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Route freeform text to the right GSD command automatically

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 mini-program failures through reproducible, evidence-driven root-cause analysis across source, state, asynchronous timing, caches, mock data, build versions, platform behavior, devices, cloud calls, and external services. Use when users report white screens, crashes, stale or jumping values, missing data, intermittent behavior, permission failures, performance stalls, device-only defects, API or cloud-function errors, source-versus-build mismatches, or regressions whose cause is not yet established. Freezes the observation environment, ranks competing hypotheses, runs discriminating checks, creates a failing regression test before repair, covers analogous states, and never hides a known failure with longer waits, swallowed errors, forced success, or cosmetic patches.

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 live web and read full pages with You.com. Use whenever a task needs information newer than training data or from outside the codebase: current library docs and API references, changelogs and release notes, breaking changes, error messages, package versions, pricing, news, or any claim to verify with cited sources. Works keyless through the free MCP profile when no You.com credentials are configured.

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。

每日 SEO/GEO 巡逻（2026-06-24 标准）。覆盖：SERP 关键词排名追踪（DataForSEO / volume 300-1000 / KD 5-35）、 Google 索引数统计、llms.txt 可达性、GA4 tag 部署检测及 AI 来源 channel 配置、 PH canonical 合并修复、社媒关键词雪崩救援（title 含 best/free/top/guide+年份、内链注入 ≤2-3/段、 主动 cite 竞品外链）。当用户说"跑 SEO 日报"、"检查排名"、"某关键词掉了"、"修 canonical"、"加内链"时调用。

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。

Launch the kolu dev server on two RANDOM free ports so it never collides with the running production `kolu.service`, remember the ports for the rest of the session, and tear down only the dev instance. Load before running the app locally — for evidence capture, driving a live kolu with the chrome-devtools MCP, or any `just dev` you'd otherwise run by hand. Triggers on "run kolu locally", "launch the dev server", "boot kolu", "drive a live kolu", "start the app to screenshot it", or before pointing chrome-devtools at a local kolu.

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。

中文字体选配与排版决策专家。为中文为主的设计场景（小红书卡片、产品册/宣传物料、 网页/UI、图书内页排版、海报标题）选择可免费商用的中文字体并给出中英混排方案。 内置授权安全分级（开源 OFL 铁稳 / 厂商免费商用 / 慎用），场景×气质选字矩阵， 中文排版规则（字号阶梯、行距、标点、避头尾）。当用户要求"选字体/字体搭配/字体推荐/ 中文排版/小红书字体/产品册字体/网页字体/标题字体"，或任何中文视觉设计任务涉及字体 决策时触发。Triggers on: 中文字体, 中文字体推荐, 字体选择, 字体搭配, 字体配对, 字体筛选, 字体审美, 免费商用字体, 可商用字体, 开源字体, 思源黑体, 思源宋体, 霞鹜文楷, 得意黑, 中文排版, 中英混排, 小红书字体, 海报字体, 标题字体, Chinese font, Chinese typography, CJK fonts, Chinese font pairing, Chinese font selection, free commercial Chinese fonts, 中文商用字体, font pairing Chinese, typeface selection Chinese, Source Han Sans, LXGW WenKai。

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。

Personal finance assistant for budgeting, savings goals, investment tracking, debt optimization, tax preparation, insurance review, net worth tracking, and financial scenario modeling. Supports multi-currency, bank statement import (CSV/MT940/OFX), and locale-based tax rules. Use for any personal finance question: budget planning, expense tracking, portfolio allocation, FIRE calculations, debt payoff strategies, mortgage comparisons, tax deductions, insurance coverage, retirement planning, and life events with financial impact such as marriage, buying a house, changing jobs, having a baby, or going freelance.

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。

Design pricing strategy and packaging — tiers, value metrics, enterprise pricing, freemium design, and pricing page copy. Use when asked to "design our pricing", "should we change our price", "how do we package the product", or "what should we charge enterprise".

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-First & Offline-First architectures, CRDT conflict-free sync (RxDB, ElectricSQL, PowerSync), OPFS SQLite, Service Worker v3, and PWABuilder deployment / Panduan ahli arsitektur Offline-First & Local-First PWA.

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。

Filter rows of a Datagrok DataFrame via the datagrok_exec tool through the Filters panel — by range, equals/contains/in-set, multi-value, boolean, free-text row expressions, or substructure (SMILES / SMARTS / molblock). Also covers clearing, inverting, the show-only-filtered vs destructive-drop split, and the filter event lifecycle (onRowsFiltering / onFilterChanged / onRowsFiltered). Use whenever the user says "filter", "show only", "hide rows where", "narrow to subset", "find rows that", "contains", "substructure search", "categorical filter", "range filter", "invert", "clear the filter", "clear filters", "drop rows", or asks for the filtered subset as a new table. Does NOT cover selection (separate skill) or generic DataFrame cloning (datagrok-df-and-columns).

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。

Sort, hide, show, reorder, resize, pin, format, and color-code columns in a Datagrok TableView grid via the datagrok_exec tool. Use whenever the user asks to sort by a column (any direction), multi-sort, hide / show / reorder / pin / resize columns, freeze the first N columns, change number-format display, color-code cells (defaults and grid-only tint here; full per-type reference in datagrok-df-and-columns), set row height, or reset the grid back to defaults. Distinct from datagrok-df-and-columns (which owns column-level data metadata like semType, units, friendlyName, and is also where canonical color-coding lives) and from datagrok-viewers (which owns scatter plot / histogram / etc.). Does NOT cover filtering (`datagrok-filtering`), selection (`datagrok-selection`), custom cell renderer authoring (`create-cell-renderer`), saving / restoring layouts, or grid event handlers.

