「ecto」の検索結果

Find Ecto N+1 queries and missing preloads. Use only when N+1 is suspected; not for broad database performance.

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Use when a task touches Ecto, even a how-to question: schemas, changesets, validations, queries, preloads, Multi…

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。

Debug Ecto constraint violations - trace triggers, check migrations; Use when seeing unique_constraint…

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Detect N+1 query anti-patterns specifically — Repo calls inside Enum/for loops, missing preloads on associations. Use when N+1 is explicitly suspected, NOT for unrelated Ecto questions or wider database performance.

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Compatibility alias for the Elixir/Phoenix plugin's Ecto constraint debugger. Invoke explicitly with /ecto:constraint-debug.

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Debug Ecto constraint violations - trace triggers, check migrations, find duplicate data. Use when seeing unique_constraint, foreign_key_constraint, or check_constraint errors.

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统计与机器学习异常值检测工具。读取结构化表格数据，使用 IQR、Z-score、IsolationForest 或分组标准差一致性方法识别异常值，输出带异常标记、删除异常行或裁剪异常值后的结果文件。 当用户提到异常值检测、离群值识别、IQR、Z-score、IsolationForest、按实验组/分类组检查数值一致性等需求时使用此skill。 即使用户没有明确说出“statistical_outlier_detector”，只要任务涉及结构化数值字段异常识别或分组均值标准差一致性检查，就应该使用此skill。 不负责字段固定阈值上下限校验、枚举值校验、时间序列缺失/频率校验或格式转换。

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A股/港股/美股通用"卡脖子"选股技能。应用 Serenity（@aleabitoreddit）的供应链瓶颈理论， 对任意板块/产业链进行结构化分析，寻找"一旦断货整个产业就停工"的瓶颈环节， 并筛选该环节中具备技术壁垒和资本信号的小盘股。 触发词：分析XX板块、找XX卡脖子、serenity分析、A股/美股/港股瓶颈产业链 EN triggers: analyze [sector] choke points / find bottlenecks in [industry] JP triggers: [セクター] のボトルネックを分析 KR triggers: [산업] 의 병목 지점 분석 适用板块示例：电力、光模块、医疗器械、半导体设备、军工、新能源车等任意板块； 港美股示例：InP衬底（AXTI）、CPO激光器（AAOI）、AI算力链（NVDA/TSM） 数据源：三级数据获取策略（智能体自带财经工具 → 本技能内置脚本 → 网络检索）， 不依赖任何特定平台，Claude Code / Codex / 通用智能体均可直接使用。 行情/估值支持 A股+港股+美股；研报/两融/筹码等深度数据仅A股。 ⚠️ 注意：本技能不预测大盘走势，不适用于纯题材炒作无实质产业逻辑的小票。

Use the open-source free `QuickDup` clone detector for .NET repositories. Use when a repo needs duplicate C# code discovery, structural clone detection, DRY refactoring candidates, or repeatable duplication. USE FOR: the repo wants QuickDup; the team needs repeatable duplicate-code scans for C#; the user asks about DRY cleanup, copy-paste detection, clone detection, or duplicate. DO NOT USE FOR: formatting-only work; repos that intentionally use a different clone detector and do not want overlap. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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。

Diagnose and fix concrete MSBuild property defects in projects and existing shared-file hierarchies. USE FOR: conditions, defaults, append versus overwrite, last-write-wins values, OS/TFM checks, portable paths, normalization, and reviews centered on those property defects. Property defects in Directory.Build.* remain in scope, including overwritten values across parent/child imports and conditions that run before TargetFramework is set, even when the fix changes import order or moves a property group to .targets. DO NOT USE FOR: placement-only or import-only requests with no concrete property defect, such as discovering shared files or choosing which file owns a target or customization (use directory-build-organization); item operations; target structure; broad reviews without a concrete property defect; non-MSBuild work.

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Identify a .NET project's test platform, framework, command mode, and SDK-style vs classic project system. Use only for "which test platform/framework?", "VSTest or MTP?", or "what runner does this project use?", including bridge settings, UseVSTest opt-outs, and incompatible or conflicting VSTest/MTP configuration. Resolves global.json, project, packages.config, Directory.Build.props, and Directory.Packages.props precedence for MSTest/xUnit/NUnit/TUnit. DO NOT USE when the user asks to run/filter tests or for commands, flags, TRX/dumps, or test-command/filter errors; use run-tests directly. Do not route hot-reload or migration requests here as the entry skill; mtp-hot-reload may use this skill internally for platform detection.

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Use the open-source free `coverlet` toolchain for .NET code coverage. Use when a repo needs line and branch coverage, collector versus MSBuild driver selection, or CI-safe coverage commands. USE FOR: coverlet setup; CI line or branch coverage; choosing between collector and MSBuild drivers. DO NOT USE FOR: coverage report rendering by itself; repos that intentionally use a different coverage engine. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Upload files or directories to TOS-compatible object storage for Volcano Engine or BytePlus and download files from URLs. Use this skill when (1) Upload Agent-generated files or directories for sharing, (2) Download files from URLs before Agent processing.

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Direct and produce Chinese-first cinematic motion videos with HyperFrames. Use this skill when the user wants a rendered promo film, article-to-video piece, product launch film, website-to-video piece, keynote reveal, kinetic typography sequence, text/icon transition promo, music-synced motion graphic, HTML/CSS/SVG/GSAP video, short-form vertical video, or a landing page/product story turned into motion. Default to Simplified Chinese, vertical 9:16, and 1080x1920 unless the user or platform clearly requires another format. The skill enforces a two-phase workflow: brief first, confirmation second, then assets, composition, validation, snapshots, render, and review report.

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Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas.

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Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

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High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics

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Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

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Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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Use when attacking a Windows Active Directory domain — Kerberos roasting/delegation, coercion + NTLM/Kerberos relay (CVE-2025-33073), ADCS ESC1-16 (EKUwu), ticket forgery & DCSync, dMSA BadSuccessor (CVE-2025-53779), BloodHound attack-path enumeration, domain dominance

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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。

Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

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。