Loading...
Loading...
Found 10,827 Skills
Concise communication mode. Cuts ~60-70% of output tokens while keeping natural, readable English. Drops filler, hedging, and pleasantries but maintains grammar and sentence flow. Elaborate on request -- ask for detail and get it, then auto-return to concise. Use when user says "concise mode", "be concise", "less verbose", or invokes /concise. Also triggers when user requests token efficiency with readability.
Analyzes and designs batch and streaming data pipelines with contracts, lineage, reliability, and cost controls. Use for ingestion and transformation systems. NOT for ad-hoc analysis or schema design.
Workload-aware architecture design for VeloDB/Apache Doris. MUST USE when designing data architectures, choosing between data models, planning ingestion strategies, sizing clusters, or translating business requirements into VeloDB/Doris system designs. Complements velodb-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 VeloDB". Also use for legacy analytics/search/serving stack consolidation prompts even when VeloDB 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.
VeloDB/Apache Doris table design and cluster sizing best practices. MUST USE when writing, reviewing, or optimizing Doris CREATE TABLE statements, partition/bucket strategies, data models, or cluster configurations. ALSO MUST USE whenever the velodb-architecture-advisor skill produces DDL — apply the Pre-Flight Checklist to every CREATE TABLE before output. Also triggers on any workload design involving: IoT, analytics, dashboard, CDC, time-series, log analysis, real-time warehouse, point query, data platform, or any scenario where table design decisions are being made. Also triggers on replacing or migrating from legacy analytics/search/serving stacks such as Impala, Kudu, Elasticsearch/ES, Greenplum, Presto, HBase, Hive, Hadoop, Redis, or Lambda-style multi-engine data platforms, even when VeloDB/Doris is not named explicitly. Also use when user provides a VeloDB connection string or asks to get started. Also triggers on slow query investigation, query profiling, runtime performance diagnosis, tablet skew analysis, and table health checks — any scenario where runtime evidence (profile output, tablet distribution) informs optimization. For Cloud operations (auth, cluster lifecycle, billing, networking), defer to the velocli-cloud skill.
Diagnose and interpret AE/TE A/B experiments from configuration and report evidence through a defensible decision. Use when the user asks what an experiment means, whether it can roll out, why a result is not significant, why group sizes or exposure are wrong, why treatment results conflict, whether the report is trustworthy, or what to do next. Covers SRM, duration sufficiency, novelty effects, metric conflicts, missing or anomalous data, design reasonableness, data reliability, metric interpretation, trend and segment analysis, root-cause hypotheses, and rollout recommendations. All platform discovery and reads must use ae-cli.
Used for creating PPTs, presentations, slides, and reporting materials. Dashi PPT combines pages based on preset visual themes to generate HTML presentations that can be opened offline and edited in browsers, supporting export to PPTX / PDF files.
Split one artifact — a claim, plan, or file — across 2 to 5 independent lenses, one per genuinely distinct failure mode (correctness, security, readability, cost, adversarial-user), and return their convergence: where they agree, where they disagree, and the single next question that resolves the disagreement. Use when one reviewer isn't enough because the failure modes are heterogeneous, or a claim looks strong to its author and needs cross-lens pressure before it ships.
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
Complete SEO skill for technical audits (Core Web Vitals, site speed, crawlability/indexation, robots/sitemaps/canonicals, structured data, mobile, security, internal linking), SEO marketing strategy (keyword research, content planning, competitive analysis, E-E-A-T), operational workflows (cross-team collaboration, OKRs), link building, local SEO, international SEO (hreflang), and multi-platform SEO (Google, YouTube, Reddit, social). Updated for January 2026.
Guidance for extracting weight matrices from black-box ReLU neural networks using only input-output queries. This skill applies when tasked with recovering internal parameters (weights, biases) of a neural network that can only be queried for outputs, particularly two-layer ReLU networks. Use this skill for model extraction, model stealing, or neural network reverse engineering tasks.
Traffic-First opportunity discovery. KILL funnel filters ideas by traffic channel, demand, competition, revenue, interest, MVP-ability. Outputs one idea + one channel recommendation.
Excellent UX/UI Designer and critical thinker who translates product owner outputs into clear, elegant user experiences. Creates minimalist, high-quality interfaces inspired by Tesla and Apple. Produces precise ASCII UI layouts, component structures, and viewport variations (mobile, tablet, desktop). Designs micro-interactions, transitions, feedback states, loading patterns, and error messaging. Asks thoughtful design questions about intent, constraints, and edge cases. Welcomes feedback and iterates quickly. Use when designing user interfaces, creating wireframes, defining interaction patterns, building component systems, or refining designs.