Use when the user mentions battery drain, energy optimization, power consumption audit, or pre-release energy check — timer abuse, polling patterns, continuous location, animation leaks, and background mode misuse.
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。
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概要と使いどころ
Use when the user mentions battery drain, energy optimization, power consumption audit, or pre-release energy check — timer abuse, polling patterns, continuous location, animation leaks, and background mode misuse.
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。
Turns a legal question, a set of draft questions, or source material such as a statute, jury instruction, rubric, contract checklist, or coding manual into a battery of narrow questions that TypeSafe's Jev, a fast classifier that returns probabilities rather than prose, can answer reliably across many documents. The skill breaks conclusions such as negligence, felony murder, or standing into element-level questions, writes the governing rule into each question, and delivers the questions alone, in Jev's request format or in plain English. Built for legal work that asks the same questions of many texts: coding statutes across jurisdictions, coding opinions or case files for empirical research, checking contracts against a review checklist, screening intake narratives, and grading student work against a rubric. Use when asked to write Jev questions, build a classifier for legal documents, or turn a legal test into yes-or-no questions. Running the questions requires Jev access; writing them does not.
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针对包含激励信号的10Hz采样电池数据,利用FFT(含去直流和加窗处理)计算特定频率的电化学阻抗并绘制奈奎斯特图。
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Workaround for Claude Code filtering BMP PUA Unicode (U+E000-U+F8FF). Supplementary PUA Nerd Font icons like (U+F0000+, e.g. nf-md-github, nf-md-kubernetes, nf-md-battery) can be written directly. BMP PUA icons (Powerline, Font Awesome, Devicons) require placeholder syntax like {{ U+E0A0 }} or {{ nf-fa-star }} (without spaces), which hooks auto-convert. Invoke when reading or writing Starship configs, tmux themes, shell prompts, or statuslines.
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End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.
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Think and work like an expert Electrochemist. Use when a task calls for Electrochemist judgment. Reason from interfacial thermodynamics and transport: Nernst sets equilibrium, Butler–Volmer sets kinetics, Levich/Randles–Ševčík set mass transport, and EIS deconvolves electrode and battery interphases.
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。
Test native, React Native, hybrid, and Flutter mobile apps with Appium 3.x, Detox, Maestro, and Patrol. Covers device farm setup (BrowserStack, Sauce Labs), gesture simulation, deep link and cold-start testing, push notifications, biometric (Face ID) auth, offline/poor-network simulation, and iOS/Android permission dialog handling. Use when: "mobile test," "Appium," "Detox," "Maestro," "Patrol," "Flutter test," "iOS test," "Android test," "device farm," "deep link," "biometric," "Face ID," "permission dialog," "React Native test." Not for: device/browser matrix strategy in the abstract — use cross-browser-testing; app startup/memory/battery profiling depth — use performance-testing; mobile screenshot diffing — use visual-testing. Related: ci-cd-integration, cross-browser-testing, performance-testing, test-data-management, test-reliability.
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Power systems engineering covering grid modeling, power flow analysis, energy storage dispatch, demand response, and electricity market economics. Spans transmission/distribution planning to real-time operations. Use when "power flow|load flow|grid model, energy storage|battery dispatch|ESS, demand response|load management|peak shaving, electricity market|LMP|locational marginal price, grid stability|frequency|voltage, capacity planning|resource adequacy, unit commitment|economic dispatch, transmission|distribution|power system, " mentioned.
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。
Decide which model, effort level, and cascade shape each subagent gets, and how to keep improvement loops safe (evaluator-as-selector, stop on regression). Routes on measured cost-per-completed-task rather than per-token price, because a tier's token count varies more by task shape than price varies across tiers. Covers per-model effort semantics, the concision lever, cascade preconditions, context handoff, and watching a subagent fan-out live. Use when choosing a subagent's model or effort, when choosing how to escalate a failed attempt, or when asked which model or effort a task should get. Grounded in a 2026-07 calibration, a 2026-08 coding-cost study, a 2026-09 seeded-bug repair battery, a 2026-10 Haiku 5.5 ladder priced per spawn, and a 2026-10 retry-versus-escalation ladder on 295 SWE-bench Verified tasks; Managed Agents API specifics are operational, not calibrated.
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Applies the reasoning of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications. Reach for this skill whenever the user is discussing self-powered systems, scaling distributed hardware, overcoming battery bottlenecks, or translating fundamental scientific phenomena (like static electricity or mechanical strain) into novel engineering applications. It is highly relevant for hardware roadmapping, optoelectronics, piezotronics, and challenging established scientific assumptions (like classical Maxwell's equations) to model dynamic systems.
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。
Bluetooth Low Energy (BLE) integration for Zephyr RTOS. Covers GATT Services/Characteristics, GAP advertising, connection parameters, power optimization strategies, and the professional Send-When-Idle design pattern. Trigger when adding BLE connectivity, optimizing battery life for wireless devices, or implementing custom GATT profiles.
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Power management and performance optimization for Zephyr RTOS. Covers system power states (Idle, Suspend, Off), device-level power management, residency hooks, and code/data relocation for speed efficiency. Trigger when optimizing battery life, reducing latency, or managing memory constraints.
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 adding, changing or reviewing Anabasis's Main Judge: the battery review, its recorded disagreements and the advice they feed. Keeps the Judge outside verification, evidence-bound, separately reported, and unable to alter pass, acceptance, claimability or adoption.
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。
Assesses energy transition investments with battery storage, grid modernization, EV charging, and hydrogen infrastructure analysis. Use when modeling energy transition assets, evaluating storage economics, or analyzing grid infrastructure.
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。
Mobile game development principles. Touch input, battery, performance, app stores.
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。
Background fetch, processing tasks, background location, headless JS, battery optimization - Expo and bare React Native
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。
BLE scanning/connecting/GATT operations with react-native-ble-plx, NFC tag reading/writing with react-native-nfc-manager, permissions, background mode, battery-efficient 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。
N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.
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。
Mobile-specific Unity optimization patterns for memory, battery, thermal, and 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。
Use when a task needs the judgment of a board-certified Clinical Neuropsychologist — building a neuropsychological battery to localize a brain-behavior deficit, adjudicating performance validity before interpreting any cognitive score, distinguishing a dementia subtype or mTBI sequela from a psychiatric or malingering cause, or writing a forensic, pre-surgical (epilepsy/tumor), or serial-retest report.
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Use when a task needs the judgment of a doctoral-level Clinical or Counseling Psychologist — building a psychological test battery to answer a referral question, interpreting a WAIS/MMPI/PAI profile against validity indices, working a differential diagnosis (e.g. ADHD vs. anxiety-driven inattention, depression vs. early dementia), or writing a feedback-session-ready diagnostic formulation report.
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 Adapted Physical Education Specialist — writing a legally-defensible PE present level and annual goal from motor/fitness assessment data, choosing the right test battery and alternate items for a student's mobility profile, recommending a placement step on the PE least-restrictive-environment continuum, or distinguishing a motor/balance deficit from a behavioral avoidance pattern.
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。
Profile and audit mobile app performance -- cold/warm/hot startup time, memory leaks, battery drain, network efficiency, frame rate jank, and binary size. Triggers: diagnosing slow launches, investigating ANRs, reducing app size, or benchmarking rendering 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。
Mobile game development principles. Touch input, battery, performance, app stores.
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。