「aso」の検索結果

When the user wants to optimize their Google Play Store listing — title, short description, full description, keywords, ratings, or Play Store-specific features. Use when the user mentions "Google Play", "Android", "Play Store", "Play Console", "short description", "full description indexed", "Google Play ASO", or wants Google Play-specific keyword, creative, or ratings strategy. For iOS App Store optimization, see aso-audit and metadata-optimization.

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When the user wants to optimize App Store metadata — title, subtitle, keyword field, or description. Also use when the user mentions "optimize my title", "ASO metadata", "keyword field", "character limits", "app description", or "write my subtitle". For keyword discovery, see keyword-research. For full ASO audits, see aso-audit.

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。

Prepare for App Store review and prevent rejections. Covers App Store review guidelines, app rejection reasons, PrivacyInfo.xcprivacy privacy manifest requirements, required API reason codes, in-app purchase IAP and StoreKit rules, App Store Guidelines compliance, ATT App Tracking Transparency, EU DMA Digital Markets Act, HIG compliance checklist, app submission preparation, review preparation, metadata requirements, entitlements, widgets, and Live Activities review rules. Use when preparing for App Store submission, fixing rejection reasons, auditing privacy manifests, implementing ATT consent flow, configuring StoreKit IAP, or checking HIG compliance.

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Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).

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Genera tickets profesionales de soporte para Meta Business Suite / WhatsApp Business API optimizados para recibir prioridad URGENTE 24hr resolution. Úsala SIEMPRE que el usuario mencione crear un ticket a Meta, escalar un caso WABA, apelar un ban o restricción, reportar un false positive, abrir caso de soporte WhatsApp Business, problema de onboarding, rechazo de template, errores de Cloud API, OTP delivery, verification stuck, BM restricted, display name rejection, o cualquier issue de una WhatsApp Business Account. También activa cuando el usuario dice frases como "ayúdame a redactar un caso para Meta", "necesito reportar esto a soporte", "hay que escalar esto a WhatsApp", "ticket para Meta", o describe un problema de una cuenta de cliente de Vambe sin pedir ticket explícitamente pero claramente necesita uno. También activa ante preguntas del tipo "¿cómo apelar esto?" o "¿qué hago si Meta dice X?" relacionadas con WABA, BM o verificación.

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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.

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Extract typical annual GPP, annual evapotranspiration, mid-season LAI, mid-season root biomass, mid-season shoot biomass, median canopy height, maximum primary-root depth, Vcmax at 25oC, and Jmax at 25oC from EcoSIM h0 NetCDF output files. Use when asked to derive these EcoSIM output targets from a selected .ecosim.h0.*.nc file for model-output processing, trait checks, or benchmark summaries.

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Break a stuck or complex problem into the smallest sub-question that, if answered, unlocks the next step — then answer it and repeat until the path forward is clear. Load when a problem feels genuinely stuck, when reasoning keeps circling, or when deep-thinking diagnoses a Socratic frame. Also triggers on "I keep going in circles", "what is the real question here", "help me reason through this step by step", "unstick my thinking", "recursive questioning", "what is the root question". Based on the recursive Socratic questioning method (EMNLP 2023) which outperforms CoT and Tree-of-Thought on complex reasoning tasks.

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Atom of Thoughts (AoT) reasoning - decompose complex problems into atomic units with confidence tracking and backtracking. For genuinely complex reasoning, not everyday questions. Triggers on: atomise, complex reasoning, decompose problem, structured thinking, verify hypothesis.

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Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and clinical judgment improvement. NOT for basic medical knowledge, technical procedures, or non-clinical healthcare domains.

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Naturalistic causal reasoning for failures and disputed attribution. Use when postmortems or root-cause debates need reframing. NOT for causal inference, fault trees, or fully observed failures.

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 causal reasoning and mental model construction for diagnosis and prediction in complex domains

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Naturalistic causal reasoning for failures and disputed attribution. Use when postmortems or root-cause debates need reframing. NOT for causal inference, fault trees, or fully observed failures.

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Use for bounded reasoning, partial planning, commitment policy, deliberation cost, and reconsideration-threshold design instead of assuming free deliberation. NOT for exhaustive planning, static optimization, or domains where reconsideration cost is irrelevant.

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Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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One-shot chat completion against DeepSeek's `deepseek-chat` model via the OpenAI-compatible /v1/chat/completions endpoint. Reads DEEPSEEK_API_KEY from the environment; degrades gracefully (exit 0 with a JSON status:degraded envelope) when the key is missing or the API is unreachable. Use for non-reasoning tasks — summarization, extraction, quick classification — where deepseek-reasoner would be overkill.

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。

Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive 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。

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay 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。

When the user wants to audit or optimize an App Store or Google Play listing. Also use when the user mentions 'ASO audit,' 'app store optimization,' 'optimize my app listing,' 'improve app visibility,' 'app store ranking,' 'audit my listing,' 'why aren't people downloading my app,' 'improve my app conversion,' 'keyword optimization for app,' or 'compare my app to competitors.' Use when the user shares an App Store or Google Play URL and wants to improve it.

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 the user wants Reasonix to build, refine, test, or validate a CLI-Anything harness for a GUI application or source repository. Adapts the CLI-Anything methodology to Reasonix without changing the generated Python harness format.

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Applies the DHDNA framework as an exploratory rubric for reasoning and writing patterns in supplied text. Used for explicit requests for DHDNA, cognitive-style reflection, a thinking-pattern profile, or comparisons of textual reasoning. Scores describe evidence in the sample, not validated psychological traits or personal identity.

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Seasonal/calendar-effect strategy. Generates trading signals from time-based patterns such as month-of-year effects and day-of-week effects. Suitable for any OHLCV data.

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Build collaborative forensic incident timelines using Timesketch to ingest, normalize, and analyze multi-source event data (including Plaso output) for attack chain reconstruction and investigation documentation. Use when reconstructing the sequence of events during an incident investigation or when multiple analysts need to jointly tag, annotate, and search a shared DFIR timeline.

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。