Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Guidance for processing financial documents (invoices, receipts, statements) with OCR and text extraction. This skill should be used when tasks involve extracting data from financial PDFs or images, generating summaries (CSV/JSON), or moving/organizing processed documents. Emphasizes data safety practices to prevent catastrophic data loss.
Huawei Cloud DDS (Document Database Service) and DCS (Distributed Cache Service) unified management skill. Provides 16 operations across five capability domains: (1) Query — list/get DDS/DCS instances, DCS node info, DCS custom templates; (2) Analysis — analyze DDS deployment architecture and DCS security posture; (3) DDS Management — create instance, add read-only/sharding nodes, create backup, delete instance; (4) DCS Management — create instance, create custom template, delete instance, restart instance; (5) Multi-mode execution using hcloud CLI for available operations and huaweicloudsdk Python SDK as fallback. Auth via AK/SK environment variables or hcloud profile. All mutating operations require explicit user confirmation. Triggers include: "DDS","DCS","文档数据库","分布式缓存","数据库实例","缓存实例","MongoDB","Redis","Memcached","DDS实例","DCS实例","数据库运维","缓存运维","文档数据库查询","分布式缓存查询","DDS管理","DCS管理","缓存节点","自定义模板","document database","distributed cache","DDS instances","DCS instances","MongoDB cluster","Redis cache","dds-dcs".
Tonghuashun iFinD (51ifind.com) financial data queries. Supports 18 API endpoints for real-time quotes, historical quotes, financial indicators, macroeconomic data, and more across A-shares, funds, bonds, futures, and indices. Chinese financial data query skill powered by THS iFinD (51ifind.com) API. Covers real-time quotes, historical data, financial indicators, macro economics, fund valuation, and more across 18 API endpoints for A-shares, funds, bonds, futures, and indices.
Analyze Claude Code session logs and surface productivity improvements. Extracts thinking blocks, tool usage stats, error patterns, debug trajectories - then generates friendly, actionable recommendations. Triggers on: introspect, session logs, trajectory, analyze sessions, what went wrong, tool usage, thinking blocks, session history, my reasoning, past sessions, what did I do, how can I improve.
ioredis v5 reference for Node.js Redis client — connection setup, RedisOptions, pipelines, transactions, Pub/Sub, Lua scripting, Cluster, and Sentinel. Use when: (1) creating or configuring Redis connections (standalone, cluster, sentinel), (2) writing Redis commands with ioredis (get/set, pipelines, multi/exec), (3) setting up Pub/Sub or Streams, (4) configuring retryStrategy, TLS, or auto-pipelining, (5) working with Redis Cluster options (scaleReads, NAT mapping), or (6) debugging ioredis connection issues. Important: use named import `import { Redis } from 'ioredis'` for correct TypeScript types with NodeNext.
Fetch webpage text with a normal HTTP request first, then automatically fall back to jina.ai proxy when direct access fails or returns login/JS-blocked content. Use when extracting content from X (Twitter) or other pages that are hard to read directly.
Light research mainline step 5 · research plan and experiment design: break the idea cleared by idea-critique and the data feasibility into a truly executable, paper-ready, reproducible question/estimand, experiment matrix and preregistration package. When to use: the idea has passed review and needs to land / experiments, ablations, comparisons, sensitivity, generalization, robustness need designing / writing a research plan PROJECT_PLAN / locking the primary outcome, exclusion/stopping rules or preregistration / planning sample size, seeds and statistical power / computing an experiment compute budget / reproducing an existing paper / worrying that the baseline is watered down or the hypothesis cannot be falsified. Trigger words: research plan / experiment design / experiment matrix / hypothesis / control baseline / ablation / fair comparison / falsifiable / statistical power / how many seeds / reproduction / reproducibility / research plan / experiment design / reproducible. Core discipline: unfair comparison (watered-down baseline) / non-falsifiability = critical veto (spec §4.2, STAGE_GATES[5]=[fair_baseline,falsifiable]); an ablation that doesn't isolate / underpowered = warn; power is back-computed at the planning stage, not a post-hoc result check; lint is heuristic and bounded, and the final verdict on fairness/falsifiability still needs human/domain judgement — never claim "proved the comparison is absolutely fair".
