Skill-uri pentru agenți pe care le poți verifica înainte să le instalezi
Skill-uri SKILL.md publice din depozite GitHub cu peste 50 de stele și de la producători, scanate și evaluate. Instalează oricare dintre ele cu o singură comandă în Claude Code, Cursor, Codex, GitHub Copilot sau Gemini CLI.
48.168 de skill-uri din 2.325 de depozite · actualizat acum 19 ore
48.168 de skill-uri
- onekgpdK-Dense-AI/scientific-agent-skillsQueries the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals are homozygous-reference at a position, which variants exist in the dataset or carried by specified individuals in a gene or region, the relatedness between two specified individuals. Variants are returned with 1000 Genomes allele frequencies (AF), gnomAD v4.1 exome and genome AF, AlphaMissense score, and HGVSp annotations.ȘtiințăFără avertismente★ 55,5 K↓ —Apare și în 1 alt depozit
- rowanK-Dense-AI/scientific-agent-skillsRowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.DevOpsFără avertismente★ 55,5 K↓ —Apare și în 1 alt depozit
- networkxK-Dense-AI/scientific-agent-skillsCreates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.ȘtiințăFără avertismente★ 55,5 K↓ —Apare și în 1 alt depozit
- brand-identitycbrock84/headcountDefines and applies visual brand — logo usage, palette, typography, imagery direction, and the guidelines that keep expression consistent across product and marketing surfaces. Use this to establish a brand from scratch, audit whether an existing brand is being applied consistently, build brand guidelines, or decide how a brand should express itself in an unfamiliar format.DesignFără avertismente★ 2,5 K↓ —
- obsesssickn33/agentic-awesome-skillsPerfectionist mode for one target: checked attempts on a UI detail or hot path, keeps the best version, stops on a budget. Only on explicit /obsess.DesignFără avertismente★ 48,9 K↓ —
- data-protection-and-encryptioncbrock84/headcountProtects data itself rather than the systems around it — classifying what you hold, encrypting in transit and at rest and understanding what each actually defends against, managing keys and their rotation, handling secrets in applications and pipelines, minimizing and de-identifying, and deleting on purpose. Use this to design data protection for a system, assess an encryption claim, set up key or secret management, or work out what a stolen backup would actually expose.DesignFără avertismente★ 2,5 K↓ —
- gtarsK-Dense-AI/scientific-agent-skillsSupports Gtars for local genomic interval models and set algebra, overlaps and counts, consensus and coverage, tokenization, fragment processing, and refget/BEDbase planning across Python, Rust, and the CLI.ȘtiințăFără avertismente★ 55,5 K↓ —Apare și în 1 alt depozit
- blog-strategyAgriciDaniel/claude-blogBlog strategy development including topic cluster architecture with hub-and-spoke design, audience mapping, competitive landscape analysis, AI citation surface strategy across ChatGPT/Perplexity/AI Overviews, distribution channel planning (YouTube, Reddit, review platforms for AI-citation SEO), content scoring targets, measurement framework, and content differentiation through original research and first-hand experience. Use when user says "blog strategy", "content strategy", "blog positioning", "what should I blog about", "blog topics", "content pillars", "blog ideation".RedactareFără avertismente★ 2,6 K↓ —
- blog-clusterAgriciDaniel/claude-blogSemantic topic cluster planning and automated execution engine for claude-blog. Performs SERP-based keyword research, groups keywords by search intent and SERP overlap, builds a hub-and-spoke cluster architecture, generates an interactive SVG cluster map, and executes the full cluster by orchestrating blog-write calls with shared cluster context and automatic internal-link injection. Fills the strategy-to-execution gap: blog-strategy plans the blueprint, blog-cluster builds the house. Use when user says "blog cluster", "topic cluster", "content cluster", "cluster plan", "cluster execute", "pillar content", "hub and spoke", "content ecosystem", "cluster map".RedactareFără avertismente★ 2,6 K↓ —
- boss-zhipin-scrapereatmoreduck/boss-zhipin-scraperScrape BOSS直聘 (job listing site) via Chrome CDP. Searches jobs by keyword/city/filters, fetches JD details, outputs structured JSON/CSV with plaintext salary, and can summarize scraped results into a job-market prompt. Optional --company locally filters results by company name (boss_name substring) with no extra request. Use when user wants to search/analyze jobs on BOSS直聘 or zhipin.com.BrowserFără avertismente★ 1,7 K↓ —
- daskK-Dense-AI/scientific-agent-skillsScales pandas, NumPy, and custom Python research workflows beyond memory or across clusters with Dask. Covers DataFrames, Arrays, Bags, Futures, chunking, schedulers, and distributed diagnostics. Use for partitioned file processing, scientific array computation, or parallel tasks whose memory and dependency structure require Dask.DateFără avertismente★ 55,5 K↓ —Apare și în 1 alt depozit
- outbound-prospectingcbrock84/headcountFinds, qualifies, and reaches prospects through cold outreach — list building, qualification criteria, cold email and multi-channel sequences, and the follow-up that actually gets replies. Use this to build a prospect list, write cold outreach, fix a sequence that is not getting responses, define qualification criteria, or decide whether a segment is worth pursuing.RedactareFără avertismente★ 2,5 K↓ —
- chief-customer-officercbrock84/headcountOwns the customer's experience after the sale — support, success, escalation, and the feedback loop back into product. Use this for a decision spanning support and product, when service quality and cost are in tension, when deciding what to staff or automate, when a customer relationship is at risk above the account-manager level, or when nobody owns a recurring customer problem.AlteleFără avertismente★ 2,5 K↓ —
