200건 · 관련도순
Generate production-quality Excalidraw architecture diagrams from natural language. Visual structure maps to conceptual structure — fan-out for one-to-many, timelines for sequences, convergence for aggregation. Includes self-validation via Playwright render review.
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Audit cross-platform attribution, conversion definitions, reporting windows, GA4, AdServices and AdAttributionKit, MMPs, browser and server events, offline conversions, and platform reconciliation. Use for attribution audit, attribution models, conversion windows, requests to add or total Meta and Google conversions, incompatible reporting-window aggregation, GA4 attribution, MMP review, AppsFlyer, Adjust, Branch, Singular, or cross-platform discrepancies.
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Use when working with Aws — mANDATORY parallel execution patterns (30x speedup), CloudWatch statistics syntax, Cost Explorer aggregation, output token limits, and common pitfalls.
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MANDATORY parallel execution patterns (30x speedup), monitor metrics aggregation, --output tsv formatting, and common pitfalls
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Use when working with Aws Cloudwatch Logs — aWS CloudWatch Logs group management, Logs Insights query execution, metric filter analysis, retention policy review, and subscription filter management. Covers log group inventory, storage cost optimization, query patterns, and cross-account log aggregation.
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Use when working with Mongodb — mongoDB database analysis, performance tuning, query optimization, and health monitoring. Covers collection analysis, index recommendations, aggregation pipelines, replica set health, Atlas cluster management, and slow query investigation.
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MongoDB with Mongoose — schemas, models, aggregation pipelines, migrations, and Atlas connections. Use when designing collections, writing queries, or integrating MongoDB into Node.js/Next.js apps.
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Full lifecycle expense reimbursement: single invoice intake and staging, batch aggregation to generate reimbursement drafts, approval workflow, ledger and dashboard. Trigger when user mentions reimbursement, expenses, invoices, travel reimbursement, approval workflow, or needs to organize / verify / approve expense records. Also applies when user needs to enter invoices, view reimbursement dashboards, ledgers, or compliance checks.
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Expert patterns for Plaid API integration including Link token flows, transactions sync, identity verification, Auth for ACH, balance checks, webhook handling, and fintech compliance best practices. Use when: plaid, bank account linking, bank connection, ach, account aggregation.
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bu skill covers Dağıt:ing AWS Security Hub as a centralized cloud security posture management platform that aggregates Bul:ings from GuardDuty, Denetle:or, Macie, and third-party tools. It details enabling security standards like CIS AWS Foundations Benchmark, configuring automated remediation, and building executive dashboards for compliance tracking across multi-account AWS organizations.
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Use when the user wants all 16 personality types (16-minds plugin) to vote on a proposal — yes/no/abstain — to see stakeholder distribution, detect hidden objections, or simulate organizational consensus. Triggers on Japanese phrases like 「全人格で投票」「16人格に賛否を聞いて」「ステークホルダーシミュ」「これ通せそう？」, English equivalents like "vote with all 16 minds" / "stakeholder simulation", or `--vote` flag on 16-minds commands. Different from /minds (which is for opinion divergence) — this is for binary judgment aggregation.
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Unified chat inbox (Busabase App-in-Skill) aggregating WhatsApp, Discord, Slack, and Telegram messages into one place with a reply queue. Use when the user invokes $kelly-messenger or /kelly-messenger, wants a unified inbox, chat aggregation, to read WhatsApp/Discord/Slack/Telegram messages in one place, review unanswered conversations, queue or approve replies, run connector sync, or manage the outgoing reply queue.
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Busabase-backed family-office App-in-Skill that consolidates multi-entity investment holdings into one read-only dashboard. Use when the user invokes $kelly-family-office or /kelly-family-office, wants a consolidated family office view, multi-entity aggregation across an individual, trust, company, fund, or foundation, total AUM in a base currency, asset allocation by asset class, by entity/member, or by custodian/institution, multi-currency FX consolidation, CSV import of holdings, unrealized P/L, or read-only portfolio monitoring. It reads Busabase only and NEVER moves money or trades.
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Busabase-backed, read-only App-in-Skill portfolio dashboard that aggregates Webull brokerage holdings (accounts, positions, cash/margin, market value, unrealized P/L, day change, buying power). Use when the user invokes $kelly-invest-webull or /kelly-invest-webull, wants to review their Webull portfolio, holdings, positions, accounts (cash/margin), asset allocation, market value, unrealized P/L, day change, cash, or buying power. Read-only aggregation only — it never places, modifies, or cancels orders and never moves money.
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Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Use when ingesting commercial or open-source CTI feeds, evaluating feed quality, normalizing data into STIX 2.1 format, or enriching existing IOCs with campaign attribution. Activates for requests involving ThreatConnect, Recorded Future, Mandiant Advantage, MISP, AlienVault OTX, or automated feed aggregation pipelines.
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Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.
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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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TanStack Table (React Table) v9 ヘッドレステーブル (@tanstack/react-table) の API リファレンス。 ark-ui / chakra-ui のスタイル付き Table とは別。 useTable, createTableHook, tableFeatures, createColumnHelper, flexRender, createCoreRowModel, table.Subscribe, atoms, React Compiler, custom features, sorting, column / global / fuzzy filtering, faceting, pagination, row selection, expanding, grouping / aggregation, column / row の ordering・pinning・sizing・visibility, virtualization。 v8 legacy (useReactTable / getCoreRowModel) と v9 移行。
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Design and coordinate multi-agent systems where specialized agents work together to solve complex problems. Covers agent communication, task delegation, workflow orchestration, and result aggregation. Use when building coordinated agent teams, complex workflows, or systems requiring specialized expertise across domains.
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Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.
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Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.
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Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.
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Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.