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概要と使いどころ
Frappe Builder website and page-building guidance for pages, blocks, components, forms, routing, CMS-style content, SEO, and web UX. Use when work touches Frappe Builder, website pages, landing pages, or content-driven experiences.
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。
CSS animation patterns - transitions, keyframes, scroll-driven timelines, @property, compositor-friendly properties, prefers-reduced-motion
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 Reanimated 4 - shared values, animated styles, spring/timing/decay, layout animations, gesture integration, scroll-driven animations, interpolation, worklets, CSS 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。
Direct MySQL database access with mysql2 driver -- connection pools, prepared statements, transactions, streaming, typed queries, error handling
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。
Native MongoDB driver (the mongodb npm package) - MongoClient lifecycle, typed collections, CRUD result shapes, cursors, aggregation pipelines, index design, transactions
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。
Serverless MySQL platform with branching, deploy requests, and edge-compatible driver
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。
Serverless PostgreSQL with branching, autoscaling, and edge-compatible driver
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。
Edge-hosted SQLite database with libSQL driver and embedded replicas
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。
Close out a completed EPIC / sprint end-to-end: author a fact-verified meta-post announcing what shipped, archive the EPIC's handovers, snapshot orchestrator memory, gate the integration→main merge on CI-green + explicit human go, drive the version bump (merge-commit conventions that auto-sync classifies), and verify the release reached the registry/PyPI. Use when the user says "close out EPIC <n>", "wrap up the sprint", "ship the meta shift", "/epic-close", or asks to finalize an integration branch that is ready to merge to main. Generalizes the EPIC 1002 (Yggdrasil II) close-out. This skill orchestrates — it authors founder docs directly (superadmin) and delegates code/regen to workers; it NEVER merges to main without the human gate.
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。
Gaia Skill Tree image-generation and deterministic export harness for Ascension Overdrive v3. Use when generating, iterating, refining, cropping, color-grading, making transparent cutouts, compressing, upscaling, or validating production image assets. Always use image gen 2 / gpt-image-2 and never nano-banana.
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。
Execute a project from a folder of raw materials. Use when the user drops messages, screenshots, documents, drafts, and references into a project directory and wants Codex to identify the real goal, build a plan, do the work, drive the project to a submission-ready state, verify completion, and return a clear final summary of what was required, what was completed, and how it was done.
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 provides expert-level guidance for Test-Driven Development (TDD) in VS Code extension development following t-wada methodology. Use when writing tests before implementation, creating comprehensive test suites, implementing Red-Green-Refactor cycles, or improving test coverage for extension components like WebViews, terminal managers, and activation 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。
Builds reusable malware configuration extractors by applying a declarative JSON spec (offsets, decode layers, field definitions) to a sample to produce structured, defanged config. Activates for requests to build a config extractor, write a reusable family config parser, or run a spec-driven configuration extraction.
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。
Frames a structured, testable threat-hunting hypothesis: grounding it in adversary behavior and available telemetry, defining the data sources and detection logic, and setting success criteria and outcomes. Activates for requests to start a threat hunt, write a hunt hypothesis, or plan a hypothesis-driven hunt.
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。
Plan and promote events — conferences, trade shows, webinars-as-events, and field marketing that drives pipeline.
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。
Domain-Driven Design: bounded contexts, ubiquitous language, aggregates, and tactical patterns. Use when modeling complex domains or structuring domain 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。
Build SaaS customer support — tiered support models, SLAs, macros, escalation, and support-driven product feedback.
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 autonomous task-driven agents with the BabyAGI loop — task creation, prioritization, and execution from a single objective.
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。
Optimize content for AI answer engines (AEO/GEO): citation-friendly structure, factual precision, and measuring AI visibility. Use when AI search drives discovery.
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。
Run goal-driven autonomous agent loops — decomposing objectives, executing tool chains, self-critiquing, and iterating until done. Use when exploring what fully autonomous task execution looks like and its limits.
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。
Create 3D web experiences: scene setup, models, materials, lighting, animation, scroll-driven scenes, and performance budgets. Use when adding 3D to websites beyond basic demos.
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。
Master Alfred on macOS: search, workflows, clipboard history, snippets, and the Powerpack. Use when building a keyboard-driven command layer on the Mac with Alfred.
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。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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 this skill whenever the user asks to run Stata commands, estimate econometric models, work with .dta files, run a .do file, generate Stata output, or do any statistical analysis where Stata is involved. Also trigger when the user mentions Stata variables, Stata syntax, or econometric tasks where Stata is the natural tool, including regressions, IV estimation, diff-in-diff, RDD, panel data, clustering, summary statistics, and margins. Stata runs through pystata on StataNow 19.5 BE; configure once with stata_setup, then drive everything with stata.run() and exchange data directly with pandas. Prefer this skill over subprocess calls or .do-file shelling for Stata work, including cases where the user does not say pystata.
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。