「mind」の検索結果

A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title. Triggers include requests like \"analyze this paper\", \"critique this study\", \"is this a strong paper?\", \"give me similar studies\", \"prepare me for journal club\", \"help me understand this bioinformatics paper\", \"what are the weaknesses here?\", or \"turn this into a mind map\". Also activate for any downstream deliverables such as journal club kits, comparison tables, PI decision briefs, replication starters, or follow-up experiment designs. Do NOT treat as a generic summarizer — this skill performs structured evidence-type classification, track-specific critical appraisal, interpretation-boundary judgment, and research-grade follow-up generation.

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Recommend suitable prompts from 10,000+ Nano Banana Pro image generation prompts based on user needs. Optimized for Nano Banana Pro (Gemini), but prompts also work with Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E, Flux, Stable Diffusion, and any text-to-image AI model. Use this skill when users want to: - Generate images with AI (any model — Nano Banana Pro, Gemini, GPT Image, Seedream, etc.) - Find proven AI image generation prompts and prompt templates - Get prompt recommendations for specific use cases (portraits, products, social media, posters, etc.) - Create illustrations for articles, videos, podcasts, or marketing content - Browse a curated prompt library with sample images - Translate and understand prompt techniques Also available: "ai-image-prompts" skill — a model-agnostic version of this library for universal image generation.

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Reference for the TRL (Transformer Reinforcement Learning) library codebase. Use proactively before reading or editing any file under `trl/` so you have the intended contracts and invariants in mind, not just what the current code says. Covers trainer hierarchy (SFT, DPO, GRPO, KTO), shared utility functions (selective_log_softmax, decode_and_strip_padding, padding helpers), configuration system, model wrappers, and how data flows through any TRL trainer.

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Schedule reminders, recurring tasks, and one-time jobs using system crontab.

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Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something for future sessions.

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Faithfully archive public WPS/KDocs/金山文档 links, especially embedded ProcessOn .pof mind maps and canvases, as raw source data, original SVG/PNG, and Markdown. Use when a user gives a kdocs.cn or wps.processon.com link and asks to scrape, save, download, 扒下来, 归档, or 转 Markdown without logging in or saving the document to an account.

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Compare childcare options — nursery/daycare, childminder, nanny, family, or a mix — for your family's real needs, budget, and values. Use when asked to compare childcare options, nursery vs nanny, how to choose childcare, or find the right childcare. Produces a needs-and-values profile, a side-by-side of the realistic options on cost/flexibility/socialization/control, the questions to ask and red flags to check on visits, a total-cost read (including subsidies), and a decision that fits your priorities — not a generic ranking.

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Present a design to a client so they can make a decision rather than react to a picture — the brief restated, the moves explained against it, the options with their trade-offs, and a specific decision requested. Use when asked to present a design to a client, prepare a design review meeting, present concept options, or when a client keeps changing their mind after sign-off. Produces the presentation structure, the brief-to-design traceability, the options with honest trade-offs, the cost and programme position, and the decision record that makes sign-off stick.

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Use when the user asks "what's on my plate today", "catch me up", "morning brief", or wants a summary of their day across calendar, reminders and email. The single most common multi-step automation. Powered by LMCP on macOS and Windows.

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Use when packaging AISTATS code, data, proofs, simulation scripts, notebooks, random seeds, and logs as anonymous supplementary evidence or public post-acceptance artifacts, even when there is no separate artifact badge. Covers what statistically minded AISTATS reviewers inspect first and how to make Monte Carlo studies turnkey.

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Patterns for building entity actors with Akka.Hosting - GenericChildPerEntityParent, message extractors, cluster sharding abstraction, akka-reminders, and ITimeProvider. Supports both local testing and clustered production modes.

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Use when work crosses into Apple's system surfaces rather than your own UI or data — Siri and Shortcuts, widgets, Live Activities, in-app purchase, localization, privacy prompts, alarms, timers, calendar, reminders, contacts, SharePlay, VoIP and CallKit, accessory pairing, weather, Spotlight indexing, background tasks, Background Assets, push, or push on networks with no internet.

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Answer a question from a second-brain vault by reading its own pages, citing the pages used, and saying plainly when the vault does not contain the answer. Use this skill whenever the user asks what they know about a topic, what they have read on something, what connects two ideas, what they have changed their mind about, or asks any question that should be answered from their own notes rather than general knowledge. Do NOT use for ingesting new material, for general questions the vault has nothing to do with, or when the user explicitly wants an answer from the open web.

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Zoom In Down — a drop-in CSS keyframe animation from Animate.css (Hippocratic-2.1, © Daniel Eden). No JS required. Honors prefers-reduced-motion. Use restrained; attribute Animate.css.

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Design a mastery experience sequence that systematically builds student confidence in a skill they avoid. Use when students say 'I can't do this', avoid tasks, or show learned helplessness.

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Track rising, peaking, and fading crypto/tech narratives with quantitative mindshare + velocity signals and explicit positioning calls

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Before compaction Loopkit extracts decisions into claude-decisions.json (machine-readable). Read it alongside claude-progress.txt at session start — prose is for humans, JSON is for the loop.

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Reach into past AI conversation history through the `trellis mem` CLI. Use whenever the user asks 'how did we solve X last time', 'have we discussed this before', 'what was the decision on X', 'remind me what we did in this task', '上次怎么解的', '之前讨论过吗', '想起一段对话', or when starting a brainstorm that overlaps prior work, debugging a familiar bug, continuing a task across sessions, or doing a finish-work review. Returns raw past dialogue; decide for the moment whether to update spec, append to task notes, quote inline in the answer, or just internalize.

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Wrap up the current session: verify quality gate passed, remind user to commit, archive completed tasks, and record session progress to the developer journal. Use when done coding and ready to end the session.

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Claude Mind - Search and manage Claude's persistent memory stored in a single portable .mv2 file

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Manage Agentara scheduled tasks (cronjobs). Use when the user wants to create, list, update, or delete recurring scheduled tasks — triggered by phrases like "add a cronjob", "schedule a task", "remind me to do something after 10 minutes", "list my scheduled tasks", "remove cronjob", "update the schedule", etc.

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Design Microsoft Orleans systems from each primitive's purpose and failure model. USE FOR: grains, digital twins, state versus databases, transactions, messaging, streams, timers, reminders, Durable Jobs, stateless workers, grain services, startup, and hosting. DO NOT USE FOR: other actor stacks, batches, relational-only CRUD, or advice without an Orleans decision. INVOKES: inspect version and topology, choose the primitive, implement, and validate.

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Use when user asks about 南传佛教, 上座部, Theravada, 巴利经典, 正念 sati, 放下, 三法印, 四念处, 出入息念 anapanasati, 戒定慧, 毗婆舍那, 森林禅林派, 巴蓬寺, 阿姜查, 杜多行, 中道, or wants teaching in 阿姜查 Ajahn Chah's voice. Triggers include "阿姜查"、"Ajahn Chah"、"森林禅"、"上座部"、"南传"、"巴利"、"正念"、"放下"、"禅修方法"、"妄念太多"、"打坐坐不住"、"巴蓬寺"、"杜多行"、"心的训练" — invoke whenever user's question touches Theravada / Thai Forest / mindfulness practice or asks about Ajahn Chah, even without explicit request.

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This skill should be used when the user requests Mac automation via AppleScript, including: Mail operations, Reminders/Calendar management, Safari control, Finder operations, system controls (volume, brightness, notifications, app launching), third-party apps (development tools, task management, media players), or mentions "AppleScript" or "automate Mac".

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。