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概要と使いどころ
Customize a new project created from this Flutter template, including app identity, package name, platform cleanup, icons, splash screen, Firebase/secrets decisions, setup tool execution, code generation, and validation.
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 and resolve PR review comments on the current branch's PR in this Flutter repository, from both AI reviewers (CodeRabbit, CodiumAI, CodePilot, Copilot, etc.) and human reviewers. Fetches comments via `gh`, splits them into AI and human groups, verifies each comment against the actual Flutter code, triages them, proposes fixes, and optionally applies fixes, replies to comments, and pushes. Use when the user asks to review PR comments, resolve PR feedback, review AI comments, review human review comments, or handle code review 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。
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。
Use when developing Thunderbird MailExtensions - Manifest V2/V3 configuration, messenger.* APIs for accounts, messages, folders and compose, UI actions, message display scripts, Experiment APIs, ATN submission, or migration from legacy extensions
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 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。
Client-side routing with data APIs — loaders, actions, error boundaries, search params, nested layouts, and code splitting
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。
Type-safe client-side routing for React with file-based routes, search params validation, loaders, and code splitting
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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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 wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
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 any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.
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。
Drain the Pharos Dependency Map coverage-audit queues into evidence-refreshed edges, reviewed target dispositions, or documented deferrals. Use monthly after weekly production dependency audits, after dependency coverage drops, or when reviewing adapter gaps, material unlinked slices, symbol leads, duplicate/split groups, or unverifiable published edges.
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 designing or changing a component's public API, its composition, or its state model in libs/ui-react or libs/ui-rnative — layering (core vs internal vs primitives), BaseProps/Props splits, converting a component to compound / changing its composition with createSafeContext, controlled/uncontrolled state, prop-naming conventions, and cross-platform API parity. Load this before shaping a new component, changing its props, or refactoring its composition.
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。
Calculate and display the token usage, turns, and estimated cost for the active session and subagent runs by parsing the harness session logs. Use when the user asks for token usage, session cost, or types `/pi-cost`.
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 whether an LLM classifier's validation-gate failure is GOLD-BOUND before spending on prompt revision or model changes. Use when: (1) a scoring pipeline over-predicts a label (precision low, recall high) and a prompt clarification is proposed to tighten it, (2) a pilot/validation gate fails and the fix candidates are prompt edits, (3) inter-rater agreement on the weak label was already low (κ < ~0.6). Core check: if gold POSITIVES share the exact feature the revision would exclude, no prompt can pass a gold-scored gate — recall craters while precision barely moves. Also documents the verified surgical-pilot design (single-section diff, tune/holdout split, pre-registered gate, perturbation check on untouched sections).
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。
Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.
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。
Gate resumption of any multi-day LLM batch-scoring campaign that calls an unpinned model alias (deepseek-chat, gpt-*-latest, gemini-*-preview, any provider alias without a pinned version). Use when: (1) resuming a paused or credit-exhausted scoring run days after its last chunk, (2) topping up credits to finish a campaign, (3) extending a cached scoring pipeline with new items. Prevents silently splicing two model versions or serving revisions into one measure. Verified 2026-07-16: for $0.30 caught a serving-revision drift WITHIN DeepSeek v4-flash (same alias, same family, litigation scores systematically shifted across a 2-day gap) before an $83 resume spend.
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 Unity 6+ UI Toolkit Runtime Data Binding code using MVVM pattern with [CreateProperty], DataBinding, and PropertyPath. Use when creating UI screens, HUDs, menus, inventories, settings panels, or any data-bound UI elements with UI Toolkit. Triggers on requests like "create a health bar", "build the HUD", "make a settings screen", "bind data to UI", "create an inventory UI", "add a pause menu", "show player stats on screen", "build a shop UI", "create a dialogue box", or any task involving runtime UI layout, styling, or data display. Also use when the user mentions UXML, USS, ViewModel, or UI bindings. Only activates when ProjectConfig.yaml -> ui_system is "UIToolkit" or "Mixed". Requires Unity 6.0+.
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。
用于创建、配置、审查或调试基于 Vite 的前端项目、vite.config.ts、环境变量、dev server proxy、HMR、插件顺序、library mode、依赖预打包、bundle splitting 或 Vite 构建性能。React/Vue 组件架构请使用框架专项 skill；中文触发词包括 Vite 配置、vite.config、Vite 构建、Vite 性能。
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 creating, configuring, reviewing, or debugging Vite-based frontend projects, vite.config.ts, env variables, dev server proxy, HMR, plugin ordering, library mode, dependency pre-bundling, bundle splitting, or Vite build performance. Use framework-specific skills for React/Vue component architecture; Chinese triggers include Vite configuration, vite.config, Vite build, Vite performance.
