, FDR correction, coefficient-matched LFC shrinkage, and result visualization. Use for PyDESeq2 or Python DESeq2 workflows with biological replicates.TestingNo warnings★ 56.1K↓ —Also in 1 other repo
- CodeLeoYeAI/agent-master-skillsCoding workflow with planning, implementation, verification, and testing for clean software development.EngineeringNo warnings★ 2.2K↓ —
- schema-explorationtimescale/pg-aiguideExplore an existing PostgreSQL database before answering questions about its data or writing SQL. Use this skill whenever a user asks for a query or a data-backed answer against an unfamiliar schema (counts, missing or failed records, recent changes), asks where a business concept lives, or asks how tables, joins, views, routines, triggers, RLS, or extensions work. Find the relevant objects with read-only pg_catalog queries, then request approval before inspecting data-derived statistics or rows. Not a schema-design or migration guide.DataNo warnings★ 1.9K↓ —
- sympysickn33/agentic-awesome-skillsSymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations.EngineeringNo warnings★ 49.1K↓ —
- einfuehrung-systemKlotzkette/claude-fuer-deutsches-rechtFür Energierecht: System einführend: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.OtherNo warnings★ 1.8K↓ —
- design-postgres-tablestimescale/pg-aiguideUse this skill for general PostgreSQL table design. **Trigger when user asks to:** - Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones. - Choose data types, constraints, or indexes for PostgreSQL - Create user tables, order tables, reference tables, or JSONB schemas - Understand PostgreSQL best practices for normalization, constraints, or indexing - Design update-heavy, upsert-heavy, or OLTP-style tables **Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security Comprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.DataNo warnings★ 1.9K↓ —
- timescaledb-hyperfunctionstimescale/pg-aiguideUse this skill when writing analytical SQL over time-series data with the TimescaleDB Toolkit (timescaledb_toolkit extension) hyperfunctions: approximate percentiles, statistical summaries, time-weighted averages, counter/gauge rates, uptime/heartbeat tracking, state durations, OHLC candlesticks, approximate distinct counts, top-N, and downsampling. **Trigger when user asks to:** - Compute percentiles/medians/p95/p99 over large or rolled-up time-series data - Compute rates or deltas from monotonic counters (Prometheus-style) or gauges - Compute time-weighted averages or integrals over irregularly sampled data - Track uptime/downtime from heartbeats, or time spent in each state - Build OHLC/candlestick or VWAP data for financial ticks - Store re-aggregatable summaries in continuous aggregates (two-step aggregation, rollup) - Approximate COUNT DISTINCT, find top-N / most frequent values, or downsample for charts **Keywords:** timescaledb_toolkit, hyperfunctions, percentile_agg, uddsketch, tdigest, approx_percenDataNo warnings★ 1.9K↓ —
- anpassenKlotzkette/claude-fuer-deutsches-rechtFür Customize – Praxisprofil anpassen: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.OtherNo warnings★ 1.8K↓ —
- dsh-find-simplificationsNewqufei1993/skills-hub用户明确要求审查或实施简化时使用，识别不必要的抽象、重复状态和兼容机制，并验证受支持行为得到保留。OtherNo warnings★ 1.9K↓ —
- huashu-article-to-xalchaincyf/huashu-skills长文精简为X平台内容（200-500字），保留核心观点和个人风格。当用户提到"转微博"、"发小红书"、"社交媒体"、"缩短文章"时使用。WritingNo warnings★ 2.2K↓ —
- anpassenKlotzkette/claude-fuer-deutsches-rechtFür Praxisprofil anpassen: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: Gesellschaftsrecht.OtherNo warnings★ 1.8K↓ —
- impressum-pflichtKlotzkette/claude-fuer-deutsches-rechtFür Impressumspflicht (Paragrafen 5. 6 DDG, Paragraf 18 MStV): ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.OtherNo warnings★ 1.8K↓ —
- apigee-agent-gateway-governancegoogle/skillsStands up an Apigee API proxy that a Google Cloud Agent Gateway calls as an ext-proc (Service Extension) callout on an agent's LLM traffic, to meter token usage per model, extract caller identity, and apply Model Armor prompt screening or a semantic cache. Use when wiring Apigee in as an AI gateway behind an Agent Gateway, when an agent's model egress needs governing, or when a callout proxy returns no verdict and you need to find out why. The distinguishing question is whether Apigee forwards the traffic or only inspects it: this skill is inspect-only, and builds the proxy itself. Don't use it when Apigee is meant to receive a request and route it onward to a backend — including hosting an MCP server behind Apigee — which needs a proxy with a target instead. Don't use for governing the agent's calls to tools or MCP servers. Don't use for choosing or switching which model a request goes to. Don't use for creating the Agent Gateway itself, nor for Apigee hybrid.OfficialEngineeringNo warnings★ 21.9K↓ —
