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.
49.399 de skill-uri din 2.375 de depozite · actualizat acum 37 de minute
49.399 de skill-uri
- infographicsK-Dense-AI/scientific-agent-skillsCreates and reviews infographics with Nano Banana 2 via OpenRouter. Use for statistical summaries, timelines, comparisons, processes, and visual explanations with supplied data or optional Sonar research. Supports ten layouts, eight style presets, reference images, and accessible palette starting points.ȘtiințăFără avertismente★ 56 K↓ —
- hivemind-memoryactiveloopai/hivemindGlobal team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.ProductivitateFără avertismente★ 1,6 K↓ —
- exa-searchK-Dense-AI/scientific-agent-skillsSearches scientific and technical web content with Exa and extracts page or PDF text from URLs in batches. Supports scholarly discovery with the publication category and academic domain filters. Applies to requests to search the web, look up current research, fetch a page, or extract an article using Exa.DocumenteFără avertismente★ 56 K↓ —
- dbt_ingestKaelio/ktxMap dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.DateFără avertismente★ 1,6 K↓ —
- 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.DesignFără avertismente★ 56 K↓ —
- scenario-formatsscenario-labs/skillsUse when one approved visual, image or video, must ship at many sizes or placements through Scenario: adapting a master to 1:1, 4:5, 9:16, or 16:9, social formats for feeds and stories, YouTube thumbnails, storefront capsules and app store graphics, marketplace product images, link previews, display banners, a website hero, or choosing between crop, resize, expand, and generative reframe. Keywords: aspect ratio, resize, reframe, outpaint, expand, derivative, safe zone, thumbnail, banner.MediaFără avertismente★ 1,9 K↓ —
- what-if-oracleK-Dense-AI/scientific-agent-skillsSupports structured what-if scenario analysis for research planning, experimental contingencies, and scientific project decisions. Explores favorable, reference, adverse, wild-card, contrarian, and second-order scenarios with explicit assumptions, evidence, and decision triggers. Use to stress-test a research plan under uncertainty; scenario narratives do not estimate causal effects or calibrated forecast probabilities.TestareFără avertismente★ 56 K↓ —
- nwb-conversionK-Dense-AI/scientific-agent-skillsConverts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema validation, NWB Inspector findings and round-trip checks. Use for NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped behavioral position CSV; this skill does not perform spike sorting or claim tested support for arbitrary acquisition formats.MarketingFără avertismente★ 56 K↓ —
- statistical-powerK-Dense-AI/scientific-agent-skillsCalculates sample sizes and statistical power for study planning. Applies when someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for complex designs — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Also handles requests that only mention an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.TestareFără avertismente★ 56 K↓ —
- wiki_captureKaelio/ktxktx's knowledge base - wiki pages for durable, reusable business knowledge. Covers capture workflow for user preferences, metric definitions, organizational conventions, and cross-references between wiki pages and semantic-layer sources. Loaded by the post-turn memory-agent only. The research agent reads wiki via `wiki_read`/`wiki_search` but does not write it.ȘtiințăFără avertismente★ 1,6 K↓ —
- 13c-metabolic-fluxK-Dense-AI/scientific-agent-skillsEstimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments requiring nonstationary MFA.AlteleFără avertismente★ 56 K↓ —
- sl_captureKaelio/ktxHow to capture new reusable patterns into ktx's semantic layer - when a measure, segment, or join belongs in the catalog and how to write it generically so it stays small and useful over time. Loaded by the post-turn memory-agent only. The research agent does not write to the SL.ȘtiințăFără avertismente★ 1,6 K↓ —
- pgvector-semantic-searchtimescale/pg-aiguideUse this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.DateFără avertismente★ 1,9 K↓ —
- protocolsio-integrationK-Dense-AI/scientific-agent-skillsReads, validates, and safely exports protocols.io data with current official REST/MCP contracts, or creates non-executing mutation plans. The bundled client makes bounded official-host GET requests only with explicit --execute. Use only for tasks explicitly targeting protocols.io or an exact protocols.io protocol version.PlanificareFără avertismente★ 56 K↓ —
- clinical-decision-supportK-Dense-AI/scientific-agent-skillsPrepares and validates research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Supports aggregate or synthetic research documentation and traceability, excluding patient care and live clinical operation.ȘtiințăFără avertismente★ 56 K↓ —
- exploratory-data-analysisK-Dense-AI/scientific-agent-skillsPerforms bounded, local exploratory analysis of explicitly supported scientific files. Supports redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain formats are reference-only and unknown formats fail closed.DateFără avertismente★ 56 K↓ —
