Data & Analytics

dawiddutoit Bundle

Guides for implementing real-time data aggregation and transformation pipelines using ClickHouse materialized views. Use when building real-time dashboards, streaming analytics, metrics aggregation, event processing, or continuous data transformation. Covers incremental aggregation, State/Merge functions, chaining views, schema evolution, and troubleshooting streaming pipelines.

dawiddutoit Skill

Performs comprehensive security audits of Java code against OWASP Top 10 and best practices. Use when auditing security, checking for vulnerabilities, analyzing SQL injection risks, preventing XSS attacks, reviewing authentication/authorization, detecting sensitive data exposure, checking dependency vulnerabilities, ensuring OWASP compliance, or hardening Java applications. Works with Java web applications, REST APIs, Spring applications, and any Java codebase.

pproenca Bundle

Remediate security findings by producing minimal, surgical code patches. Triggers on 'patch security findings', 'fix vulnerabilities', 'remediate findings', 'threat patch', or when the user provides a findings.json (from threat-model), a Codex security findings CSV, a THREAT-MODEL.md, or individual vulnerability descriptions and wants them fixed. Also trigger when reviewing code flagged by a security scanner and the user wants actionable fixes rather than just reports.

pproenca Bundle

Drizzle ORM targeting SQLite (better-sqlite3, libsql/Turso, bun:sqlite, Cloudflare D1, expo-sqlite, op-sqlite). Covers schema definition (column modes, primary keys, foreign keys, indexes), drizzle-kit migrations (generate vs push, renames, custom SQL), the query builder (selects, upserts, returning, EXPLAIN), the relational query builder (relations(), `with`, partial columns), transactions and `db.batch()`, prepared statements with `sql.placeholder()`, connection pragmas (WAL, foreign_keys, busy_timeout), and Drizzle type inference (`$inferSelect`, `$inferInsert`, `$type<>`, drizzle-zod). Use when writing, reviewing, or refactoring Drizzle code for SQLite. Trigger even if the user doesn't say "performance" — schema/migration choices made now are expensive to reverse later, and SQLite-specific traps (single-writer model, no native booleans/dates, ALTER TABLE limits, FK pragma off by default) catch teams who reach for Drizzle without reading the SQLite docs.

pproenca Bundle

Processing, transforming, or moving datasets that may exceed RAM on a single low-compute box — covers memory discipline (streaming, generators, dtype shrinkage), I/O access patterns (sequential vs random, mmap, async), data formats (Parquet vs CSV vs JSON, predicate pushdown), chunking & batching, spill-to-disk (external merge sort, DuckDB/Polars), pipelining (bounded queues, backpressure, checkpointing), codec selection (zstd/lz4/gzip), concurrency for I/O-bound workloads (asyncio, threads, prefetch), and observability (iowait vs CPU%, rows/sec, py-spy/strace). Trigger on "process a large file", "stream this", "out-of-core", "OOM kill", "this is slow", or code with `pd.read_csv` of multi-GB files, `requests.get(...).content` on big bodies, `BytesIO` on unbounded inputs, per-row INSERTs, sequential `requests.get` loops, falling `tqdm` rates — even if I/O or memory isn't mentioned. Complement to computer-science-algorithms.

pproenca Bundle

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.

educlopez Skill

Dense dashboard / admin / Bloomberg / Retool / data-heavy internal tools. Locked knobs: CRAFT=7, MOTION=3, DENSITY=9. IBM Plex + mono numbers, semantic palette, 4/8px grid, sparklines, tabular-nums. Trigger on: dashboard, admin panel, data-dense, analytics, Bloomberg-like, Retool-like.

oldwinter Bundle

Run Lev8 multi-case browser pressure tests from CSV or tabular case files. Use when Codex needs to launch multiple fresh Chrome tabs for Lev8/ci-app.lev8.com, submit one query per case, handle Search Plan / Run search plan follow-up loops, verify right-side live result tables, and produce a concise E2E pressure-test summary.

yonatangross Bundle

Queries local analytics across OrchestKit projects for agent usage, skill frequency, hook timing, team activity, session replay, cost estimation, and model delegation trends. Privacy-safe with hashed project IDs. Supports time-range filtering and comparative analysis. Use when reviewing performance, estimating costs, or understanding usage patterns.

yonatangross Bundle

Database design and migration patterns for Alembic migrations, schema design (SQL/NoSQL), and database versioning. Use when creating migrations, designing schemas, normalizing data, managing database versions, or handling schema drift.

yonatangross Bundle

A/B test evaluation, cohort retention analysis, funnel metrics, and experiment-driven product decisions. Use when analyzing experiments, measuring feature adoption, diagnosing conversion drop-offs, or evaluating statistical significance of product changes.

