Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Net asset value (NAV) reconciliation and tie-out for Chinese funds and investment portfolios. Adapts the original nav-tieout skill for A-share holdings, Chinese fund accounting standards, and domestic custody arrangements. Triggers on "A股净值核对", "NAV计算", "NAV tie-out China", "基金净值", "net asset value [fund]", or "portfolio valuation [date]".
Execute database migrations across ORMs and platforms with zero-downtime strategies, data transformation, and rollback procedures. Use when migrating databases, changing schemas, performing data transformations, or implementing zero-downtime deployment strategies.
Audit relational schema normalization — 1NF through 3NF violations, repeating groups and CSV-in-a-column, partial and transitive functional dependencies, and the inverse trap of premature or undisciplined denormalization. Module M1. Feeds the Design & Integrity score (Modelado category).
This skill should be used when a user needs help debugging a Snowflake SQL query, understanding a Snowflake query failure, isolating incorrect results, fixing joins or filters, diagnosing performance problems, or turning a vague warehouse error into a concrete root cause and corrected query. It is a standalone Snowflake query debugging skill for ad hoc SQL work across databases, schemas, and environments.
Design, tune, or review EF Core data access with proper modeling, migrations, query translation, performance, and lifetime management for modern .NET applications. USE FOR: DbContext, migrations, model configuration, EF queries, tracking, loading, performance, transactions, and EF6 migration decisions. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
Methodology for exploratory data analysis on scientific files. Decision frameworks by data type (tabular, sequence, image, spectral, structural, omics), quality assessment, report generation, format detection across 200+ formats. Use when given a data file for initial exploration or to pick an analysis before a pipeline.
Any live-web task via the Firecrawl CLI — including ordinary web research: searching the web, reading or extracting pages, gathering sources, discovering site URLs, bulk extraction, downloading a site, change alerts, or pages needing clicks/login — web only; local files route to firecrawl-parse. For US legal or regulatory questions use gov; for papers use firecrawl-research-index; for library, API, error, or bug questions use firecrawl-developer-index.
Comprehensive skill for interacting with Grafana's HTTP API to manage dashboards, data sources, folders, alerting, annotations, users, teams, and organizations. Use when Claude needs to (1) Create, read, update, or delete Grafana dashboards, (2) Manage data sources and connections, (3) Configure alerting rules, contact points, and notification policies, (4) Work with folders and permissions, (5) Manage users, teams, and service accounts, (6) Create or query annotations, (7) Execute queries against data sources, or any other Grafana automation task via API.
Evaluates GraphRAG systems across knowledge graph completeness, retrieval relevance, answer correctness, reasoning depth, and hallucination prevention. Provides structured evaluation frameworks, metric selection guidance, and testing protocols. Use when evaluating GraphRAG quality, benchmarking multi-step reasoning, measuring hallucination reduction, or when user mentions evaluate GraphRAG, quality metrics, answer correctness, test my GraphRAG, or measure RAG performance.
Fast-track GTM value preview for new users. Runs gtm-analytics-audit and gtm-strategy back to back and outputs the top 5 tracking opportunities with business rationale and effort estimates. No implementation, no DOM changes. Just a clear answer to "what should I track and why". Trigger on - "quickstart", "what should I track", "show me tracking opportunities", "quick GTM overview", "I'm new to GTM", "where do I start".
Kuaishou comment analysis tool. Enter a work link to fetch first-level comment data, with paged browsing and comment sentiment analysis (positive/negative/demand/competitor), generating a polished HTML report. Use when the user needs to view Kuaishou work comments, analyze comment sentiment, or understand user feedback. Triggers: 快手评论, 作品评论, 评论查询, 评论分析, 评论舆情, 看评论.
