Data & Analytics

ai-analyst-lab Skill

Run one analytics task through several fresh trials to measure what repeats and what varies. Use for reliability, repeatability, variance, or repeated-run requests. This measures stability, not correctness.

ai-analyst-lab Skill

Operating rules for every data analysis. Apply for ANY data-analysis intent: "analyze", "investigate", "why did X change", "compare", "report on", "dashboard", "metrics", "funnel", "retention", "revenue", "conversion", "trend", "segment", "forecast", "how are we doing", "dig into", "break down", or any question about data, a metric, a CSV, or a table. Sets the method and routes to the other skills; load before any analytical question.

ai-analyst-lab Skill

Independently validate the current analysis with a second model (OpenAI Codex). Codex re-derives the same answer from the same data — blind to Claude's SQL and numbers — and the skill reports AGREE / DISAGREE / PARTIAL per finding. Use when the user types "/codex-review", or says "validate with codex", "codex review", "second opinion from codex", "have the other model check this", "independently verify this analysis", "does codex agree", "cross-check this with gpt/codex", or wants a different model to confirm a result before acting on it. This is multi-model validation: a real independent re-analysis, not a critique of Claude's work. If the Codex plugin or CLI isn't installed, this skill detects that and walks the user through setup first.

ai-analyst-lab Skill

Guided wizard to connect a new dataset to the AI Analyst system. Use this skill whenever the user wants to add a new data source, connect a database, set up data access, or configure a new dataset for analysis. This skill handles the full connection workflow: choosing connection type (CSV, DuckDB, PostgreSQL, Snowflake, BigQuery, Databricks, Redshift, SQL Server, MySQL), collecting credentials, validating connectivity, profiling schema, and setting up the knowledge brain. Trigger this skill when users say things like "/connect-data", "connect my database", "add a new dataset", "set up my data", "I have a database I want to analyze", "can you connect to my Postgres/BigQuery/Snowflake", "I need to add CSV files", "how do I get my data into this system", or "connect to my warehouse". Also trigger after first-run welcome when users need to set up their first dataset, or after /switch-dataset when the target dataset doesn't exist yet. This is the primary entry point for all new data connections — always offer this

stanfish06 Skill

Reactive Python notebooks stored as pure .py files — cells form a dependency DAG, so changing one cell automatically reruns its dependents (or marks them stale under the lazy runtime) and hidden state cannot exist. Covers the notebook file format, marimo edit/run/export CLI, mo.ui interactive elements, SQL cells, running notebooks as apps or scripts, and sandboxed notebooks with PEP 723 inline dependencies. Use when creating or editing marimo notebooks, building interactive data apps or dashboards in pure Python, converting Jupyter notebooks, or when reproducibility/git-friendliness rules out .ipynb.

stanfish06 Skill

The workhorse library for in-memory tabular data in Python. Use for loading, cleaning, reshaping, joining, grouping, and analyzing labeled row/column data. Trigger terms include "pandas", "dataframe", "series", "csv", "parquet", "tabular data", "groupby", "pivot", "merge", "data wrangling", "data cleaning". Covers pandas 2.x and the 3.0 changes (Copy-on-Write, default string dtype, PyArrow-backed dtypes).

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High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.

ai-analyst-lab Skill

Advanced: runs the same question under two configurations and diffs the results. Ask one analytics question with a piece of context and without it, and measure what changed. Trigger on "/context-compare", "run it with and without <the definition/context>", "does adding <X> change the answer", "is this context worth it".

ai-analyst-lab Skill

First-time Snowflake setup wizard for the NATIVE ConnectionManager path (the connection the analyst actually queries through, with auto-logged provenance). Prompts for every connection field, stores the approved credential in .env, registers the dataset, and VERIFIES the session is live on the warehouse before declaring success. Use when the user says "set up snowflake", "connect to snowflake", "configure the warehouse", or is routed here from /connect-data. For day-to-day remote querying after setup, use connect-snowflake. An optional Snowflake MCP server (for interactive ad-hoc queries) is covered in the appendix.

ai-analyst-lab Skill

Query the live/remote Snowflake warehouse instead of the local practice copy. Use when the user says "connect to snowflake", "use the live data", "go remote", "query the warehouse", or asks "is this hitting snowflake or duckdb?". Assumes credentials already exist (first-time setup is /setup-snowflake); opts into remote via ConnectionManager and verifies the connection type before any query runs.