terrylica Skill

Consume FXView tick data from Parquet files. Schema, file layout, DuckDB queries, Python/Rust examples.

terrylica Bundle

backtesting.py configuration for SQL oracle validation and range bar pattern backtesting. Use when running backtesting.py against.

terrylica Skill

Memory-safe Python script patterns for long-running processes under systemd MemoryMax constraints. Covers allocator purge (mimalloc/glibc malloc_trim), HTTP response lifecycle, DataFrame cleanup, thread-local connection reuse, and periodic GC cadence. Battle-tested through 5 OOM optimization cycles on production GPU workstations. Use this skill proactively whenever writing or reviewing Python scripts that: run under systemd with MemoryMax, process data in loops (downloads, ETL, backfill), use ThreadPoolExecutor, or make repeated HTTP requests. Also use when diagnosing OOM kills, RSS creep, or fd exhaustion in Python services. TRIGGERS - memory optimization, OOM prevention, RSS reduction, malloc_trim, systemd MemoryMax, memory leak, allocator purge, memory-safe script, RSS creep, fd exhaustion, SIGKILL status 9, MemoryHigh, glibc arena, mimalloc purge, requests memory leak, ThreadPoolExecutor cleanup.

terrylica Bundle

Investigate why ODB bars are oversized, have zero duration, or show anomalous price ranges by forensically analyzing Parquet.

aladicf Skill

Design or improve data visualizations, charts, and data presentation interfaces. Use when the user asks to add charts, build dashboards, visualize data, choose chart types, make data accessible, or present metrics and analytics.

lyndonkl Skill

Computes substacker's rolling 4-week baseline for open rate, click rate, views-per-send, and weekly subscriber delta using corpus/stats/ archived CSVs. Produces per-metric z-scores of the current week against the baseline and flags cold-start windows where fewer than 4 prior weeks exist. Use after ingest-substack-csv each Monday. Trigger keywords — baseline, rolling median, z-score, cold start, per-metric comparison.

lyndonkl Skill

Normalizes a single inbox file of any supported format (plain markdown, Claude.ai JSON export, Claude Code JSONL session, Readwise markdown/CSV highlight, transcript with timestamps or speaker labels, link capture) into a clean markdown body plus partial frontmatter (id, title, source block, word_count). Handles format-specific failure modes — JSON content-block arrays, timestamp stripping, per-highlight chunking, URL-vs-commentary separation. Use when ingesting any inbox item for the substacker Librarian. Trigger keywords — normalize, convert, parse, transcript, export, JSON, JSONL, highlight, CSV.

lyndonkl Skill

Scans the substacker published corpus for clusters of posts that could become a product — a course, a book, a cohort, or a consulting offer. Produces at most 2 candidates with evidence + audience signal, or an honest "not yet" verdict if nothing qualifies. Typically fires once the writer has 30+ posts. Trigger keywords — product hiding, course from essays, book from essays, corpus to product, product scan.

lyndonkl Skill

Uses WebFetch to pull publicly visible subscriber count and per-post public view count from substacker's Substack archive page and individual post URLs. Supplements the CSV when subscriber-count field is stale (>24h old) or when a post has public shares not yet reflected. Rate-limited to ≤10 fetches per invocation. Use when CSV subscribers-end field may have drifted or when external-share attribution needs a public signal. Trigger keywords — public stats, Substack public page, subscriber count check, post views supplement, WebFetch.

lyndonkl Bundle

Analyzes profitability per customer, product, or transaction to determine business model viability and scalability. Covers CAC, LTV, contribution margin, cohort analysis, and growth-readiness assessment. Use when evaluating business model viability, validating startup metrics (CAC, LTV, payback period), making pricing decisions, comparing business models, or when user mentions unit economics, CAC/LTV ratio, contribution margin, customer profitability, or break-even analysis.

lyndonkl Bundle

Creates rigorous, validated models of entities, relationships, and constraints for database schemas (SQL, NoSQL, graph), knowledge graphs, ontologies, API data models, and taxonomies. Covers relational, document, graph, event/time-series, and dimensional schema patterns with lifecycle modeling, soft deletes, polymorphic associations, and hierarchies. Use when user mentions "schema", "data model", "entities", "relationships", "ontology", "knowledge graph", or when data structures need formalization.

posthog-skills Bundle

Add PostHog product analytics events to track user behavior. Use after implementing new features or reviewing PRs to ensure meaningful user actions are captured. Also handles initial PostHog SDK setup if not yet installed.