Use this skill when the user says "data review", "viral analysis", "analyze my content", "break down a creator's viral videos", or "analyze which episode performed best". When the user says "full benchmark analysis", "cross-creator analysis", "which topics go viral most easily", or "summarize viral patterns", run in full-benchmark mode and read all viral topic analysis reports for horizontal comparison. Automatically read published video scripts, classify them by viral tier (✈️🚗🛵🚲🚶), extract reusable creative patterns such as title formulas, content structures and topic types, and generate a classified summary report. Do not trigger for: topic recommendations (use li-recorder) or script writing (use li-writer).
One-stop AI toolkit for Amazon product selection, integrating 33 sub-capabilities across 12 tool categories — competitor lookup, ABA, front-end, Keepa, Sorftime, Jungle Scout, Sellics, SIF, Jimu, business insights, and more — covering product selection, keywords, competitors, reviews, niches, and trends.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Verifies that implemented neural network models correctly respect their intended symmetries through systematic equivariance testing, layer-wise isolation, and gradient analysis. Use when testing model equivariance, debugging symmetry bugs, verifying implementation correctness, checking if a model is actually equivariant, or diagnosing why an equivariant model isn't working.
Run graph queries on Obsidian vault link structure: neighbors (N hops), shortest path, connected clusters, hub notes, bridge/articulation points, enriched orphans, relationship summaries, vault-wide statistics. Supports configurable frontmatter relationship fields. Use for: knowledge graph, note relationships, graph query, link analysis, note connections, hub notes, cluster analysis, bridge notes, relationship summary, vault stats, find path between notes, analyze connections, show orphans, 筆記關係, 連結分析, 孤立筆記, 知識圖譜, 樞紐筆記, vault 統計.
This skill MUST be used to combine qualitative and quantitative research (質性與量化整合、混合研究), including research design, literature review, evidence synthesis, and innovation (研究設計、文獻探討、證據綜整、創新). It SHOULD be used when a research question benefits from integrating contextual understanding with measurement or testing. It MUST NOT force numerical synthesis when evidence is unsuitable or override explicit single-method constraints.
Operate PostgreSQL instances safely: configuration review, index and query-plan analysis, vacuum and bloat management, WAL archiving and point-in-time recovery, replication and failover, extensions, major-version upgrades, and evidence-based diagnostics with the bundled read-only pgdiag script. Use when running or inspecting a PostgreSQL server, diagnosing performance or backup health, or planning an upgrade or failover. Do not use for application-level data access patterns (that's backend-engineering) or schema design (that's data-architect/data-engineering).
Every Apify platform feature, plus a local SQLite store, cross-Actor search, novelty diffing, cost-aware runs, and... Trigger phrases: `scrape twitter for AI mentions`, `run apify actor`, `what's new in my reddit scraper`, `build a newsletter from this week's scrapes`, `how much did my apify scrapes cost this month`, `use apify`, `run apify`.
Translate privacy principles and legal requirements into data-flow, lifecycle, acceptance, and verification artifacts. Map data classification, purpose, processing, access, retention, deletion, residency, and consent; define verifiable privacy acceptance criteria; and produce data-lifecycle records, retention/deletion verification plans, and privacy change reviews. Use when engineering privacy into a system, feature, or data flow — not for legal advice, jurisdiction-specific regulatory interpretation, or replacing security engineering or incident response.
Pipeline skill for protein structure analysis covering PDB/mmCIF parsing, RMSD superposition, Ramachandran/dihedral analysis, binding pocket detection, contact maps, B-factor flexibility, DSSP secondary structure, and format conversion. Triggers on PDB, protein structure, RMSD, Ramachandran, binding pocket, Biopython, PyMOL, pocket detection, fpocket, DSSP, PDBQT, structural alignment, superposition.
Guidance for the Microsoft Purview Unified Catalog (data catalog) — business-friendly discovery, governance domains, data products, glossary terms, and data quality on top of the Data Map. Covers governance domains, data products, and curation. WHEN: Purview data catalog, unified catalog, data products, governance domain, business glossary, data quality, data discovery for analysts, curate data assets, data stewardship.