- test-gap-auditgithub/awesome-copilotRun a read-only audit for missing, weak, stale, or mis-scoped test coverage. If the user does not name a scope, audit the full repository and identify important code paths, routes, features, services, workflows, and contracts that lack proper tests. If the user names a feature, PR, branch, route, workflow, service, bug fix, API, security-sensitive path, or risky code change, focus only on that specific scope. Use when the user asks what tests are missing, whether coverage is enough, what regression tests to add, or how to prove a change is safe. This is not a general bug audit and not a security review; it evaluates whether behavior is covered by tests.TestareFără avertismente★ 40,6 K↓ —
- eval-driven-devgithub/awesome-copilotImprove AI application with evaluation-driven development. Define eval criteria, instrument the application, build golden datasets, observe and evaluate application runs, analyze results, and produce a concrete action plan for improvements. ALWAYS USE THIS SKILL when the user asks to set up QA, add tests, add evals, evaluate, benchmark, fix wrong behaviors, improve quality, or do quality assurance for any Python project that calls an LLM model.TestareFără avertismente★ 40,6 K↓ —
- aeonK-Dense-AI/scientific-agent-skillsThis skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.PlanificareFără avertismente★ 55,5 K↓ —Apare și în 1 alt depozit
- ai-ml-governancecbrock84/headcountGoverns models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.DevOpsFără avertismente★ 2,5 K↓ —
- chief-content-officercbrock84/headcountRuns content as an operation — the production pipeline, editorial calendar, repurposing engine, competitive content intelligence, and audits of what already exists. Use this to build or fix a content production system, plan a calendar, decide how one piece becomes many, rank content ideas by expected impact, or audit an existing library for what to update, consolidate, or retire. For deciding what territory to own and why, use `content-strategy` first.RedactareFără avertismente★ 2,5 K↓ —
- review-a-designinkeep/open-knowledgeReviews whether a design is SOUND — solving the right problem, derived from its stated goals and constraints — and emits ranked, evidence-backed findings, not edits. Read when asked to 'review this design', 'is this design sound', 'pressure-test this proposal', 'do a design review', 'does this solve the right problem', 'poke holes in this spec', 'should we build this', or to critique a proposal / spec / ADR / architecture or product decision. Do NOT read when the user wants to AUTHOR one of these — routing a new proposal is frame-a-proposal, a spec is write-a-spec, a decision record is record-a-decision, a postmortem is write-a-postmortem. Do NOT read for code review of a diff, or to fact-check individual claims (that is a correctness pass, a different job).DesignFără avertismente★ 4,7 K↓ —
- analytical-method-validationK-Dense-AI/scientific-agent-skillsPlans, executes, and documents validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.TestareFără avertismente★ 55,5 K↓ —
- bulk-rnaseqK-Dense-AI/scientific-agent-skillsPrepares bulk RNA-seq FASTQ, Salmon, STAR or featureCounts output for gene-level differential expression. Covers nf-core/rnaseq and standalone quantification, biological replication, strandedness, reference provenance, validated count assembly and a PyDESeq2 handoff. Use for FASTQ-to-counts analysis, nf-core/rnaseq configuration, STAR/Salmon quantification, or building a counts matrix for DESeq2. For single-cell data use scanpy; for statistical fitting alone use pydeseq2.ȘtiințăFără avertismente★ 55,5 K↓ —
- omero-integrationK-Dense-AI/scientific-agent-skillsInspects and automates microscopy data workflows against OMERO.server with omero-py, BlitzGateway, OMERO CLI, tables, annotations, ROIs, rendering, and documented OMERO.web APIs. Use this skill for scoped OMERO inventory, metadata export, import/export planning, or reviewed write workflows.MediaFără avertismente★ 55,5 K↓ —
- behavioral-marketingcbrock84/headcountApplies decision science and cognitive bias research to marketing and product decisions — how people actually choose under uncertainty, and how framing, defaults, sequencing, and social context change behavior. Use this to diagnose why a well-argued offer is not converting, to structure choices and pricing presentation, to design an experience around how attention and memory work, or to pressure-test whether a persuasion tactic is legitimate or manipulative.MarketingFără avertismente★ 2,5 K↓ —
- iam-helper-for-privileged-access-managementgoogle/skillsManages the end-to-end lifecycle of on-demand, temporary access using Privileged Access Manager (PAM). Use when a user asks to create, read, update, or delete PAM entitlements, request temporary access, or approve/deny pending PAM grants. Do NOT use for permanent IAM policy bindings, troubleshooting IAM permission errors, or general Google Cloud resource provisioning.OficialAlteleFără avertismente★ 21,7 K↓ —
fable-loop
Sahir619/fable-method
PlanificareActualizat 3 oct. 2026
De la autor
End-to-end orchestrated workflow that runs a task the way Fable ran sessions - parallel evidence subagents, one committed plan, surgical execution with an intent gate, adversarial verification agents, honest outcome-first report. Use for non-trivial multi-step tasks when the user says "/fable-loop", "run the fable loop", or "do this the way Fable would". For the rules alone without orchestration, use fable-method; for large multi-phase projects, prefer the GSD workflow and use this inside phases.
Instalare
npx skills add Sahir619/fable-method --skill fable-loop -a claude-code
Se instalează în .claude/skills/fable-loop/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Descarcă .zip
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
La ce are acces
Doar instrucțiuniNu am văzut acces la rețeaLicență: MIT
Verificări de siguranță pe SKILL.md
Niciun avertisment.
De ce 89
Securitate100
Proveniență80
Întreținere100
Licență100
Adopție54
Fișiere (2)
- SKILL.md
- agents/openai.yaml