データ分析、機械学習、LLMアプリ、プロンプト設計（8,911 件）
Design and implement multi-tenant SaaS architectures with RLS, tenant isolation, and PostgreSQL / Desain dan implementasikan arsitektur SaaS multi-tenant dengan RLS, isolasi tenant, dan PostgreSQL.
日本語の概要は準備中です。原文の説明を表示しています。
Smart agentic web data extraction with multi-strategy scraping (Crawl4AI v4, Firecrawl), LLM extraction loops, anti-bot bypass, and structured export / Ekstraksi data web cerdas dan agentic dengan scraping multi-strategi (Crawl4AI v4, Firecrawl), ekstraksi LLM, bypass anti-bot, dan ekspor terstruktur.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for Knowledge Graphs, GraphRAG, Microsoft GraphRAG, Neo4j Text2Cypher, multi-hop relational retrieval, and hybrid vector-graph search / Panduan ahli Knowledge Graph, GraphRAG, dan pencarian relasional multi-hop.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for Feature Flags & Progressive Rollout (PostHog, LaunchDarkly, GrowthBook), A/B testing orchestration, and canary releases / Panduan ahli Feature Flags, A/B testing, dan rilis bertahap.
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Grants the AI the ability to act proactively using native cron/timer scheduling. The agent can monitor systems, poll APIs, or watch logs in the background and self-trigger without waiting for user prompts.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for deterministic prompt prefix engineering, KV-cache locking for Gemini 4 Pro / Claude 5.5 / GPT Astra 6, canonical tool sorting, and achieving 90%+ prompt cache hit rates / Panduan ahli rekayasa prefix prompt deterministik, penguncian KV-cache untuk Gemini 4 Pro / Claude 5.5 / GPT Astra 6, pengurutan kanonikal tool, dan pencapaian 90%+ cache hit rate.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for AI FinOps, dynamic model routing (LiteLLM, Portkey, RouteLLM), cost optimization, budget enforcement, semantic caching, and latency-based fallback architectures / Panduan ahli untuk AI FinOps, routing model dinamis, optimasi biaya, penegakan anggaran, semantic caching, dan arsitektur fallback berbasis latensi.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for Local Small Language Models (SLMs) and Edge AI execution — WebLLM, Transformers.js v3, ONNX Runtime Web, WebGPU, and zero-latency local embeddings / Panduan ahli SLM lokal dan AI edge di browser.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for maps and geospatial data (Mapbox GL JS, Leaflet, Google Maps, PostGIS) / Panduan ahli peta dan data geospasial (Mapbox GL JS, Leaflet, Google Maps, PostGIS).
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for defending AI applications against prompt injection, jailbreak attacks, indirect injection via tool outputs, and data exfiltration through tool calls — input sanitization, output filtering, canary token detection, and layered defense architecture / Panduan ahli pertahanan aplikasi AI terhadap prompt injection, serangan jailbreak, injeksi tidak langsung via output tool, dan eksfiltrasi data melalui tool calls — sanitasi input, filtering output, deteksi canary token, dan arsitektur pertahanan berlapis.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for Data Pipelines, ETL/ELT, and Analytics Engineering. Covers dbt, Apache Airflow, Dagster, BigQuery, ClickHouse, and DuckDB / Panduan ahli untuk Data Pipelines, ETL/ELT. Mencakup dbt, Airflow, Dagster, BigQuery, ClickHouse, dan DuckDB.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP v1.x), hybrid reasoning models, RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, Model Context Protocol (MCP), model hybrid reasoning, arsitektur RAG, vector database, dan agen AI.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for Prompt Engineering, Chain-of-Thought, few-shot prompting, structured output, prompt injection defense, and automated AI evaluations & regression benchmarking (Promptfoo, DeepEval) / Panduan ahli rekayasa prompt dan evaluasi otomatis AI.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for data visualization, charts, and dashboards using D3.js, Recharts, Chart.js, Nivo, and Tremor / Panduan ahli visualisasi data, chart, dan dashboard menggunakan D3.js, Recharts, Chart.js, Nivo, dan Tremor.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for observability, analytics, telemetry, and data pipelines (OpenTelemetry, PostHog, Mixpanel) / Panduan ahli untuk observabilitas, telemetri, dan analitik.
