'preferences' 검색 결과

A 15-minute guided setup that teaches Cowork who you are, how you work, and what tools you use — so every result feels tailored instead of generic. Use when setting up Cowork for the first time, for personalizing your workspace preferences, during onboarding after your first session, when configuring your tools and context, or when the user says "set up cowork", "configure cowork", "why is cowork giving me generic results", "cowork setup", "personalize cowork", "make cowork know me", "cowork feels like chatgpt", or "how do I get better results from cowork".

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Set up a slushpile workspace. Ingests an existing resume or LinkedIn export, interviews the user to fill the gaps, then writes profile.md, preferences.yaml, and stories.md. Run once per workspace, before any other slushpile skill.

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Read every application.yaml in the workspace and report the state of the search — the ranked queue, what is waiting on you, what has gone quiet, and the regression of the pipeline's own predictions against actual outcomes. Writes the calibration findings back into job_search.md and preferences.yaml.

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"Run conjoint analysis to measure how product attributes drive consumer preferences and willingness to pay. Use this skill when the user needs to quantify feature value trade-offs, estimate willingness to pay for specific features, or optimize product configuration — even if they say 'which features do customers value most', 'willingness to pay for feature X', or 'product attribute trade-offs'.".

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

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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.

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Captures and evolves user preferences with confidence levels. Maintains Voice of the Customer model with exact language, dissatisfaction and aspiration signals. Handles promotion/demotion, global vs project scoping, auto memory system, and Omi wearable data integration.

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After architecture is complete, produces a flat actionable rules sheet for programmers — what you must do, what you must never do, per system and per layer. Extracted from all Accepted ADRs, technical preferences, and engine reference docs. More immediately actionable than ADRs (which explain why).

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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"

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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.

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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.

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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.

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@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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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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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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Use when a request needs external sources, current facts, listings, a supplied URL, papers, or library and API documentation. When using Firecrawl for the request, use firecrawl_search to find sources and firecrawl_scrape to read a supplied or discovered URL. Firecrawl also retrieves documents, collects structured data, and tracks website changes. Respect explicit source and tool preferences.

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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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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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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.

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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"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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

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