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Found 875 Skills
Investment thesis tracker — maintains and updates the investment thesis for portfolio holdings and watchlist names by continuously tracking key data points (revenue growth, gross margin, user metrics), catalyst progress (new products, expansion, policy), and risk milestones, then renders a verdict on whether the thesis still holds. Triggers: "投资逻辑", "Thesis追踪", "投资假设", "逻辑验证", "跟踪持仓", "买入逻辑", "持仓理由", "投資邏輯", "Thesis追蹤", "投資假設", "邏輯驗證", "追蹤持倉", "investment thesis", "thesis tracking", "investment hypothesis", "thesis validation", "thesis check", "investment rationale", "position monitoring", "thesis intact", "is my thesis still valid".
Guides product management for human data platforms—annotation and labeling products, workforce workflows, task design, quality systems (gold sets, adjudication, inter-annotator agreement), customer ML-team project delivery, contributor experience, and privacy-safe handling of human-generated training data. Use when prioritizing roadmap for labeling/RLHF/eval data platforms, writing PRDs for annotation or QA features, defining success metrics for throughput and quality, scoping enterprise customer workflows, or balancing cost-quality-speed tradeoffs—not for hands-on model training (data-scientist), warehouse/analytics pipelines (data-warehouse-engineer), generic BRD workshops without product lens (business-analyst), AI solution architecture for copilots (applied-ai-architect-commercial-enterprise), or control implementation for audits (compliance-engineer). UX flows: product-designer. Eval harnesses: prompt-engineer-agent-prompts-evals. Pricing/packaging for platform: product-management-monetization.
Guides property and casualty (P&C) insurance—commercial and personal lines, major LOBs (property, GL, workers comp, commercial auto, umbrella, specialty), underwriting and risk selection, policy triggers (occurrence vs claims-made), limits and exclusions, claims (FNOL, reserving, litigation), reinsurance and catastrophe, distribution (agents, brokers, MGAs), metrics (loss ratio, combined ratio, cat load), and state DOI/rate filing overview—not legal advice. Use for P&C insurance, property and casualty, commercial lines, workers comp, general liability, combined ratio, loss ratio, underwriting, claims-made, occurrence policy, reinsurance, catastrophe, MGA, rate filing, or FNOL—not actuarial modeling (actuary), life/health depth, legal interpretation (commercial-counsel), or GRC controls without insurance context (compliance-engineer).
Workload-aware architecture design for Apache Doris. MUST USE when designing data architectures, choosing between data models, planning ingestion strategies, sizing clusters, or translating business requirements into Apache Doris system designs. Complements doris-best-practices with decision frameworks and sizing-first workflow. Use when user describes a workload involving: IoT, sensor data, telemetry, real-time analytics, dashboard, log analysis, log search, CDC sync, time-series, device monitoring, point query service, ad-hoc analytics, lakehouse federation, ETL/ELT pipeline, report analytics, clickstream, user behavior, observability, metrics, fleet tracking, or any OLAP workload requiring table design from scratch. Also triggers on prompts like: "design a table for...", "how should I store...", "build an architecture for...", "we have X devices sending data every Y seconds", "recommend a cluster size for...", "what data model should I use for...", "we need to ingest X GB/day", "migrate from MySQL/PostgreSQL to Apache Doris". Also use for legacy analytics/search/serving stack consolidation prompts even when Apache Doris is not named explicitly, including replacing or migrating from Impala, Kudu, Elasticsearch/ES, Greenplum, Presto, HBase, Hive, Hadoop, Redis, or Lambda-style multi-engine data platforms.
DORA (DevOps Research and Assessment) Core Model for measuring and improving software delivery performance. Use this skill to assess team performance tier, identify capability gaps, and connect delivery metrics to product release strategy.
Analyze production Agentforce agent behavior using session traces and Data Cloud. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries. DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.
Use when working with Lightdash YAML files, dbt models with Lightdash metadata, the lightdash CLI (deploy, upload, download, preview, lint, warehouse-catalog, sql, set-warehouse), or managing charts, dashboards, spaces and access, AI agents, scheduled content, users, groups, custom roles, metrics, and dimensions as code
Drive a Metabase instance from the terminal via the `mb` CLI. Authenticate with named profiles; inspect databases (list, get, full metadata rollup, schemas, tables in a schema) and trigger manual schema sync / field-values rescan; inspect tables, fields; list/get/create/update/archive cards (questions, models, metrics) and run them as JSON/CSV/XLSX; list/get/create/update dashboards and patch dashcards; list/get/create collections and traverse the hierarchy by id, entity_id, or "root"/"trash" (with items and recursive tree); list/get/create/update/archive native query snippets, segments, and measures; author/update/run transforms and schedule transform-jobs; read/update settings; search content (cards, dashboards, collections, transforms, metrics); manage Enterprise workspaces; git-sync to/from a git remote (status, dirty, import, export, branches, stash, add/remove a collection from sync). Use whenever the user wants to interact with a Metabase from the terminal — "log into metabase", "what profiles do I have", "list cards", "run card 42 as CSV", "create a transform", "list dashboards", "move a dashcard", "list collections", "what's in collection 4", "show the collection tree", "list snippets", "create a segment", "archive a measure", "search metabase for X", "spin up a workspace", "import the latest changes", "add a directory to git sync", "set a setting", "what schemas are in this database", "trigger a sync", "rescan field values", or anything hitting `mb <verb>`.
Analyze marketing performance with key metrics, trend analysis, and optimization recommendations. Use when building performance reports, reviewing campaign results, analyzing channel metrics (email, social, paid, SEO), or identifying what's working and what needs improvement.
Expert-level Prometheus monitoring, metrics collection, PromQL queries, alerting, and production operations
Core bioinformatics concepts including SAM/BAM format, AGP genome assembly format, sequencing technologies (Hi-C, HiFi, Illumina), quality metrics, and common data processing patterns. Essential for debugging alignment, filtering, pairing issues, and AGP coordinate validation.
Use when you need to choose the right visualization for your data and question, then create a narrated report that highlights insights and recommends actions. Invoke when analyzing data for patterns (trends, comparisons, distributions, relationships, compositions), building dashboards or reports, presenting metrics to stakeholders, monitoring KPIs, exploring datasets for insights, communicating findings from analysis, or when user mentions "visualize this", "what chart should I use", "create a dashboard", "analyze this data", "show trends", "compare these metrics", "report on", "what does this data tell us", or needs to turn data into actionable insights. Apply to business analytics (revenue, growth, churn, funnel, cohort, segmentation), product metrics (usage, adoption, retention, feature performance, A/B tests), marketing analytics (campaign ROI, attribution, funnel, customer acquisition), financial reporting (P&L, budget, forecast, variance), operational metrics (uptime, performance, capacity, SLA), sales analytics (pipeline, forecast, territory, quota attainment), HR metrics (headcount, turnover, engagement, DEI), and any scenario where data needs to become a clear, actionable story with the right visual form.