Light research mainline step 7 · result analysis: don't describe good or bad, explain "why", bind every conclusion tightly to a claim + evidence strength, and guard against p-hacking. When to use: after experiments finish and results need interpreting / asking "what do these numbers mean" / performing significance tests + effect size + confidence intervals + multiple-comparison correction / worrying about p-hacking (uncorrected multiple comparisons, selective reporting, HARKing) / assigning evidence strength to each claim so writing can calibrate wording / judging whether results support hypotheses and are reproducible. Trigger words: result analysis / interpret data / what do these results mean / significance / p value / effect size / Cohen's d / confidence interval CI / multiple comparisons / BH-FDR / Bonferroni / correction / p-hacking / selective reporting / garden of forking paths / HARKing / evidence strength / claim-evidence binding / SHAP / ablation analysis / slice analysis / paired test / result analysis. Core discipline: statistical errors / p-hacking = critical (spec §4.2, STAGE_GATES[7]=[stat_validity,evidence_strength]); overinterpretation / missing effect size = warn; for significance look at q not p, non-significant results may only be reported as "no significant difference found", and wording strength must match evidence strength; statistical checks are bounded — never claim "proved the method works / causality holds". This skill initiates the 7→5 (hypothesis unsupported) / 7→6 (not reproducible) loop-backs.
Design or modernize a software system from an evidence-backed current-state inventory through quality attributes, architecture options, API and schema contracts, migration/rollback plans, ADRs, and verification. Use for greenfield or existing monoliths, modular monoliths, services, system/API/database design, schema migration review, data-flow reliability, tenant/PII controls, or architecture evolution. Existing systems stay read-only until the user selects an option and authorizes exact mutations. Unknown facts stay UNKNOWN. This is an off-DAG engineering skill: do not emit findings or invent STAGE_GATES, ROUTES, stages, or back-edges.
Loads a fitted Meridian model and generates standard visualization reports: Model Results Summary. Use when the user wants to load a saved model and view results (model performance, fit, health). Don't use for running budget optimization (use meridian-budget-optimization) or building/fitting the model (use meridian-model-building).
Analyze the structure of a 1C metadata object from an XML dump — реквизиты, tabular sections, forms, движения, types. Use to study object structures (instead of reading XML-файлы directly) and as a preparatory step when writing queries and code that works with objects
This skill should be used when the user asks to evaluate, validate, compare, explain, or debug model performance. PROACTIVELY activate for: (1) metrics selection for classification, regression, ranking, NLP, CV, recommender, forecasting, and generative AI, (2) train/validation/test splits, cross-validation, grouped or time-series validation, (3) confusion matrices, ROC/PR curves, calibration, thresholds, error analysis, (4) ablation studies, statistical significance, confidence intervals, bootstrap tests, (5) bias, fairness, explainability, robustness, leakage detection. Provides: rigorous evaluation methodology and production-readiness checks.
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
Build reusable dimension / lookup tables for a star schema — country/region, timezone, currency, date, plan/product, and other descriptive attributes — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model dimension tables, lookup tables, a star schema, conformed dimensions, or wants to enrich events/revenue/usage with country, region, timezone, plan, or currency attributes without repeating JOINs. Covers sourcing the dimension data (upload, warehouse source, or derive from events), shaping it into an aliased one-row-per-entity view (optionally materialized on a slow schedule since dimensions change rarely), and attaching it to facts via a saved or person join so its columns read as native fields. Key rule: for currency use the built-in convertCurrency() instead of a hand-rolled rate table. Read modeling-warehouse-foundations first; dimensions here are reused by the revenue, conversion, activation, and product-usage modeling skills.
Financial tracking, revenue analytics, expense management, and pricing optimization. Integrates with Stripe for revenue data, tracks unit economics, and generates financial reports. Use when the user needs revenue tracking, financial reports, pricing optimization, expense management, or says 'revenue', 'MRR', 'ARR', 'finances', 'pricing', 'expenses', 'profit', 'ROAS', or 'unit economics'.