Python bridge to ImageJ2/Fiji for macros, plugins (Bio-Formats, TrackMate, Analyze Particles), NumPy↔ImagePlus/ImgLib2 exchange, and ImageJ Ops. Automates Fiji headlessly from Python. Use scikit-image for pure Python without Fiji plugins; napari for visualization.
Prepare and seed test data for a feature into a target environment. Analyzes the feature's implementation, proposes data categories covering regular and edge cases, and populates the environment via existing seed mechanisms code access, REST, MCP. Use when the user asks to seed test data, populate a test or staging environment, prepare data for manual/QA testing of a feature, generate a dataset for a feature, or create fixtures in an environment.
Cheminformatics toolkit for molecular analysis and virtual screening: SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints (Morgan/ECFP, MACCS), Tanimoto similarity, SMARTS substructure filtering, Lipinski drug-likeness, reaction enumeration, 2D/3D coordinates. For simpler API use datamol; use RDKit for fine-grained sanitization, custom fingerprints, or SMARTS/reaction control.
Distills a logical relational-database design methodology into rules an agent applies while designing or reviewing a schema. Covers the design sequence (mission → tables → fields → keys → relationships → business rules → views → integrity review), one-subject-per-table decomposition, atomic single-valued fields, candidate/primary/foreign keys, relationship types with deletion rules and participation, the four levels of data integrity, database-vs-application business rules, validation tables, views for derived data, and the flat-file / spreadsheet / RDBMS-driven antipatterns to avoid. The structure is logical and RDBMS-agnostic, and normalized by construction. Use when designing a new relational schema, reviewing or refactoring an existing one, resolving redundant or repeating data, choosing keys, modeling relationships, or deciding where a constraint belongs.
Use when reviewing/judging a finished data-model document (the persistence/ domain model — entities, keys, relationships, indexes, normalization, lifecycle) to decide if an engineer can build the schema + query it correctly — an acceptance gate, not authoring. Judges it against a single-sourced 9-condition integrity + queryability bar: entities typed + keyed (stored shape, not the api-spec DTO); relationships carry cardinality + a referential rule (on-delete / embed-vs-reference); every index traces to an access pattern; normalization + paradigm choice + tradeoffs; lifecycle + migration; diagram ⇄ tables in sync; privacy/scale; one-directional vs the api-spec + consistent with the shipped schema; amend delta-scoped. Paradigm-aware (relational/document/graph/wide-column/key-value — never a relational reflex). Emits exactly `VERDICT: approve|revise` — approves a model meeting the bar, revises on a named gap. Not for authoring, the api-spec/feature-spec, or generic design docs (use design-review).
Statistical visualization on matplotlib with native pandas support. Auto aggregation, CIs, grouping for distributions (histplot, kdeplot), categorical (boxplot, violinplot), relational (scatterplot, lineplot), regression (regplot, lmplot), matrix (heatmap, clustermap), grids (pairplot, FacetGrid). Use for quick statistical summaries; matplotlib for fine control; plotly for interactive HTML.
Analyze stocks and cryptocurrencies using Yahoo Finance data. Supports portfolio management, watchlists with alerts, dividend analysis, 8-dimension stock scoring, viral trend detection (Hot Scanner), and rumor/early signal detection. Use for stock analysis, portfolio tracking, earnings reactions, crypto monitoring, trending stocks, or finding rumors before they hit mainstream.
Analyze stocks and cryptocurrencies using Yahoo Finance data. Supports portfolio management, watchlists with alerts, dividend analysis, 8-dimension stock scoring, viral trend detection (Hot Scanner), and rumor/early signal detection. Use for stock analysis, portfolio tracking, earnings reactions, crypto monitoring, trending stocks, or finding rumors before they hit mainstream.
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
Query STRING REST API for PPIs (59M proteins, 20B interactions, 5000+ species). Retrieve networks, run GO/KEGG enrichment, find partners, test PPI significance, visualize networks, analyze homology. For chemical interactions use chembl-database-bioactivity; pathways use kegg-database.
