vincelele/ai-fomo-skills/ai-fomo-init

Claude Code and agent skill by vincelele/ai-fomo-skills

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Initialize a personal AI FOMO workspace and Personal Alignment Layer. Use when the user wants to set up an anti-AI-FOMO system, create a source intelligence workspace, configure long-term AI interests, define information value standards, or generate starter workspace templates for AI information judgment.

Install

npx vskill@latest install vincelele/ai-fomo-skills/ai-fomo-init

SKILL.md

AI FOMO Init

Purpose

Create a local Personal Alignment Layer and starter workspace. This skill aligns the agent before any source is summarized, collected, or filed.

Use When

Use this skill when the user asks to:

  • set up AI FOMO
  • initialize a personal AI alignment workspace
  • create an anti-FOMO knowledge workspace
  • configure what AI information is worth attention
  • generate starter folders and templates

Do Not Use When

Do not use this skill to:

  • summarize a specific source
  • collect from a source
  • write wiki/sources
  • generate a digest from existing material

Use ai-fomo-sources for source intake and ai-fomo for judgment and filing.

Core Workflow

  1. Choose or confirm the target workspace path.
  2. Ask the user for a profile brief: resume, bio, portfolio, LinkedIn-style summary, project history, or free-form notes.
  3. Extract a first-pass alignment map from the profile brief.
  4. Ask targeted QA to fill only the gaps that matter for AI information filtering.
  5. Copy the starter workspace from assets/starter-workspace/ if the user wants files created.
  6. Write confirmed alignment context into self-context/.
  7. Mark weak inferences as draft or pending confirmation.
  8. End with a short alignment contract summary for the user to review.

Two-Stage Alignment Intake

Stage 1: Profile Brief

Invite the user to provide one or more of:

  • resume or CV
  • personal bio
  • project list
  • portfolio notes
  • work history
  • current goals
  • examples of AI content they found valuable or useless

From this, extract only tentative alignment signals:

  • role and current work
  • long-term themes
  • recurring decisions
  • technical or product interests
  • likely source preferences
  • possible downrank patterns

Do not treat these as final until confirmed.

Stage 2: Targeted QA

Ask a small number of focused questions to close gaps. Prefer 3 to 7 questions.

Question areas:

  • what decisions the user wants AI information to improve
  • which AI topics are high priority now
  • which topics are interesting but lower priority
  • what kinds of content waste the user's time
  • what sources they trust or distrust
  • how aggressive the agent should be about skipping material
  • what output style is most useful

Do not ask a long onboarding survey. The goal is enough alignment to start, then improve through feedback.

Personal Alignment Layer

The minimum layer should answer:

  • who the agent is serving
  • what the user is building, studying, or deciding
  • which AI themes are high priority
  • which content types should be downranked
  • what counts as high-signal information
  • how feedback should change future filtering

If the user provides too little information, ask concise follow-up questions instead of overfitting.

Output Contract

When initialization finishes, report:

  • workspace path
  • files created or updated
  • confirmed alignment facts from the profile brief and QA
  • draft assumptions still requiring user review
  • next recommended action: connect sources or process the first source

Safety Rules

  • Do not write credentials into the workspace.
  • Do not copy private examples into templates.
  • Do not infer sensitive personal context unless explicitly provided.
  • Do not assume the user's interests match the author's interests.
  • Do not treat one-time task goals as long-term preferences.
  • Do not turn resume details into public examples or shareable templates.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
247
Repository forks
1
7-day trend score
28

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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