- scenario-ad-variantsscenario-labs/skillsUse when making multiple independent ad or campaign edits from existing footage with Scenario: hook, CTA, copy, end-card, placement, or targeted visual variants. Also for revising one variant or resuming a partially completed batch. Not for a new commercial from a still image, ordinary single-clip grading, or automatic Cartesian combinations.MarketingNo warnings★ 2.2K↓ —
- scenario-meshyscenario-labs/skillsUse when creating or refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo or 1-4 multi-view angles, text-to-3D, retexturing an existing GLB, remeshing to a target polycount, UV unwrapping, auto-rigging a humanoid character, or applying a library animation clip. Keywords: Meshy 7 and Meshy 6, Ultra mode, Smart Topology, PBR maps, texture prompt, triangle or quad topology, game-ready mesh, A-pose, T-pose, GLB pipeline.MediaNo warnings★ 2.2K↓ —
- umap-learnK-Dense-AI/scientific-agent-skillsApplies UMAP-learn to nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.OtherNo warnings★ 56.1K↓ —
- postgres-hybrid-text-searchtimescale/pg-aiguideUse this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.DataNo warnings★ 1.9K↓ —
- primer-designK-Dense-AI/scientific-agent-skillsDesigns and audits PCR and RT-qPCR primers with Primer3, explicit thermodynamic conditions, reference-based off-target amplification searches, and traceable sequence coordinates. Use for designing primer pairs, checking existing primers, exon-junction or isoform-specific assays, variant masking, cloning tails, multiplex compatibility, and interpreting Primer-BLAST results. Includes bounded local in-silico PCR and BLAST screening; distinguishes computational candidates from experimentally validated assays.DesignNo warnings★ 56.1K↓ —
- pkpd-modelingK-Dense-AI/scientific-agent-skillsPharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when analysing concentration-time data, deriving exposure metrics, fitting PK or PD models, or evaluating dosing regimens. Triggers include "pharmacokinetics", "pharmacodynamics", "PK/PD", "NCA", "non-compartmental", "AUC", "Cmax", "lambda z", "half-life", "clearance", "volume of distribution", "compartmental model", "population PK", "popPK", "NONMEM", "nlmixr2", "Pharmpy", "Monolix", "exposure-response", "Emax", "EC50", "indirect response", "effect compartment", "TMDD", "PBPK", "bioequivalence", "RSABE", "ABEL", "allometric scaling", "first-in-human", "MABEL", "drug-drug interaction", "DDI", "ICH M12", "concentration-QTc", "therapeutic drug monitoring", "MIPD", and "dosing regimen".OtherNo warnings★ 56.1K↓ —
- tddNewLeoYeAI/agent-master-skillsRun a red-green-refactor TDD workflow — generate failing tests first, implement to green, then check coverage gaps. Usage: /tdd <generate|coverage|validate> [target]TestingNo warnings★ 2.2K↓ —
- foundry-agentic-loopgithub/awesome-copilotBuild agentic solutions on Microsoft Foundry, GitHub and Azure: hosted agents, toolbox tools and skills, Foundry IQ grounding and a React/FastAPI app. Three modes: rapid prototyping in an existing Foundry project; spec-driven development (SDD), where the foundry-agentic-loop-workflow runs Specify, Implement, Verify and Deploy to create the Foundry project and deploy with azd, or one phase runs alone; and Improve, which refines this skill and the workflow from past runs. Use for Agentic Loop requests and canvas actions, or end-to-end agent solutions from architecture to Azure deployment. Not for isolated Azure administration or unrelated UI edits.DevOpsNo warnings★ 40.8K↓ —
- puaLeoYeAI/agent-master-skillsForces exhaustive problem-solving with corporate PUA rhetoric. Trigger on: task failed 2+ times, about to give up, suggesting user do it manually, blaming environment unverified, stuck in loops, passive behavior, or user frustration ('try harder', 'figure it out', '换个方法', '为什么还不行'). ALL task types. Not for first failures.ProductivityNo warnings★ 2.2K↓ —
- exklusivitaetKlotzkette/claude-fuer-deutsches-rechtFür Exklusivität: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: AGB-Recht-Prüfer. Route: exklusivitaet.OtherNo warnings★ 1.8K↓ —
datanalysis-credit-risk
github/awesome-copilot
OtherUpdated 23 hours ago
From the author
Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
Install
npx skills add github/awesome-copilot --skill datanalysis-credit-risk -a claude-code
Installs to .claude/skills/datanalysis-credit-risk/ · 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: MIT
Safety checks on SKILL.md
- Ships scripts we have not reviewedLow
Why 89
Security85
Provenance95
Maintenance100
Licence100
Adoption71
Files (4)
- SKILL.md
- references/analysis.py
- references/func.py
- scripts/example.py