2 399 data analysis skills from 443 authors. They crunch numbers, pivot tables and turn both into something readable. Half of them fit into 1 951 tokens or less — that is what one costs your context window when the agent loads it. 545 ship runnable scripts rather than instructions alone. 31 of them cannot work without an MCP server, most often rube. We also found 355 copies of these same skills sitting in other people's repositories — counted once here, not 355 times.
2 399 unique 443 authors 1 328 updated this month 230 from vendors
Use when user requests diagrams, flowcharts, architecture charts, or visualizations. Also use proactively when explaining systems with 3+ components, complex data flows, or relationships that benefit from visual representation. Generates .excalidraw files and exports to PNG/SVG via Kroki API or locally using excalidraw-brute-export-cli.
Add new Prometheus metrics to the Cloudflare exporter. Use when adding metrics, implementing new GraphQL/REST queries, or extending metric coverage. Covers the full workflow from schema discovery through client implementation and README updates.
CUMCM 国赛、MCM/ICM 美赛与电工杯数学建模竞赛的端到端协作工作流。Use when a user explicitly works on one of these modeling contests or asks to run/review a modeling-competition paper from problem selection through modeling, solving, robustness, writing, compliance, and final submission review. Provides 10 stages, persistent decision state, competition-specific rules/templates, deterministic scoring helpers, numbered decisions, and Codex/Claude Code handoff. Do not trigger for generic model selection, ordinary data analysis, or non-competition paper review.
Plugin shim for the mathmodel-skill competition workflow. Use when Codex invokes this plugin for a CUMCM, MCM/ICM, or Diangong Cup modeling-contest task, including problem selection, modeling, robustness, contest-paper writing, compliance, or final submission review. Do not use for generic data analysis or non-competition paper review.
UI/UX design-system intelligence - 84 styles, 192 palettes, 74 font pairings, 25 charts, 99 UX guidelines across 22 stacks (searchable dataset + CLI). Internal genjutsu module: loaded by /genjutsu:cast and /genjutsu:paint, not invoked directly.
Turn a website, PWA, dashboard, or marketplace into an iOS app with App Store strategy, screenshots, metadata, and release gates.
Suede-owned measurement discipline for tracking plans, event and conversion instrumentation, UTMs, attribution, and verification of what actually fires. Use when setting up, auditing, or repairing analytics across web, product, paid, and lifecycle surfaces. NOT FOR: experiment design or significance decisions (use suede-ab-testing), campaign optimization (use suede-ads), or revenue-process architecture (use suede-revops).
Design, build, and optimize dashboards for RIA practice management with AUM tracking, revenue analytics, and KPI frameworks. Use when the user asks about tracking firm-level metrics, monitoring advisor productivity, measuring organic growth rate, analyzing client retention and attrition, building executive or branch manager views, setting up exception alerts for NIGO and operational items, benchmarking against industry peers, or designing role-based dashboard access. Also trigger when users mention 'how is the practice doing', 'revenue per advisor', 'client attrition', 'net new assets', 'effective fee rate', 'practice benchmarking', 'AUM growth decomposition', or 'advisor capacity'.
Design, generate, and deliver client performance reports across all channels, covering quarterly reports, tax reporting, portal integration, and compliance review. Use when the user asks about building or redesigning report templates, choosing what to include in quarterly or annual client reports, transitioning from print to digital delivery, integrating a client portal, presenting net-of-fee performance with benchmarks, managing report production timelines, or handling e-delivery consent. Also trigger when users mention 'client reporting', 'quarterly performance report', 'report customization', 'tax lot report', '1099 supplement', 'report disclaimers', 'UHNW reporting', or 'report QA process'.
Guide regulatory filing mechanics and deadlines for investment advisers, broker-dealers, and large traders — which forms to file, where, and by when. Use when the user asks about Form PF filing thresholds, 13F institutional holdings reports, 13H large trader filings, Form ADV amendment filing timing (including the annual updating amendment filed via IARD), FOCUS report preparation, blue sheet requests, CAT reporting infrastructure, or FINRA short interest and TRACE reporting. Also trigger when users mention 'filing deadline calendar', 'do we need to file Form PF', 'crossed the $100M 13F threshold', 'CAT clock synchronization', 'how to respond to a blue sheet request', or 'FOCUS report errors'. (For what the ADV brochure must contain and when it must be delivered to clients, use client-disclosures.)