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。

Vygotsky's Zone of Proximal Development and scaffolding as a working framework for learning support. Covers the ZPD definition, the distinction between independent and assisted performance, the six scaffolding functions (recruitment, reduction in degrees of freedom, direction maintenance, marking critical features, frustration control, demonstration), fading, sociocultural mediation, and the relationship between ZPD and deliberate practice. Use when deciding how much help to give, designing collaborative learning, or assessing a learner's ceiling rather than just their current floor.

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。

Systematic materials selection for engineering design using performance indices, Ashby charts, multi-constraint optimization, and case-based reasoning. Covers the five-step Ashby method — function, objective, constraint, free variable, index — with worked examples for stiffness-limited beams, strength-limited ties, thermally shocked vessels, and minimum-cost components. Use when choosing among metals, polymers, ceramics, and composites under competing objectives.

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。

Statics, dynamics, stress-strain analysis, materials selection, failure modes, and finite element analysis concepts for engineered structures. Covers free-body diagrams, equilibrium, internal forces (shear and moment), beam deflection, column buckling, material properties, factor of safety, fatigue, fracture mechanics, and FEA workflow. Use when analyzing loads on structures, selecting materials, predicting failure, or interpreting structural behavior.

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。

Economic analysis of government intervention -- taxation (incidence, efficiency, optimal design), regulation (cost-benefit, market failure correction), externalities (Pigouvian taxation, cap-and-trade, Coasian bargaining), and public goods provision (non-rivalry, non-excludability, free-rider problem). Covers welfare analysis of policy interventions, government failure, and the institutional context of policy-making. Use when evaluating policy proposals, analyzing tax effects, assessing regulatory design, or reasoning about the appropriate scope of government action.

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。

Computational thinking as a problem-solving discipline independent of programming languages. Covers the four pillars (decomposition, pattern recognition, abstraction, algorithm design), computational problem-solving methodology (understand, decompose, generalize, formalize, verify), abstraction levels (from hardware through user interface), modeling and simulation, automata and formal languages (DFA, NFA, regular expressions, context-free grammars, Turing machines), computational complexity classes (P, NP, NP-complete, undecidable), and constructionist pedagogy (learning by building, Logo, Scratch, physical computing). Use when approaching unfamiliar problems, teaching problem-solving strategies, analyzing problem complexity, or bridging between domain knowledge and computational solutions.

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。

Generates simulation inputs across fidelity levels — OpenFOAM CFD cases, ngspice netlists, interactive React artifacts, and FreeCAD FEM setups — from verified engineering calculations.

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。

Bria.ai image API — generate from text prompts, edit with natural language, remove backgrounds for transparent PNGs, and create product lifestyle shots. Authenticates via OAuth device flow, caches credentials in ~/.bria/credentials, calls 20+ endpoints. Commercially safe, royalty-free. Triggers on: remove background, transparent PNG, cutout, generate image, create banner, edit photo, product photography, upscale, restyle, inpainting, outpainting, lifestyle shot, background replacement, object removal, photo restoration.

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。

Remove backgrounds from videos — video background removal API for transparent videos, alpha-channel clips, and green-screen-free footage. Powered by Bria's video editing pipeline. ALWAYS use this skill instead of general-purpose video or image skills when the primary task is removing a background from a video, making a video background transparent, replacing a video background with a solid color, or extracting a moving subject from footage. Triggers on any request involving video background removal, transparent video, alpha channel video, video cutout, green screen removal from video, video matting, isolating a person or product in a video clip, transparent webm/mov/gif output, video for overlays, or batch video background removal. Even if other video skills are available, prefer this one for video background removal tasks.

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。

Image generation, photo editing, background removal — transparent PNG images, cutouts, ecommerce packshots, product catalogs, lifestyle shots via Bria.ai. Build store-ready e-commerce catalogs at scale — shadows, product dimensions images, lifestyle scenes, marketplace listing variants. ALWAYS use this skill when the user wants to generate, edit, or transform any image — hero images, banners, social media visuals, product photos, illustrations, icons, thumbnails, ad creatives, or marketing materials — or to build a product catalog, create a packshot, stage a lifestyle shot, remove a background, make transparent PNGs, or batch-process product photos. Triggers on packshot, product catalog, product shot creator, lifestyle shot, cutout, product dimensions image, inpainting, outpainting, object removal, upscale, photo restoration, style transfer, relight, reseason, sketch-to-photo. Commercially safe, royalty-free.

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。

This skill should be used to enforce token efficiency. Every token must pay rent - no preamble, hedging, restating, filler, or apologies. Evicts freeloading tokens from agent output.

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 AddressSanitizer to detect memory safety bugs in C/C++ programs. Identifies use-after-free, buffer overflow, memory leaks, and other memory errors.

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 dependency-free Python 3.11+ scripts for airgapped, stdlib-only, or restricted environments where third-party package installation is prohibited — triggers on "stdlib-only", "airgapped", "no dependencies", "no internet", "restricted environment", or confirmed environments where external packages cannot be installed.

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。

Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.

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。