「filter」の検索結果

Use when theming the Django admin with django-unfold - settings and sidebar configuration, custom admin site, components and @display decorator, filters, actions, tabs, dark mode and styling, or Django 6.x compatibility

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。

Use when building PyQt/PySide6 UIs with QtWidgets - display, input and container widgets, buttons, item views like QListWidget and QTableWidget, layout management, or event handling (mouse/keyboard events, event filters, shortcuts)

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。

Fierce autonomous internal co-founder. Bounded web research for evidence-backed improvements. Structured idea formulation with self-critique filter that rejects ideas not serving the goal. Auto-implements high-confidence aligned improvements, proposes medium-confidence ideas for approval. Evaluates viral coefficient, AI search visibility (GEO), and solo SaaS economics. Considers higher pursuits: employing disabled people, spiritual tech investigation, 99% wealth donation ethos.

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。

TanStack Table v8 patterns - useReactTable, column definitions, sorting, filtering, pagination, row selection, virtual scrolling, server-side data

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。

React Native Skia GPU-accelerated 2D graphics - Canvas, declarative drawing, shaders, image filters, Paragraph text, Atlas batch rendering, Reanimated animations

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。

pnpm workspace protocol, filtering, catalogs, shared dependencies, publishing, and CI/CD for monorepo management

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。

NestJS backend framework - modules, controllers, services, DI, guards, pipes, interceptors, exception filters, middleware, DTOs with class-validator

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。

Pinecone serverless vector database -- index management, vector operations, metadata filtering, namespaces, hybrid search, inference API

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。

Qdrant vector database -- collection management, point operations, payload filtering, named vectors, quantization, recommendations, snapshots

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。

Weaviate vector database patterns with weaviate-client v3 -- collection management, vectorizer modules, hybrid search, filtering, generative search (RAG), multi-tenancy, batch imports

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。

Chroma vector database -- collection management, automatic embedding, metadata filtering, document storage, query patterns

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。

Meilisearch search engine patterns -- client setup, indexing, search, filtering, facets, geo search, multi-tenancy, task management

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。

Transform git commit history into polished user-facing changelogs and release notes. Use when the user wants a CHANGELOG.md from a version tag or date range, weekly product updates, app store release notes, or Keep a Changelog formatted output with internal noise filtered out.

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。

Generate a weekly changelog entry from git history, filtering operational noise and producing editorial summary, field notes, statistics, and a bounded commit manifest. Stops for review unless commit was explicitly requested.

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。

Write Prisma Next queries for Postgres, SQLite, or Mongo — pick a lane (Postgres/SQLite `db.orm.<Model>` + `db.sql.<table>`; Mongo `db.orm.<root>` + `db.query.from(...)` pipeline builder), filter / project / sort / paginate, eager-load with `.include(...)`, Postgres/SQLite `db.transaction(...)`, Postgres/SQLite ORM `.aggregate(...)`, Mongo aggregations via query builder, namespace-aware accessors (`db.orm.<ns>.<Model>`, `db.sql.<ns>.<table>`). Triggers: query, where, match, select, project, orderBy, take, skip, include, lookup, first, all, count, aggregate, group, create, update, delete, upsert, returning, transaction, db.close, script teardown, variant, polymorphism, drizzle-style, kysely-style. Notes: `.all()` is a Thenable (just `await` it), iterators are single-use (`RUNTIME.ITERATOR_CONSUMED`), Postgres `count` is `number` while sum/avg/min/max are `number | null`, ranges use chained `.where()` or `and(...)` (no `.between(...)`).

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。

Converts hunt findings into portable Sigma detection rules: choosing the right logsource, expressing robust selection/filter logic, setting level and ATT&CK tags, and validating before converting to a SIEM query. Activates for requests to write a Sigma rule, convert a hunt to a detection, or create portable SIEM detection logic.

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。

Get detailed data about a shader asset in the Unity project. Returns shader properties, subshaders, passes, compilation errors, and supported status. Use 'assets-find' tool with filter 't:Shader' to find shaders, or 'assets-shader-list-all' tool to list all shader names.

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。

Search the asset database using the search filter string. Allows you to search for Assets. The string argument can provide names, labels or types (classnames).

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。

Use when a task needs the judgment of an Electronics Engineer working on RF, analog, and mixed-signal hardware — closing an RF link budget for a wireless telemetry radio, sizing an op-amp or instrumentation-amplifier front end against a gain-bandwidth constraint, designing an anti-alias or IF filter to a cutoff and Q, pre-checking a design against FCC/IEC EMC radiated-emissions limits before formal compliance testing, or picking between a superheterodyne and direct-conversion receiver architecture.

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。

Use when a task needs the judgment of an Automotive Engineering Technician — setting up and instrumenting test equipment (strain-gauge bridges, thermocouples, load/torque sensors) to an engineer's written test plan, selecting DAQ sample rate and an SAE J211 CFC filter class for a vehicle test channel, verifying instrument calibration (torque wrench, thermocouple, load cell, shunt-cal check on a strain bridge) against a traceable standard before a test run, reducing dyno or road-load test data (SAE J1349 power correction, rainflow-counted durability damage fraction) into engineering units, and documenting test results against the engineer's target with stated margin. Distinct from an automotive engineer (owns the system-level design decisions — structure, powertrain sizing, suspension geometry, NVH) — this role installs the sensors, configures the DAQ, executes the instrumented test, and reduces the data under a design and test plan the engineer specified.

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。

Build comprehensive DNA methylation maps by aggregating WGBS (Whole Genome Bisulfite Sequencing) data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "where is DNA methylated/unmethylated in my tissue?" by combining per-CpG methylation data into tissue-level methylation profiles. Handles coverage filtering, identifies hypomethylated regions (HMRs) and partially methylated domains (PMDs), and manages cross-lab variation.

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。

Build comprehensive histone mark maps by aggregating narrowPeak data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "where is this histone mark present in my tissue?" by combining peak calls from multiple studies into a union peak set with confidence annotations. Handles cross-lab batch effects, broad vs narrow marks, and ENCODE blocklist filtering.

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。

Build comprehensive chromatin accessibility maps by aggregating ATAC-seq and DNase-seq narrowPeak data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "where is chromatin accessible in my tissue?" by combining peak calls into a union peak set. Handles cross-lab variation, ATAC vs DNase platform differences, and ENCODE blocklist filtering.

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。

Conduct rigorous cross-study meta-analysis of scRNA-seq data from ENCODE, integrating multiple single-cell transcriptomic datasets for a tissue/cell type. Use when the user wants to answer "what cell types exist in my tissue and what genes define them?" by combining scRNA-seq data across donors, labs, and platforms. Follows the Mawla et al. 2019 framework for assessing cross-study reproducibility, TIN-based quality filtering, and detection-limit-aware interpretation. Handles batch correction (Harmony/Seurat), dropout awareness, cross-contamination artifacts, and platform-specific biases. Use this skill for ANY scRNA-seq integration task, cross-dataset comparison, cell atlas construction, or reproducibility assessment involving ENCODE single-cell data.

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。