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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

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。

Audit a live page for accessibility issues, locate each WCAG violation precisely, and return a selector-grounded fix worklist without editing.

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。

Migration guide for Swift and iOS. Use when migrating ObservableObject to @Observable (iOS 17), enabling Swift 6/6.2 Strict Concurrency, fixing @MainActor/Sendable warnings, adopting Approachable Concurrency (Swift 6.2), or handling iPadOS 26 windowing system changes (UIRequiresFullScreen deprecation). Covers breaking changes and version upgrade 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。

Single Source of Truth (SSOT) guidelines for SwiftUI state management. Use when designing state architecture, choosing between @State/@Binding/@StateObject/@ObservedObject/@EnvironmentObject, fixing state synchronization issues, refactoring duplicated state, or reviewing state management in PRs. Covers SSOT principles, property wrapper selection, state lifting, and common violations.

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。

Export/publish the live portfolio tracker as a static, stripped snapshot into the "Portfolio Snapshot" folder — an empty Trades section (first), the holdings table, and the 1-month-return bar chart baked to static SVG (scatter chart opt-in), with a Publish_Snapshot property. Strips all instruction/engine/log elements (refresh dataviewjs, tip callouts, Upcoming Earnings, Column Legend, Notes, Log). Use when the user says "snapshot the portfolio", "publish the portfolio", "export Live Portfolio", or "portfolio snapshot".

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。

Adversarial thesis testing — act as a short seller to find the weakest points in a thesis. Use when user says "stress test", "devil's advocate", "challenge this thesis", or "break [TICKER]".

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。

Earnings-season triage across ALL active theses (not just Live Portfolio holdings) — every earnings call reported inside the window gets a Quartr event summary, a Conviction-Trigger/Outstanding-Questions cross-reference, and a coverage check against existing Research notes, producing a ranked deep-read queue for /transcript. Use when user says "earnings sweep", "who reported", "triage earnings", "catch up on earnings season", or after any earnings-heavy stretch. Default window 14 days; `/earnings-sweep 90` covers a full season.

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。

Extract and update the EcoSIM output-variable catalog from Fortran history-field registration calls such as hist_addfld1d and hist_addfld2d. Use only when explicitly asked to update, regenerate, refresh, or rebuild the list of EcoSIM output variables from HistDataType.F90 or related F90 source; do not invoke for ordinary EcoSIM output analysis unless the variable list itself must be updated.

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。

Map observed site vegetation, species lists, land-cover descriptions, or plant management PFTs to EcoSIM vegetation codes using templates/ecosim_pftpar_20260303.nc.cdl. Use when choosing or validating EcoSIM pft_type values, pfts entries, pfts_short categories, pfts_long plant type meanings, or the combined short-code plus Koppen climate numerical code used by plant management inputs.

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。

Edit EcoSIM PFT-parameter NetCDF files such as ecosim_pftpar_20260303.nc with the ParEditor class from applications/notebooks/scripts/ParamEditor.py. Use when asked to modify, scale, copy, compare, or delete PFT parameter records for a plant functional type (six-character pfts code such as gr3s43 or ndlf34), update microbe parameter NetCDF values, or maintain the JSONL change log that records past parameter changes. Never modify plant_trait.*.desc files; they are read-only references.

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。

Read-only sanity-check of EcoSIM plant_trait.*.desc parameter values for the first grid. Use when validating plant trait description files, enforcing clean C3/C4 physiological parameterization, reviewing photosynthetic kinetics, active leaf/root protein allocation, and root hydraulics such as RSRR, RVSR, and ARSRA, or checking ranges, units, and woody/herbaceous block consistency before running EcoSIM. Never edit or replace the `.desc` file.

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。

Investigate implementation failures using root-cause-first debugging. Use when an implementer is blocked, verification fails, or repeated remediation does not converge.

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 comprehensive technical design for a specification

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。

Review a task implementation against approved specs, task boundaries, and verification evidence. Use after an implementer finishes a task, after remediation, or before accepting a task as complete.

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 comprehensive requirements for a specification

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。

Show specification status and progress

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。

Initialize a new specification with detailed project description

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 implementation tasks for a specification

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。

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

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。

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

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 when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".

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。

Jailbreak API-served LLMs using G0DM0D3 techniques — Parseltongue input obfuscation (33 techniques), GODMODE CLASSIC system prompt templates, ULTRAPLINIAN multi-model racing, encoding escalation, and Hermes-native prefill/system prompt integration. Use when a user wants to bypass safety filters, jailbreak a model, or red-team model robustness.

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。

Pre-commit verification pipeline — static security scan, baseline-aware quality gates, independent reviewer subagent, and auto-fix loop. Use after code changes and before committing, pushing, or opening a PR.

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 when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.

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。