Implement Syncfusion Windows Forms Pivot Chart control for visualizing multidimensional data with interactive drill-down capabilities. Use this when working with pivot charts, pivot data visualization, drill-down charts, hierarchical chart data, or business intelligence visualizations. Supports 11+ chart types (Line, Spline, Column, Area, Stacking), data binding with IEnumerable/DataTable, drill up/down operations, pivot table field list, grouping bar, legend customization, Excel export, zooming, scrolling, and touch support.
Best practices for building, training, evaluating, and deploying neural networks with TensorFlow and Keras. Use when writing tf.data input pipelines, defining or training a Keras model, configuring callbacks for checkpointing and early stopping, evaluating a trained model, or exporting a model for serving.
Read your TikTok niche and audience from real data. Scan a hashtag for top videos with plays, likes, and comments, see which hooks and sounds are working, pull a profile's videos (yours or a competitor's), and read the commenters on a video (TikTok hides likers, so commenters are the signal). Powered by Apify, no login. Triggers on "what is trending on TikTok", "analyze this hashtag", "competitor videos", "read my comments", "which sounds are working". Not for writing a script (use tt-hook-scripter).
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like “the xlsx in my downloads”) — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Dung khi can viet bao cao marketing cho nguoi khac doc — weekly 1 trang cho CEO, monthly day du, hoac strategy report theo quy. Cau truc TL;DR, so vs target, insight, quyet dinh can duyet, viec tiep theo. Kich hoat khi user nhac 'bao cao marketing', 'report thang', 'weekly update cho sep', 'tong ket quy', 'lam bao cao cho khach hang', 'bao cao content'. Khong dung cho — chan doan tai sao so xau thi dung skill 03-danh-gia-hieu-suat; bien data tho thanh insight thi dung skill 13-phan-tich-du-lieu.
Use when the user needs to work backward from a revenue goal to the numbers that produce it — required leads, max CPA and CPL, funnel conversion thresholds, and the budget that has to be spent, with three scenarios and currency handling for US, EU, SEA, and LATAM. Trigger on 'reverse KPI', 'how much budget do I need', 'what CPA can I afford', 'work backward from revenue', 'how many leads to hit target', 'break-even ROAS'. Also use when the user names a revenue target and asks whether it is realistic. Not for — splitting an existing budget across channels and months, see `61-budget-planning-global`; a full paid media plan, see `54-media-plan-global`; the period plan, see `00-marketing-plan-global`.
Use when raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and prescriptive layers, cuts by channel, campaign, creative, audience, and time, cohorts, and a decision log. Trigger on 'analyze this data', 'read these numbers for me', 'what does this export say', 'pull insight from GA4', 'cohort analysis', 'here is the spreadsheet'. Also use when the user pastes a table and asks what it means. Not for — diagnosing ad root cause, see `03-performance-eval-global`; writing the report a stakeholder reads, see `07-marketing-report-global`; auditing account setup, see `21-ads-audit-global`.
Dung khi can test co ky luat de biet phuong an nao thang that — chon dung mot bien de test, viet gia thuyet, tinh sample size, setup tracking, doc y nghia thong ke va ghi test log cho ads, landing page, email hoac gia. Kich hoat khi user nhac 'A/B test', 'split test', 'test creative', 'test copy', 'test landing', 'chay bao lau moi du ket luan', 'ban nao thang', 'test xong khong biet doc ket qua', 'ket qua co dang tin khong'. Khong dung cho — so dang xau va can chan doan nguyen nhan thi dung skill 03-danh-gia-hieu-suat; nhan ngan sach cho creative da thang thi dung skill 55-scaling-ads; ra soat toan bo trang khong ra don thi dung skill 68-cro-audit-trang.
This skill MUST be called for optimizing 1C queries: temp tables, JOIN vs subqueries, virtual table parameters, DCS, indexes. SHOULD also be called for slow queries and performance diagnostics. Do NOT use for basic query syntax — use 1c-help-mcp.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.