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Run before writing or changing code for any feature, bug fix or refactor that touches data, jobs, money, outside services or user-facing behaviour. Finds where the change will break before it is built, by walking ten named failure classes (twice, halfway, outside call, failure-is-not-empty, neighbors, endings, money, hostile user, words, scale and time) against the code, and writes the answers into the plan. Use when planning a change, when asked to "think it through", "cover the gaps", "make sure it's bug free", or before any edit to payment, publishing, deletion, auth or background-job code.
日本語の概要は準備中です。原文の説明を表示しています。
Rewrites the prompt typed after the command before any work starts, then works from the rewrite. Splits a mixed-up message into a numbered list of asks, turns words like "be 100% sure", "cover all cases" or "don't break anything" into the checks they stand for, keeps every name, path, number and quote exactly, and stops to ask only where it would have to guess. Runs only when the user types it.
日本語の概要は準備中です。原文の説明を表示しています。
Lifts the task's scope for one task. The session, its reviews and fix-the-class then look across the whole codebase and every repo that shares its code, data or vendors, and every problem found anywhere is handled by the usual rules. Runs only when the user types it.
日本語の概要は準備中です。原文の説明を表示しています。
Used to invoke the PXMeter tool for rigorous quality assessment of biomolecular structure prediction models (e.g., proteins, nucleic acids, small molecules). This skill covers single-sample CIF evaluations and large-scale dataset benchmarks, providing rich result parsing support. Trigger this skill when the user asks to "run PXMeter", "compare PDB/CIF evaluation results of different tools", "check benchmark scores", or asks "how accurate is the structure generated by the model".
日本語の概要は準備中です。原文の説明を表示しています。
Unified Kaggle skill. Use when the user explicitly mentions Kaggle, kaggle.com, a Kaggle URL, Kaggle competitions, Kaggle datasets/models/notebooks, Kaggle forums/discussions/writeups, Kaggle benchmarks, hackathons hosted on Kaggle, Kaggle badges, or Kaggle account setup. Do not use for generic ML, GPU/TPU, notebook, dataset, benchmark, or data-science tasks unless the user clearly ties them to Kaggle.
日本語の概要は準備中です。原文の説明を表示しています。
Data-model rules that make a schema importable from day one, so the migration-import-engineer is never blocked on missing columns. Every SMB Product-Builder product must let a customer bring their data from an incumbent (ServiceTitan/Toast/Mindbody/Shopify) — that requires provenance (source_ref) and rollback (import_batch_id) on importable entities, and modelling real-world actors as entities rather than inline fields. Applied by architect when writing the data model in ARCH-{slug}.md, and checked by migration-import-engineer. One cheap rule set prevents the migration↔architecture seam gap from recurring across all 40 products.
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Reusable 3-round self-challenge + arbiter pattern for filtering false positives from findings/verdicts. Use when the cost of a false-positive gate block exceeds the cost of ~4 extra LLM turns.
日本語の概要は準備中です。原文の説明を表示しています。
Shared review framework that every domain reviewer (pci, oracle, gov, edtech, healthcare, mlops, etc.) MUST follow. Defines the output artifact (TM-{slug}.md), mandatory sections, severity scale, verdict format, the workflow scaffold (when-invoked, Step-0 read-inputs, HANDOFF), and the "domain heuristic vs generic check" boundary. Eliminates duplication across the ~30 reviewer prompts.
日本語の概要は準備中です。原文の説明を表示しています。
Structured idea generation + multi-LLM debate for the product-owner stage. Diverge (generate genuinely different bets), debate (a 4-persona panel on 4 models argues over 2 rounds), converge (synthesize a recommendation). Used by product-owner before architect; available to architect for design-space exploration.
日本語の概要は準備中です。原文の説明を表示しています。