ai-analyst-lab Skill

Configure AI Analyst to understand your development environment and codebase structure. Use this skill whenever developers need to integrate AI Analyst into their workflow, or when questions arise about database configuration, SQL conventions, schema naming patterns, dbt integration, warehouse connections, or team-specific data practices. Trigger this skill when users mention: "set up for our team", "configure for our codebase", "integrate with our database", "tell it about our SQL conventions", "set up dbt integration", "configure schema prefix", "set up warehouse connection", "integrate with our data stack", "configure for development", "set up team conventions", or any request about configuring AI Analyst to work with an existing development environment, data warehouse, or analytics infrastructure. This is ONLY for development teams integrating AI Analyst into their codebase. Most users (PMs, execs, data scientists doing ad-hoc analysis) should use `/setup` instead. Only invoke this when the user explicitl

ai-analyst-lab Skill

Apply whenever you generate any chart, graph, or data visualization — from SQL results, in the chart-maker agent, in decks, or on "make a chart / visualize / plot / dashboard" requests. Enforces Storytelling With Data: gray first, one focus color, action titles, direct labels, no pies, using the helpers in helpers/viz/chart_helpers.py.

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Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

twells89 Bundle

Author, retrieve, or modify a Sigma data model spec (the JSON/YAML semantic-layer definition with sources, columns, metrics, relationships, filters, controls, folder groupings, and column-level security) by calling the Sigma REST API directly. Use when the user wants to build a new data model from existing warehouse tables, add metrics or relationships to an existing model, change a model's source, edit columns, or round-trip a data model spec through code. **Out of scope: converting from another BI tool's format (dbt, LookML, Tableau, Power BI, Alteryx, etc.).** Those conversions are handled by the Sigma data-model converter (browser tool + MCP) — point users there when they paste source-format input. Requires an SIGMA_API_TOKEN — obtain via the sigma-api skill first.

twells89 Bundle

Scan Sigma workbooks for custom SQL elements, dedupe across workbooks, build or reuse Sigma data models, then repoint the workbooks via the v3alpha `:swapSources` endpoint. Use when you want to find ad-hoc SQL in workbooks and promote it to one reusable data model per unique query.

deadmade Bundle

Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.

stanfish06 Skill

Fast Python I/O for BigWig (continuous genome signal) and BigBed (interval annotation) files via libBigWig. Use for random-access signal queries at specific genomic coordinates (bw.values, bw.stats), computing per-region summary statistics (mean/max/coverage) over a BED file of regions, writing custom BigWig tracks from numpy arrays, and loading ChIP-seq/ATAC-seq/RNA-seq/methylation coverage tracks (e.g. produced by deeptools bamCoverage) into pandas/numpy for downstream analysis or ML feature extraction. Complements deeptools (which generates BigWig files) and chip-seq/atac-seq workflows.

stanfish06 Skill

MinHash/FracMinHash sketching for alignment-free comparison of genomes and metagenomes. Use for fast all-vs-all genome similarity and ANI estimation across thousands of genomes without alignment, taxonomic classification of metagenomes against GTDB/NCBI reference databases (sourmash gather/tax), and sequencing-cohort QC (contamination or duplicate detection). Complements upstream assembly/QC pipelines (snakemake-workflow-engine, nextflow) and feeds downstream phylogenetics; distinct from alignment-based tools like BLAST or mash-style exact-num MinHash by supporting scaled (FracMinHash) sketches that compare well across very different dataset sizes.

stanfish06 Bundle

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

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Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.

stanfish06 Skill

The standard Python SQL toolkit and ORM — 2.x style only. Covers declarative models with DeclarativeBase/Mapped/mapped_column, engine and session lifecycle, CRUD with select() and Session.scalars(), relationships and eager loading (selectinload vs joinedload), transactions, async engines, and Alembic migrations. Use when defining database models in Python, querying relational databases (PostgreSQL, SQLite, MySQL) from application code, wiring a database into FastAPI, fixing N+1 or DetachedInstanceError problems, or migrating legacy Query-API code to 2.x.

stanfish06 Skill

Create, analyze, proofread, and modify Office documents (.docx, .xlsx, .pptx) using the officecli CLI tool. Use when the user wants to create, inspect, check formatting, find issues, add charts, or modify Office documents.

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Use when the user asks about Mixpanel product analytics, event data, funnel analysis, retention curves, cohort analysis, segmentation queries, user behavior, conversion rates, churn, DAU/MAU, ARPU, revenue metrics, feature adoption, A/B test results, user paths, flow analysis, or any request to query, explore, visualize, or analyze Mixpanel data using Python. Also use when the user asks to read, write, or manage Mixpanel "business context" — the markdown documentation that grounds AI assistants in an organization's structure and goals.