Compute and compare investment return metrics including TWR, MWR (dollar-weighted IRR on portfolio cash flows), CAGR, and annualized returns. Use when the user asks about portfolio performance calculation, comparing manager returns, linking sub-period returns, understanding why different return methods give different numbers, converting returns across time periods, or computing the IRR of an investor's own contributions and withdrawals. Also trigger when users mention 'how much did I make', 'annual return', 'compound growth', 'dollar-weighted vs time-weighted', 'what was my rate of return', 'geometric vs arithmetic mean', 'log returns', or ask about the effect of cash flows on reported returns. For project or loan IRR, NPV, and generic 'solve for the rate' problems, use time-value-of-money instead.
Apply statistical methods to financial data including descriptive statistics, covariance estimation, regression, hypothesis testing, and resampling. Use when the user asks about return distributions, correlation between assets, building a covariance matrix, running a CAPM regression, testing whether alpha is significant, checking if returns are normal, or estimating confidence intervals. Also trigger when users mention 'volatility', 'how correlated are these', 'fat tails', 'skewness', 'R-squared', 'beta of a fund', 'bootstrap a Sharpe ratio', 'shrinkage estimator', 'Ledoit-Wolf', or ask why their optimizer produces unstable weights.
Analyze alternative investments including hedge funds, private equity, and venture capital. Use when the user asks about hedge fund strategies (long/short, macro, event-driven), PE or VC performance metrics (IRR, TVPI, DPI), fee structures ('2-and-20', carry, hurdle rates), the J-curve effect, illiquidity premiums, lock-up periods, or hedge fund replication. Also trigger when users mention 'managed futures', 'CTA', 'fund of funds', 'vintage year', 'capital calls', 'distributions', 'carried interest', or ask how to evaluate an alternative investment manager.
Quantify realized risk from historical data using volatility estimators, drawdown analysis, and downside risk metrics. Use when the user asks about historical volatility, maximum drawdown, drawdown duration, historical VaR, downside deviation, semi-variance, or tracking error. Also trigger when users mention 'how risky has this been', 'worst decline', 'Parkinson estimator', 'Yang-Zhang', 'peak-to-trough loss', 'recovery time', 'annualized volatility', or ask how to measure past investment risk.
Evaluate investment performance on a risk-adjusted basis using industry-standard ratios and capture analysis. Use when the user asks about Sharpe ratio, Sortino ratio, Information Ratio, Treynor ratio, Calmar ratio, Omega ratio, or upside/downside capture. Also trigger when users mention 'risk-adjusted returns', 'return per unit of risk', 'M-squared', 'is this fund worth the volatility', 'how to compare two managers', 'capture ratio', or ask which investment performed better after accounting for risk.
Generate clear, accurate performance reports for investment portfolios with benchmarks, attribution, and risk dashboards. Use when the user asks about portfolio performance reports, return summaries, benchmark comparison, risk dashboards, goal progress tracking, or GIPS-compliant reporting. Also trigger when users mention 'quarterly report', 'how did my portfolio do', 'time-weighted vs money-weighted return', 'annualized returns', 'net-of-fee performance', 'rolling Sharpe', or ask how to present investment results to clients.
Smart Money analytics on OKX: leaderboard traders, position tracking, trade records, closed-position history, aggregated consensus signals, and signal history. Use this skill when the user asks about 聪明钱, smart money, 牛人榜, leaderboard, top traders, 交易员排行, trader ranking, trader positions, trader PnL, 交易员持仓, 交易员收益, 历史平仓, closed positions, realized PnL track record, trade history, 成交记录, smart money signal, 聪明钱信号, long/short ratio, 多空比, capital flow, 资金流向, position conviction, 仓位强度, entry price distribution, smart money overview, 聪明钱总览, signal history, 信号历史, trader search, 搜索交易员, who is trading BTC, 谁在交易BTC, recommend traders, 推荐交易员, best traders, top performers.
> Use when building Script Reports, Query Reports, dashboard charts, or Number Cards in ERPNext. Prevents empty report output from wrong column definitions, broken filters, and unoptimized SQL in large datasets. Covers Report Builder, Script Report (Python + JS), Query Report, Report filters, dashboard Chart DocType, Number Card, report permissions.
> Use when implementing scheduled tasks and background jobs in Frappe v14/v15/v16. Covers hooks.py scheduler_events, frappe.enqueue, queue selection, job deduplication, testing with bench execute/scheduler, monitoring via Scheduled Job Log and RQ Dashboard, error handling, long-running job patterns, email digest, data cleanup, and report processing, queue selection, job deduplication, scheduler implementation, run task automatically, background process, scheduled task not running, async task.
