データ・AIのスキル

omics-plotting: publication-style figure authoring for omics / bioinformatics results with matplotlib / seaborn. Read this before writing any plotting or figure code in any omics analysis — RNA-seq, proteomics, single-cell, variant, or database results — not only when a plot is explicitly requested: whenever an analysis will produce a figure, load this first and follow its recipes. Covers volcano, MA, expression / correlation heatmap, GSEA bar / dot plot, box / violin / bar / ridgeline, PCA / UMAP / t-SNE scatter, Kaplan–Meier, Manhattan / QQ / forest. Supplies a shared journal-ready style and copy-paste recipes so every figure looks like one consistent system. To combine several plots into ONE multi-panel composite figure, use the sibling `multipanel` skill.

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Assemble multiple plots into ONE publication-ready multi-panel journal figure (e.g. Figure 1 with panels A, B, C). Use whenever the user asks to combine, compose, or lay out several plots as a single composite figure — newly plotted from data or from already-rendered panels the user supplies (PNG/PDF). Ask the user to pick one of two approaches: (1) redraw every panel into one unified figure using independent, tightly packed `subfigures` (each sized to its own labels, so axes need NOT align), consistent style, correctly placed panel letters, and per-panel legends/colorbars; (2) composite already-rendered PNG/PDF panels onto a mosaic canvas and add panel letters (image compositing, not plotting). Both export vector PDF + high-DPI PNG. For a SINGLE plot from a data table, use the sibling `omics-plotting` skill instead.

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3Dmol.js WebGL molecular visualization emitted as self-contained HTML. Render structures (PDB/SDF/XYZ/MOL2/cube) with stick, sphere, cartoon, line, and surface styles; animate trajectories with a frame-delay (interval, ms) control; and animate vibrational normal modes via vibrate() from per-atom dx/dy/dz displacements or from precomputed frames. Output standalone HTML that loads 3Dmol from a CDN, with optional play/pause and speed controls. Use for transition-state imaginary-mode animations, MD or reaction-path playback, docking poses, and orbital/density isosurfaces. For static 2D chemical structure drawings use rdkit-chemdraw-cdxml; for 2D statistical plots use matplotlib or plotly.

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Parse/write FCS (Flow Cytometry) files v2.0-3.1. Events as NumPy, channel metadata, multi-dataset files, CSV/FCS export. Use FlowKit for gating/compensation.

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Python statistical modeling: regression (OLS, WLS, GLM), discrete (Logit, Poisson, NegBin), time series (ARIMA, SARIMAX, VAR), with rigorous inference, diagnostics, and hypothesis tests. Use scikit-learn for ML; statistical-analysis for test choice.

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Time-to-event modeling with scikit-survival: Cox PH (elastic net), Random Survival Forests, Boosting, SVMs for censored data. C-index, Brier, time-dependent AUC; Kaplan-Meier, Nelson-Aalen, competing risks. Pipeline/GridSearchCV compatible. Use statsmodels for frequentist, pymc for Bayesian, lifelines for parametric.

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API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.

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Guide for annotating statistical significance (p-value asterisks) on comparison plots. Covers standard notation (ns, *, **, ***, ****), matplotlib bracket+asterisk implementation, and use with seaborn box/violin/bar plots. Use when preparing publication-ready figures with significance markers.

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Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.

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Guided statistical analysis: test choice, assumption checks, effect sizes, power, APA reporting. Pick tests, verify assumptions, or format results for publication. Covers frequentist (t-test, ANOVA, chi-square, regression, correlation, survival, count, reliability) and Bayesian. Use statsmodels or pymc-bayesian-modeling to fit.

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Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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Operational knowledge for the daily-brief digest pipeline (this project). RSS/API fetchers, pluggable LLM enrichment (default claude CLI on Max; also anthropic/openai/deepseek/minimax API), trading section, HTML rendering, cross-platform scheduler integration (Windows Task Scheduler / macOS launchd / Linux cron). Load when the user asks about running daily / regenerating sections / debugging a failed run / adding or disabling sources / LLM quota / scheduler / why a tab shows wrong data / why a source failed / switching LLM backend. Always prefer the documented npm commands over re-implementing logic. Diagnose by reading logs/daily-*.log first, then logs/llm-calls.jsonl for LLM-side issues.

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Add or revise source-level JSDoc for Shift APIs. Use this skill before writing or editing documentation comments for exported classes, methods, constructors, domain data structures, render frames, reactive state, or any API where caller intent, side effects, lifetime, ownership, or nullability are easy to misunderstand.

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When a founder needs to qualify inbound leads, define their ICP, build a lead scoring model, set MQL criteria, or route prospects through pipeline stages. Activate when the user mentions lead scoring, ICP, MQL, SQL, lead qualification, inbound leads, or pipeline design.

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When the user needs to draft, review, or update a privacy policy for their product, or needs to understand data privacy obligations across jurisdictions.

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When the user wants to prepare a due diligence data room for fundraising, or when an investor has requested additional materials after a pitch. Also activates for "data room", "due diligence", "DD checklist", or "what documents do investors need?".

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Pulls financial statement numbers for US public companies straight from SEC EDGAR's free official XBRL APIs (companyfacts, companyconcept, frames, submissions) into a cited table. Every value carries its accession number, form, fiscal period, filed date, XBRL tag and a link to the filing, and the tool handles fiscal-year ends, quarterly vs YTD vs TTM, derived Q4, restatements and units. Use this whenever the user wants 10-K numbers or 10-Q numbers, says "pull financials for", asks for public company revenue, net income, EPS, cash, debt or capex, wants comps or a competitor side-by-side, a credit check, prospect research, or model inputs, or mentions SEC filings or EDGAR, even if they never say XBRL. Free and primary-source, no paid data terminal needed. Do not use it for private companies, stock prices or valuation multiples.

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Generate 30+ LinkedIn posts that attract your target prospects. Creates industry insights, thought leadership, engagement prompts, and comment strategies. Use when building personal brand to attract inbound leads through social selling.

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Predicts client renewal likelihood based on health score signals. Analyzes engagement, support, adoption, satisfaction, billing, stakeholder, and usage data to calculate a weighted Health Score (0-100), classify renewal risk, and generate a prioritized action plan with early warning signals and recommended save plays.

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Construct statistical arguments for MVP/awards. Narrative framing, comparison to past winners, advanced metrics, counter-arguments.

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TAM/SAM/SOM calculator with deep market research. Produces comprehensive market-sizing.md with top-down and bottom-up estimates, methodology, data sources, assumptions, sensitivity ranges, growth projections, competitive landscape, and Mermaid visualizations. Use when user needs market size estimates, addressable market analysis, go-to-market sizing, investor-ready market analysis, or business plan market validation.

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Deep-scan any tools you connect (CRM, email, support, reviews, analytics, billing, database) to produce a data-grounded Ideal Customer Profile and a reusable persona library. Read-only by default. Use when you need to define or refresh your ICP, build buyer personas from real data instead of guesses, or generate the persona inputs that the customer-panel-of-experts and prospect-panel-simulator skills consume.

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Input your best customers and find 100+ companies that match the profile. Uses firmographic data, tech stack, growth signals, and similarity scoring to identify ideal prospects. Use when building target account lists or expanding to new markets.

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