- xiaohongshu-auto-postingbrowser-act/skillsAutomates the complete Xiaohongshu (XHS / Little Red Book) content operation workflow: pain-point topic collection → style case collection → topic selection → content writing → publishing → performance tracking. Use when user mentions xiaohongshu auto posting, xhs auto post, little red book posting, xiaohongshu post, xiaohongshu auto posting, xiaohongshu content operations, xhs content marketing, post to xiaohongshu, publish on xhs, post on xiaohongshu, xiaohongshu promotion, xiaohongshu operations, xiaohongshu automation, xhs automation, track xhs performance, track xiaohongshu performance, xiaohongshu data tracking, xiaohongshu analytics, switch xhs account, update xhs keywords.DateFără avertismente★ 6,3 K↓ —
- slKaelio/ktxktx's semantic layer - a structured catalog of sources (tables/views), measures, joins, and segments expressed as YAML. Covers the schema and how to query it via `sl_query`. Use when the task involves querying pre-defined metrics (ARR, churn, retention, LTV, MAU) or reading SL source YAML to understand the catalog. Capture is handled by the `sl_capture` skill (memory-agent only).ProductivitateFără avertismente★ 1,6 K↓ —
- dependency-and-risk-managementcbrock84/headcountManages delivery risk and cross-team dependencies — identifying, sizing, mitigating and escalating what could stop the work. Use this to build a risk register that gets used, manage dependencies between teams, decide what to escalate and when, or work out why the same risks keep materializing unmanaged.AlteleFără avertismente★ 2,6 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★ 56 K↓ —Apare și în 2 alte depozite
- chief-operating-officercbrock84/headcountAn agent organization structured as a company — 15+ departments, 125+ skills, each independently installable, citing the standards and regulators that settle the question. Runs in Claude Code and ChatGPT.JuridicFără avertismente★ 2,6 K↓ —
- business-continuity-and-resiliencecbrock84/headcountPlans for operating through disruption — impact analysis, recovery objectives, continuity plans, and the exercises that prove they work. Use this to run a business impact analysis, set RTO and RPO, write or test a continuity plan, prepare for a supplier or site failure, or answer a customer's resilience questionnaire.TestareFără avertismente★ 2,6 K↓ —
- huashu-video-outlinealchaincyf/huashu-skills快速生成2-3个视频大纲方案，含标题、封面建议和结构设计。当用户提到"视频大纲"、"视频结构"、"脚本大纲"、"视频选题"时使用。MediaFără avertismente★ 2,2 K↓ —
- obviously-awesomewondelai/skillsDefine product positioning by mapping competitive alternatives, unique attributes, and best-fit customers to the right market category. Use when the user mentions "positioning", "competitive alternatives", "how to position", "market category", "positioning canvas", "repositioning", "category creation", "what category are we in", or "why prospects dont get what we do". Also trigger when launching a new product, entering a crowded market, or diagnosing why prospects dont grasp the product's value. Covers the positioning canvas and team workshops. For customer jobs analysis, see jobs-to-be-done. For go-to-market, see crossing-the-chasm.AlteleFără avertismente★ 2,6 K↓ —Apare și în 2 alte depozite
genomic-coordinates
K-Dense-AI/scientific-agent-skills
ȘtiințăActualizat ieri
De la autor
Converts genomic intervals between coordinate conventions, normalises and compares variant representations, and detects assembly or contig-naming mismatches before they corrupt an analysis. Used whenever coordinates cross a format, tool, or assembly boundary - converting between BED, GFF/GTF, VCF, SAM/BAM, WIG, PSL, genePred, Picard interval_list, or region strings; reconciling 0-based half-open with 1-based inclusive; left-aligning or trimming indels; checking whether two variant records describe the same change; mapping genomic to transcript, CDS, or protein positions; auditing a BED/GTF/VCF for convention violations; or diagnosing GRCh37 vs hg19 vs GRCh38 vs T2T, chr-prefix, and liftover problems. Triggers include "off by one", "0-based", "1-based", "half-open", "coordinate system", "left-align", "normalize variant", "bcftools norm", "chr prefix", "wrong genome build", "liftover", "REF mismatch", and "HGVS".
Instalare
npx skills add K-Dense-AI/scientific-agent-skills --skill genomic-coordinates -a claude-code
Se instalează în .claude/skills/genomic-coordinates/ · 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
Rulează scripturile incluseNu am văzut acces la rețeaLicență: MIT
Verificări de siguranță pe SKILL.md
- Include scripturi pe care nu le-am verificatScăzut
De ce 89
Securitate85
Proveniență95
Întreținere100
Licență100
Adopție73
Fișiere (10)
- SKILL.md
- references/format-conventions.md
- references/reference-builds.md
- references/transcript-coordinates.md
- references/variant-representation.md
- scripts/_common.py
- scripts/audit_intervals.py
- scripts/check_contigs.py
- scripts/convert_coords.py
- scripts/normalize_variant.py