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bocodes1bocodes1/claude-skills/know-me/SKILL.md

know-me

Skill0 starsChanged 7 months agoMITView on GitHub

Always-on user preference learning - track communication patterns, decision style, domain knowledge, and personal context. Use every session (review user model), when user corrects behavior, or during memory maintenance

15 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use albert-dm, instantly-campaign and 9 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • yawningwhaleAISkill

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    Also in 5 projects that use this

  • yawningwhaleAISkill

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    Create a new Instantly cold-email campaign in the proven 5-email structure of the user's top-performing campaign (~16% reply rate). Use when the user wants to launch a new outbound campaign, write a cold email sequence, create an Instantly campaign, draft outreach for a new service or niche, or set up a sales sequence.

    Also in 6 projects that use this

  • RoxenRobloxSkill

    youtube-broll-maker

    Generate 10-second B-roll footage of yourself doing an action using the Higgsfield MCP (Seedance 2.0). Upload one photo of yourself, describe the action ("typing on laptop", "walking at night"), pick from 5 cinematography style presets, get an MP4. Use when the user wants B-roll, cutaway footage, "me doing X" video, YouTube B-roll, or any image-to-video of themselves performing an action.

    Also in 3 projects that use this

  • RoxenRobloxSkill

    youtube-popup-graphic

    Generate stunning Adobe-Premiere-Pro-style UI popup motion graphics for YouTube videos using the Higgsfield MCP. The user uploads a style reference image (any UI mockup, dashboard, glass card, design they love), names one skill/feature/item, and the skill produces an MP4 of a single card popping in styled to match the reference. Use when the user wants a "popup graphic", "popup animation", "card popup", "skill popup", "AE/Premiere-style animation", "stunning UI animation for YouTube", or says "make me a popup for <skill/feature/item> styled like <reference>".

    Also in 3 projects that use this

  • kronus-techSkill

    upwork-proposal

    Generate a tailored Upwork cover letter from a job posting. Invoke with the job posting text or URL. Auto-invoked when user says "cover letter", "upwork proposal", "apply to this job".

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  • op7418Skill

    youtube-clipper

    YouTube 视频智能剪辑工具。下载视频和字幕,AI 分析生成精细章节(几分钟级别), 用户选择片段后自动剪辑、翻译字幕为中英双语、烧录字幕到视频,并生成总结文案。 使用场景:当用户需要剪辑 YouTube 视频、生成短视频片段、制作双语字幕版本时。 关键词:视频剪辑、YouTube、字幕翻译、双语字幕、视频下载、clip video

    Also in 3 projects that use this

  • +4 more from yawningwhaleAI
  • +1 more from RoxenRoblox

The file

We split this skill into the rules it gives an agent

9 rules. 0 are common, 9 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 14 edited versions we read keep these.

  • 9

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    When user explicitly states a preference:

    line 51
  2. Distinctive:

    Update existing memory files rather than creating duplicates

    line 57
  3. Distinctive:

    After 3-5 interactions showing same pattern:

    line 60
  4. Distinctive:

    Don't log exact message times obsessively

    line 66
  5. Distinctive:

    If something feels too personal to write down, don't

    line 68
  6. Distinctive:

    Do adjust immediately: when explicitly told a preference

    line 74
  7. Distinctive:

    Do revert: if an adaptation doesn't land well

    line 75
  8. Distinctive:

    Save from success AND failure: If you only save corrections, you drift away from validated approaches

    line 76
  9. Distinctive:

    feedback memories: Every correction becomes a durable feedback memory

    line 81

“Common” is measured: kept by at least 6 in 10 of the 14 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Know-Me — User Preference Learning

Core Principle

Every interaction is a data point. Notice patterns. Remember preferences. Adapt silently.

This skill is always-on — its principles apply every session.

What to Track

Communication Preferences

  • Verbosity: Brief or detailed answers?
  • Tone: Casual, professional, technical, friendly?
  • Format: Lists vs paragraphs? Tables vs prose?
  • Follow-up style: Asks clarifying questions or wants you to just go?
  • Pet peeves: Things that annoy them

Decision Patterns

  • Risk tolerance: Conservative or experimental?
  • Speed vs quality: Ship fast or polish first?
  • Delegation style: "Just do it" or "let me review"?
  • Tool preferences: Preferred platforms, services, workflows

Domain Knowledge

  • Expertise areas: What they know well (don't over-explain)
  • Learning areas: What they're exploring (provide more context)
  • Projects: Active initiatives, goals, deadlines
  • Business context: Industry, role, stakeholders

Personal Context

  • Schedule patterns: Busy times, active hours
  • Interests: Topics they engage with enthusiastically
  • Important people: Names/relationships mentioned frequently

How to Learn

Passive Observation (Every Interaction)

Notice without being asked:

  • User shortens your responses -> they want brevity
  • User asks for more detail -> they want depth
  • User corrects your tone -> adjust and remember
  • User skips your suggestions -> those weren't valued
  • User praises something -> do more of that
  • User says "no not that" -> save as feedback memory immediately

Active Recording

When user explicitly states a preference:

  1. Acknowledge it naturally (don't make a big deal)
  2. Save as appropriate memory type:
    • Core preference -> user_*.md memory
    • Working style correction -> feedback_*.md memory
    • Project context -> project_*.md memory
  3. Update existing memory files rather than creating duplicates

Pattern Detection

After 3-5 interactions showing same pattern:

  • Promote from observation -> confirmed preference in memory
  • Example: User always says "keep it short" -> save as feedback memory about communication style

Privacy Rules

  • Track patterns, not surveillance data
  • Don't log exact message times obsessively
  • Focus on preferences that help you serve better
  • If something feels too personal to write down, don't
  • User can ask to see/edit/delete anything tracked

Adaptation Guidelines

  • Don't announce that you're adapting — just do it
  • Don't over-correct from a single data point — wait for patterns
  • Do adjust immediately when explicitly told a preference
  • Do revert if an adaptation doesn't land well
  • Save from success AND failure: If you only save corrections, you drift away from validated approaches

Integration Points

  • memory-management: Preference updates follow memory curation and pruning schedule
  • self-improvement: Partner feedback section in weekly review feeds back here
  • feedback memories: Every correction becomes a durable feedback memory
  • All skills: Observed delegation style affects how every skill operates

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