posthog-skills Bundle

Add PostHog LLM analytics to trace AI model usage. Use after implementing LLM features or reviewing PRs to ensure all generations are captured with token counts, latency, and costs. Also handles initial PostHog SDK setup if not yet installed.

posthog-skills Bundle

PostHog LLM analytics for all supported providers

posthog-skills Bundle

Help existing PostHog customers improve their PostHog instance. Triggers on "help [customer] improve their PostHog setup", "audit [company]'s PostHog instance", "create tracking plan for [company]", "design data schema for [customer]", or requests to improve analytics coverage, fix instrumentation gaps, expand PostHog usage, or build better insights for customers already using PostHog. Use when working with a customer who already has PostHog installed.

ovachiever Bundle

Build type-safe D1 databases with Drizzle ORM for Cloudflare Workers. Includes schema definition, migrations with Drizzle Kit, relations, and D1 batch API patterns. Prevents 12 errors including SQL BEGIN failures. Use when: defining D1 schemas, managing migrations, writing type-safe queries, implementing relations or prepared statements, using batch API for transactions, or troubleshooting D1_ERROR, BEGIN TRANSACTION, foreign keys, migration apply, or schema inference errors. Prevents 12 documented issues: D1 transaction errors (SQL BEGIN not supported), foreign key constraint failures during migrations, module import errors with Wrangler, D1 binding not found, migration apply failures, schema TypeScript inference errors, prepared statement caching issues, transaction rollback patterns, TypeScript strict mode errors, drizzle.config.ts not found, remote vs local database confusion, and wrangler.toml vs wrangler.jsonc mixing. Keywords: drizzle orm, drizzle d1, type-safe sql, drizzle schema, drizzle migrations,

ovachiever Bundle

Build secure WordPress plugins with core patterns for hooks, database interactions, Settings API, custom post types, REST API, and AJAX. Covers three architecture patterns (Simple, OOP, PSR-4) and the Security Trinity. Use when creating plugins, implementing nonces/sanitization/escaping, working with $wpdb prepared statements, or troubleshooting SQL injection, XSS, CSRF vulnerabilities, or plugin activation errors.

thelobbi Skill

Database design, SQL queries, migrations, and optimization. Activate for PostgreSQL, MySQL, SQLite, schema design, queries, indexes, and data modeling.

thelobbi Skill

Understanding and optimizing Claude Code session performance — token tracking, bottleneck identification, caching behavior, and cost estimation

thelobbi Skill

Home Assistant sensor configuration and analytics patterns.

mims-harvard Bundle

Microscopy and quantitative imaging analysis — colony morphometry, fluorescence intensity quantification, cell-count statistics, dose-response curves, and ANOVA/Dunnett on image-derived measurements. Uses pandas/numpy/scipy/scikit-image. Use for analyzing tabular outputs from CellProfiler/ImageJ, image-derived measurement statistics, and image-based assay quantification.

mims-harvard Bundle

Compare GWAS studies, perform meta-analyses across cohorts, and assess signal replication. Uses GWAS Catalog metadata, study-level statistics, and cross-cohort comparison. Use for evaluating GWAS reproducibility for a trait, meta-analysis sample size and effect-size aggregation, and detecting study heterogeneity (population, design, ancestry).

mims-harvard Bundle

Answer biomedical FACTUAL / recall / multiple-choice questions by querying ToolUniverse database tools instead of answering from memory. Triggers on any 'which gene/drug/variant/disease/pathway/miRNA/TF...' lookup, any question phrased 'according to <database>' (DisGeNet, OMIM, MSigDB, miRDB, GTRD, MGI, Ensembl, ClinVar, ChEMBL, OpenTargets, Reactome, GtoPdb, UniProt...), and multiple-choice biology/medicine knowledge questions where one option must be verified against an authoritative source. NOT for analyzing user-supplied data files (CSV/VCF/h5ad → use the data-analysis router) and NOT for open-ended literature synthesis. Use whenever a single correct answer exists in a public biomedical database and could be looked up rather than guessed.

mims-harvard Skill

End-to-end observational epidemiology analysis — from research question (PECO Population/Exposure/Comparator/Outcome) to publication-ready statistical report. Covers cohort/case-control/cross-sectional design, regression with confounders, propensity scoring, sensitivity analysis. Writes Python code for every step. Use for epidemiology study analysis, NHANES/UK-Biobank-style analyses.

mims-harvard Skill

Population genetics using the 1000 Genomes Project (IGSR) — superpopulation/population search, sample metadata, variant frequencies across AFR/AMR/EAS/EUR/SAS, ancestry-specific analyses. Use for ancestry comparison, population-aware allele frequency lookups, and 1000-Genomes-cohort-specific analyses (distinct from gnomAD which has different sample composition).