> Use when creating or customizing Workspace pages in Frappe v14-v16. Covers Workspace DocType structure, shortcuts, number cards, dashboard charts, custom HTML blocks, JSON content format, shipping workspaces with custom apps, and role-based access control. Prevents common mistakes with content/child-table desync and missing fixtures. workspace builder, module, fixtures, sidebar.
> Use when building Query Reports, Script Reports, or configuring Report Builder, including chart data integration. Prevents report errors from wrong column definitions, missing permissions, and incorrect data formatting. Covers Query Report (SQL-based), Script Report (Python-based), Report Builder, report columns definition, filters, chart_data, report permissions, prepared_report.
> creating or editing dashboards, adding tiles, changing layouts, updating data sources, adjusting themes, resizing tiles, or any dashboard-building task. This skill defines the defineTile() API, board.json schema, theming conventions, size guidelines, and resize adaptation rules for the live-preview tile grid environment. Consult before your first edit in a new conversation.
> (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
> Quality control metrics and filtering thresholds for protein design. (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage filtering pipelines, (5) Computing PyRosetta interface metrics (dG, SC, dSASA), (6) Checking biophysical properties (instability, GRAVY, pI), (7) Ranking designs with composite scoring. This skill provides research-backed thresholds from binder design competitions and published benchmarks.
Use to turn a dataset into a verifiable multimedia blog (a data story / data-driven article / interactive dashboard from a dataset). Orchestrator for the Data Journalist Agent (Data2Story): a 7-team newsroom (14 agents) running detective → scout → analyst → imagineer → editor → copywriter → designer → interaction → hero → cinematographer → programmer → auditor → critic → inspector in sequence. Trigger when the user hands over a dataset (CSV/JSON/folder/path) and wants a published story, blog post, or interactive report built from it. Creates a versioned project folder per run.
Read editor.md, editor.json, analyst.json, and designer.json. Resolve chart data from analyst data_tables. Build the final index.html with data-* traceability attributes. Pure implementation — no editorial or visual decisions, no raw data access.
Exhaustively profile a dataset and list ALL possible analyses — distributions, correlations, rankings, trends, group comparisons, anomalies. Reads detective.json for context. Outputs analyst.json with ana_xx IDs and chart-ready data_tables.
Read editor.md, editor.json, analyst.json, and designer.json. Resolve chart data from analyst data_tables. Build the final index.html with data-* traceability attributes. Pure implementation — no editorial or visual decisions, no raw data access.
A shared reference library for editorial-grade data-visualization craft — Vega-Lite-first with a D3 fallback for charts Vega-Lite can't express. Read by the Designer at chart selection (intent → ranked chart type), the Programmer at implementation (editorial Vega-Lite recipes, annotation layers, axis/label de-clutter, encoding craft), and the Auditor/Critic for chart-quality review. It encodes the FT Visual Vocabulary intent taxonomy, the Cleveland–McGill channel-accuracy ordering, the BBC bbplot de-clutter ruleset, and colorblind-safe encoding rules. Not a pipeline stage — a craft source, like frontend-design.
Perform structured Ziwei Dou Shu (紫微斗数 / Zi Wei Dou Shu) system design, chart interpretation, and implementation planning. Use when the user asks to build a Ziwei skill or engine, define palaces/stars/four-transformations/limits, interpret an existing chart JSON/table, or produce deterministic-vs-heuristic output. Do not use for birth-data-only personal readings unless verified chart facts or a deterministic chart engine are available.
Compute and interpret a Western tropical natal chart (birth chart / natal chart / 本命盘 / 星盘) from birth datetime and location. Use for requests about planets, signs, houses, aspects, or outputs grouped into personality, career, and relationships. Skip Vedic or sidereal unless explicitly requested, tarot, horary, Chinese astrology, daily horoscopes, or astronomy-only questions unrelated to a natal chart.
中国国家统计局公开数据查询技能，当用户想查询经济、CPI、GDP、人口、房价指数等数据时触发。
A skill to build and manage Home Assistant configurations. Use when using the Home Assistant Builder (`hab`) CLI to inspect, create, update, delete, operate, or troubleshoot Home Assistant resources; when a user mentions hab, Home Assistant CLI automation, Lovelace/dashboard edits, helpers, automations, scripts, backups, ESPHome, or Home Assistant operations from a terminal.