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。
Sales enablement, technical presales and deal acceleration for B2B SaaS revenue teams. Use this skill when planning outbound campaigns, running discovery calls, building deal strategy, creating battle cards, writing proposals, responding to RFPs, preparing a product demo, scoping a proof of concept or pilot, answering a security questionnaire or the AI annex buyers now attach to it, executing sales sequences, coaching qualification, or deciding what the website's AI chat agent is allowed to say. Also triggers on: outbound, cold outreach, reply handling, reply classification, out-of-office replies, auto-replies counted as replies, our reply rate is inflated, prospect replied remove me, opt-out by reply, is this list ready to send, launch readiness, CRM segment outbound, we emailed a current customer, outbound hit an open opportunity, ownerless leads, prospects engaging our competitors, competitor connections, displacement list, is this personalization creepy, can we mention they visited our site, signal freshness, deal strategy, pipeline, battle card, objection handling, common objections, why do we keep hearing this objection, mine call transcripts for objections, objection register, proposal, RFP, discovery call, MEDDPICC, sales sequence, qualification, sales engineer, solutions engineer, presales, demo plan, demo script, POC, proof of concept, pilot success criteria, technical evaluation, technical win, security questionnaire, vendor security review, SOC 2 questionnaire, AI security questionnaire, AI annex, AI vendor due diligence, the buyer is asking if we train on their data, do you train on customer data, which model do you use, model provider sub-processor, AI subprocessor disclosure, where does inference run, can the customer turn the AI feature off, AI-CAIQ, CSA AI Controls Matrix, ISO 42001, ISO/IEC 42001, NIST AI RMF, EU AI Act Article 50, are we a provider or a deployer, AI feature disclosure, our model vendor changed and nobody told the buyer, technical objection, mutual action plan, MAP, close plan, mutual close plan, buyer enablement, the deal is stalled, deal stalled after the demo, how do I get this deal to close, close date keeps slipping, JOLT, buyer indecision, no-decision loss, website chatbot, chat widget, AI chat agent, AI SDR, inbound AI SDR, conversational marketing, live chat, what should our chatbot be allowed to say, our chatbot quoted the wrong price, the bot promised a discount, do we have to disclose it is AI, EU AI Act chatbot disclosure, chatbot handoff to sales, chat transcript review, prompt injection on our chatbot, Drift, Qualified, Intercom.
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 a Derrick Operator (derrickhand) — running the monkeyboard during a pipe trip, reading trip-tank volume against calculated displacement for a swab or kick signature, calculating a barite weight-up in sacks, deciding whether a volume anomaly warrants stopping the trip for a flow check, or running the mud circulating system (shakers, desander, degasser) between trips.
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 a Conveyor Operator — diagnosing a mistracking belt before it becomes edge damage or spillage, verifying every lockout/tagout isolation point on a conveyor with a gravity take-up or incline before entering the nip zone, calculating each conveyor's actual delivered capacity in a multi-conveyor line to find the true bottleneck, deciding whether a mechanical or vulcanized splice fits a given tension rating, or judging whether a pinch-point guarding gap needs an immediate stop versus a scheduled fix.
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 a tenure-track or tenured Agricultural Sciences faculty member at a land-grant university — deciding whether to submit a grant this cycle versus wait, allocating time across the teaching/research/extension appointment split, diagnosing a stalled graduate student or field trial, or drafting a tenure-and-promotion dossier narrative.
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。
Query the Ensembl REST API for regulatory feature annotations, variant effect prediction (VEP), coordinate liftover, gene lookups, and cross-references. Use when the user needs to annotate variants with VEP (consequence, CADD, REVEL, SpliceAI), check Ensembl Regulatory Build overlap for ENCODE regions, convert coordinates between GRCh37 and GRCh38, resolve gene IDs (Ensembl ↔ symbol ↔ RefSeq), look up gene phenotype associations, or cross-reference ENCODE targets with Ensembl annotations. Also use when the user mentions Ensembl, VEP, variant effect predictor, liftover, assembly conversion, regulatory build, gene lookup, or cross-references between databases.
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。