「preferences」の検索結果

Align language models with human preferences using Direct Preference Optimization — no reward model, no RL loop.

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。

Design memory for AI agents — short-term context, long-term stores, retrieval strategies, and memory-augmented reasoning. Use when an agent must remember facts, learn preferences, or stay coherent across sessions.

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。

Guides AI agents in using Tree Ring Memory for durable recall, project decisions, user preferences, warnings, future seeds, privacy-safe memory capture, and lifecycle-aware forgetting.

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。

Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md. Trigger: /caveman-compress FILEPATH or "compress memory 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。

Use this skill to create, configure, or tune Field Service Lightning scheduling policies — including work rules (pass/fail filters) and service objectives (weighted ranking criteria). Covers the four default policies, custom policy design, work rule type selection, and objective weighting strategy. NOT for running or tuning the Optimizer itself — use admin/fsl-scheduling-optimization-design. NOT for resource skills and preferences — use admin/fsl-resource-management. NOT for resource capacity records — use admin/fsl-capacity-planning.

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 admins are reducing Chatter signal-to-noise across an org — covers org-level Chatter Email Settings, per-user Email Settings preferences, group-level email digest frequency (Daily / Weekly / Every post / Never), bell-icon Custom Notifications vs Chatter feed-post tradeoffs, automated-feed suppression on record types via Feed Tracking config, and the Chatter Settings org defaults that control whether new users start with everything-on. Triggers: 'chatter feed full of automated posts nobody reads', 'users complain about chatter email volume', 'turn off chatter notifications for a specific group', 'change default chatter digest frequency', 'stop process builder posts from filling the feed', 'NotificationFrequency invalid value', 'ChatterEmailsMD.settings will not deploy', 'custom notification recipient limit'. NOT for Custom Notifications API design (use apex/apex-custom-notifications-from-apex), NOT for Connect API / Chatter REST API integration patterns (use apex/apex-connect-api-chatter), NOT for Chatter group lifecycle, archiving, ownership and unlisted-group governance (use admin/chatter-group-governance).

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Use when configuring or troubleshooting a Salesforce B2C Commerce (SFCC) storefront — including Business Manager site creation, SFRA cartridge path setup, customer groups, search index rebuilding, and key quota limits. Trigger keywords: SFCC, Commerce Cloud, Business Manager, storefront, cartridge, SFRA, site preferences, replication. NOT for B2B Commerce on Lightning (WebStore, BuyerGroup) — use admin/b2b-commerce-store-setup. NOT for choosing SFCC vs B2B Commerce — use architect/b2b-vs-b2c-architecture.

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。

@thedotmack's personal landing page design system. Use this skill whenever building landing pages, marketing sites, product pages, or conversion-focused web experiences. Covers visual hierarchy, UX patterns, marketing copy, messaging strategy, and conversion optimization based on proven preferences and successful projects.

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Persistent cross-session knowledge base. Manages per-project learnings that compound across sessions — patterns, pitfalls, preferences, and project quirks. The agent gets smarter on your codebase over time. At session start, searches relevant learnings. During sessions, logs discoveries. Supports review, search, prune, and export. Use proactively when the agent encounters a durable project quirk, user preference, or pattern that would save 5+ minutes next time. Also use at session start to recover context. Triggers: "remember this", "log this", "what have we learned?", "show learnings".

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Drafts jurisdiction-specific Advance Health Care Directive packages with agent appointment, living will instructions, HIPAA authorization bridge, organ donation preferences, and execution compliance checklists. Trigger when the user mentions advance directive, healthcare proxy, living will, health care agent, HIPAA authorization, medical power of attorney, end-of-life planning, capacity trigger planning, state-specific directive forms, witness/notary requirements for healthcare documents, or cross-state directive portability.

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Save important context to persistent memory for future sessions. PROACTIVELY USE THIS when you detect: - Decisions: "let's go with", "decided to", "chose", "went with" - Learnings: "I learned", "turns out", "TIL", "realized", "discovered" - Errors resolved: "fixed", "the problem was", "solution was" - Client info: names, emails, phone numbers, addresses, invoice amounts - Preferences: "I prefer", "I like", "don't like", "always use" - References: API keys locations, file paths, URLs, credentials - Patterns: "pattern", "convention", "always do", "standard approach" - Important commands run worth remembering Do NOT ask permission - just capture silently when these patterns appear.

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Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.

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Understand audience demographics, preferences, behavior patterns, and engagement quality across Facebook, Instagram, YouTube, and TikTok.

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Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md. Trigger: /caveman:compress <filepath> or "compress memory 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。

Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md. Trigger: /caveman:compress <filepath> or "compress memory 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。

This skill recommends that a multidisciplinary team, including an experienced endocrinologist, incorporates patient values and preferences while providing education about treatment options for Cushing’s syndrome. Use when planning treatment for a CS patient requiring coordinated care; triggers include persistent hypercortisolism after transsphenoidal surgery, consideration of bilateral adrenalectomy, radiotherapy, or medical therapy, and need for shared decision‑making.

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。

Recall project knowledge, past decisions, failures, and preferences when they can help a coding task. Capture changed files before responding.

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 sending notifications via mail, Slack, database, or custom channels — with queuing, on-demand recipients, and notification preferences.

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。

Run a two-agent proxy approval council for autonomous sprint gates. Use when a sprint needs delegated approval for human gates such as deploy, live mutation, push, cleanup, skip, or shortened watch decisions, while requiring two independent agents to reach consensus that the human principal would approve under written policy and preferences.

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。

Load this skill whenever the project supports light/dark mode, colour theme switching, high-contrast mode, or responds to prefers-color-scheme. Under no circumstances hard-code colours that break in alternative themes. Absolutely always test colour contrast in both light and dark themes, and respect user OS-level colour preferences via CSS media queries.

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Load this skill when configuring or reviewing CI/CD pipelines, GitHub Actions workflows, or automated testing setups. Ensures accessibility regressions are caught before code reaches production by enforcing quality gates, structured reporting, and layered automated + manual testing across all pages and user preferences.

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Use when designing, reviewing, or implementing Settings/Preferences for Apple-platform apps — what belongs in Settings vs Inspector/Toolbar, grouped forms, native controls, secure storage, launch at login, and destructive actions.

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。

Rewrite text that did not land, with clear context, plain language, and my writing preferences.

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Draft and revise evidence-grounded academic manuscripts with adaptable author preferences. Use for section writing, implementing structural edits, claim calibration, citation-integrated prose, prose cleanup, standalone review excerpts, and LaTeX template migration. Use paper-review for critique or author responses, and paper-figures-tables for final visual artifacts.

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。