Always invoke for access to facilities or opportunities by walking, driving, cycling, or public transport, even for a conceptual question with no routing terms or data yet. Covers hospital and service access, transit/GTFS, routes, isochrones, OD matrices, closest facility, 2SFCA, walkability, coverage, and equity. Invoke when Euclidean buffers proxy for network access. Use movement-trajectory for observed tracks and MCDA for suitability without network costs.
Parses a single financial statement PDF (checking, savings, credit card, brokerage, 401k, HSA, mortgage, tax form) and emits a normalized JSON record with institution, account mask, statement period, opening/closing balances, line-item transactions or holdings, and a confidence score. Use when extracting structured data from a bank, brokerage, retirement, or HSA PDF statement, when ingesting a drop of household finance documents, or when user mentions parsing a statement, extracting transactions from a PDF, or normalizing statement data.
Design practical pilots and evaluation protocols for industry R&D, AI, ML, analytics, LLM, banking, fintech, or enterprise solutions. Use when the user has a proposed method or solution direction and needs to validate it with offline tests, backtests, human evaluation, A/B tests, baselines, metrics, ablations, guardrails, and a go/no-go decision. Especially use after publication-grounded-solution-design or industry-problem-framing.
Manage Power BI table partitions, named expressions (M/Power Query data sources), and calendar table configuration using pbi-cli. Invoke this skill whenever the user mentions "partitions", "data sources", "M expressions", "Power Query", "incremental refresh", "named expressions", "connection parameters", or wants to configure how tables load data. For broader modeling tasks (measures, relationships, hierarchies), see power-bi-modeling instead.
Reddit product-research scouting tool. Mines cross-border e-commerce product opportunities from real Reddit user discussions and generates in-depth analysis reports (multiple concrete pain points + quoted user statements + product specification suggestions + data-backed opportunity scores). No Reddit API Key needed; accesses via Chrome cookies. Three modes: broad/targeted/weekly report; one-click automatic push of results to Feishu Docs + Bitable. Triggers when the user asks to "run a product research analysis", "find product opportunities", "Reddit product research", "product research report", or "weekly report".
Populate person or group properties from a data warehouse table or materialized view, so warehouse columns become properties usable in feature flags, cohorts, and insights. Use when the user wants to "sync my Postgres columns to person properties", "map a warehouse table to people", "replace our daily identify cron", "update person properties from the warehouse", or asks for reverse ETL into PostHog. Covers picking the table and the identifier column, proposing a whole-table column mapping in one call, excluding identity and reserved properties, checking collisions with properties that already exist, and running the first backfill.
Executing SQL, managing Snowflake objects, deploying applications, and orchestrating data pipelines using the Snowflake CLI (snow) command. Use this skill when you need to run SQL scripts, deploy Streamlit apps, execute Snowpark procedures, manage stages, automate Snowflake operations from CI/CD pipelines, or work with variables and templating.
Craft and optimize SQL for any warehouse dialect — Snowflake, BigQuery, Databricks, Redshift, PostgreSQL. Covers window functions, CTEs, aggregations, joins, funnel queries, cohort retention, deduplication, dialect translation, slow query tuning, and cross-platform syntax differences.
SQL Server and Azure SQL Database with T-SQL, indexes, stored procedures, and performance optimization patterns. USE WHEN: user mentions "SQL Server", "T-SQL", "MSSQL", "Azure SQL", "SQL Server indexes", "stored procedures", "SQL Server performance" DO NOT USE FOR: PostgreSQL - use `postgresql`, MySQL - use `mysql`, MongoDB - use `mongodb`
Runs and reasons about the automated AI health check that suggests path-cleaning rules for web-analytics teams. Use when asked to generate path-cleaning suggestions for a team or cohort, to run the suggestion check, to review/apply AI-suggested rules, to inspect path_cleaning_suggestions health issues, or to extend the suggestion pipeline. Covers the suggest_path_cleaning_rules management command, the path_cleaning_suggestions health check, the cohort gating (precompute teams), and how suggestions are validated against real paths before storage. For hand-authoring or applying rules directly, use managing-path-cleaning-rules instead.
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.