> Use when the user has a known, already-observed anomaly in their data — a metric spike or drop, an outlier, an unexpected number — and wants its root cause diagnosed, not guessed. Forms a slate of candidate causes, tests each against the data, and eliminates the ones the data refutes, narrowing the live candidates until exactly one survives refutation and passes a positive confirming test. The result is an investigation log with the confirmed root cause and the evidence that ruled out the alternatives. Not for open-ended discovery over a dataset with no specific anomaly in hand (that is data-analysis), and not for checking an external claim against sources (that is claim-verify) — this is reactive diagnosis of one anomaly you already know about.
> Use when the user wants an iterative, self-checking exploratory analysis of a dataset — surfacing findings that are each verified by re-running the computation, not asserted. Proposes one specific hypothesis at a time, writes and runs analysis code to test it, and records the finding only if the numbers support it at a meaningful effect size; loops until no new verified finding appears or the budget is hit. The result is a findings report where every claim is backed by a reproducible number. Not for diagnosing a single known anomaly or pipeline failure, and not for verifying an external claim against sources (that is a claim-verification task) — this is open-ended discovery over a bound dataset.
> Use when the user has a results draft or a set of data-backed claims and wants each one adversarially verified against the underlying dataset before publishing — a pre-publication red-team of the findings. Extracts the discrete checkable claims from the draft, reproduces each claim's number against the data, stress-tests it against the threats most likely to kill it (outliers, confounds, Simpson's reversals, tiny subgroups, alternative specifications), and marks it verified, fragile, or refuted; fragile and refuted claims are revised — hedged, scoped, or retracted — until every claim is verified or appropriately qualified. The result is a draft where every surviving claim has been reproduced and survived a stress test. Not for open-ended discovery of new findings over a dataset (that is a data-analysis task), and not for diagnosing a single known anomaly or pipeline failure — this is a gate over an existing draft.
> Use when the user has a messy tabular data dump (CSV/TSV/parquet/Excel/JSON) and wants it iteratively cleaned to an inferred data contract — a checklist of deterministic pass/fail checks, not a quality score. A single agent profiles the table, synthesizes a per-column contract compiled into binary checks (types, nulls, duplicates, inconsistent categories, format/range violations, outliers), then applies one targeted transform at a time, keeping it only if it reduces its target check's violations with no regression and no guardrail breach. Stops deterministically when every check passes, every remaining check is an unfixable residual, or a budget is hit; emits a replayable pipeline and an auditable ledger. Not for open-ended analysis of an already-clean dataset, diagnosing one known anomaly, or verifying a claim against sources — those are analytical loops; this rewrites the data to a contract.
Generate auditor-ready compliance evidence from Datadog Audit Trail for SOC 2 and PCI DSS. Maps framework controls to specific query patterns and produces formatted output.
Log management - search, archives, metrics, and cost control.
> Generate a BYOD ownership preferences reference table for a customer. Walks through preference types, generates CSV, and provides upload instructions (UI, API, cloud storage, or Terraform). Use when asked about BYOD setup, preferences reference table, k9_ownership_preferences, or ownership customization.
> Systematic dataset profiling protocol for empirical research. Use this skill when the user has a new dataset and wants to understand it before analysis — including unit of observation, variable definitions, panel structure, data quality, and descriptive statistics. Trigger on phrases like "explore this data", "profile this dataset", "what's in this data", "understand this dataset", "describe this data", "what are the variables", "check the panel structure", or any request to examine a dataset before running regressions.
Create marketing visualizations from data. Use when: creating charts for reports; visualizing campaign performance; generating dashboards; presenting data insights; exporting charts for presentations
Generate PDF/HTML reports from templates and data. Use when: creating client reports; generating weekly summaries; producing marketing performance reports; automating recurring reports
Build a phased reputation recovery plan after a crisis — from damage assessment through stakeholder communication, trust-rebuilding actions, and sentiment tracking. Use when: rebuilding brand reputation post-crisis, designing a public apology and accountability strategy, creating stakeholder-specific recovery communications, tracking sentiment recovery metrics, planning a PR damage-control roadmap, or recovering from a data breach, product recall, or public backlash.
Automate QBR preparation with account summaries, success metrics, challenges, and strategic recommendations
Design and execute customer onboarding playbooks with milestones, success metrics, and automated touchpoints
Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
Construisez un système de distribution complet (posting, DM delivery, retargeting, tracking) qui transforme vos hooks en pipeline prévisible, basé sur la méthodologie A4 du Marketing Swarm de Lasse Flagstad. Use when: **Lancer une machine de contenu LinkedIn/X** - Cadence, review flow, posting engine; **Automatiser les DMs après engagement** - Trigger → message → tag → booking; **Installer le retargeting** - Email + Meta/IG pour ne jamais perdre un lead; **Créer un dashboard simple** - Les 6 ...