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主动渗透测试工具链。覆盖信息收集、端口扫描、漏洞扫描、Web 渗透、SQL 注入、目录爆破、密码破解等场景。 通过 MCP server（pentestMCP / mcp-security-hub）将 20+ 安全工具暴露给 AI agent。 触发关键词：渗透测试、端口扫描、Nmap、漏洞扫描、Nuclei、SQL 注入、SQLMap、目录爆破、FFUF、密码破解、Hashcat、信息收集、子域名、Web 渗透、ZAP、Burp。

stanfish06 Skill

Python genomic interval arithmetic with BEDTools, complementing pysam, polars, and query for downstream tables. Use when intersecting, merging, subtracting, shuffling, finding closest features, computing coverage, or converting BED, GFF, GTF, VCF, BAM, and pandas DataFrames into interval operations.

stanfish06 Bundle

Expert-thinking profile for Data Scientist (computational / analytics, ML & experimentation): Reasons from CRISP-DM business estimands, leakage-safe sklearn Pipelines and nested CV, SQL/warehouse semantic metrics, A/B power and SRM/AA guardrails, causal DAG covariate discipline, and Model Cards/Datasheets while treating train-test leakage, Simpson's paradox, peeking, and PSI>0.25 drift as first-class failure...

stanfish06 Skill

Use this skill any time a .xlsx file is involved -- as input, output, or both. This includes: creating spreadsheets, financial models, dashboards, or trackers; reading, parsing, or extracting data from any .xlsx file; editing, modifying, or updating existing workbooks; working with formulas, charts, pivot tables, or templates; importing CSV/TSV data into Excel format. Trigger whenever the user mentions 'spreadsheet', 'workbook', 'Excel', 'financial model', 'tracker', 'dashboard', or references a .xlsx/.csv filename.

stanfish06 Bundle

PyTorch Geometric (PyG) for graph neural networks — node/link/graph classification, message passing (GCN, GAT, GraphSAGE, GIN), heterogeneous graphs, neighbor sampling, and custom datasets. Use when working with torch_geometric, not for general NetworkX analytics or non-graph PyTorch models.

stanfish06 Bundle

VDR Index Setup skill for Datasite deal rooms. Use this skill whenever a user wants to create, propose, design, or set up a Virtual Data Room (VDR) index or folder structure for a deal. Triggers include: "set up a data room", "create a VDR index", "build a deal room structure", "prepare the index", "set up the fileroom", "I need a data room for [deal/company]", or any request to organise or structure documents for due diligence. Also triggers when a user wants to replicate an existing deal room structure or import an index from a spreadsheet or reference deal. Use when the user is starting a new deal room or wants to customise the folder hierarchy before documents are uploaded. Do not use to audit or review an existing data room — use gap-analysis, document-quality-check, or risk-analysis-audit for that.

stanfish06 Bundle

Bulk Q&A Answers skill for Datasite deal rooms. Use this skill whenever a sell-side deal team wants to answer multiple buyer questions at once, generate AI draft responses from VDR content, produce a Q&A tracker spreadsheet, or build a Q&A management dashboard. Triggers include: "answer the Q&A", "draft responses to buyer questions", "process the question list", "generate Q&A tracker", "answer all questions", "bulk answer", "Q&A management dashboard", "respond to diligence questions", or any request to systematically work through a list of buyer questions using data room content as the source. Use whenever a buyer has submitted questions and the deal team wants AI-assisted drafting. Do not use for individual one-off questions outside a structured Q&A process. Do not draft answers from general knowledge — all responses must come from the data room.

stanfish06 Bundle

Expert-thinking profile for Health Economist (computational / HEOR / health technology assessment): Reasons from QALY/ICER and NMB opportunity-cost framing, NICE reference case and WTP bands, cohort Markov/PSM models with PSA (CEAC/CEAF), ISPOR transferability and DCE conjoint checklists, CHEERS 2022 and trial-based RCT-CEA reporting.

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Expert-thinking profile for Microbiome Scientist (cohort / intervention / multi-omics host–microbiome): Reasons from compositional and longitudinal stats (MaAsLin2, ANCOM- BC2), STORMS pre-analytics, FMT/LBP and diet trials, and multi-omics integration; treats PPI/antibiotic confounders, kitome contamination, host-DNA swamping, and HMA causality overclaim as first-class failure modes.

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DataFrame schema validation using pandera. Schema definitions, column checks, and decorator-based validation.

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Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

stanfish06 Bundle

Expert-thinking profile for Actuarial Scientist (life / health / P&C / valuation / capital & accounting): Reasons from mortality tables (qx, period/cohort, select/ultimate) and Chain-Ladder/Mack reserving through GLM/GAM frequency–severity and Tweedie pricing, limited-fluctuation and Bühlhmann-Straub credibility, Solvency II SCR standard formula, and IFRS 17 CSM/RA while treating triangle truncation, overfitting, and tail...

stanfish06 Bundle

Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.