askill
continuous-learning-v2

continuous-learning-v2Safety --Repository

Protocol for the AI to self-analyze sessions and record 'instincts' (reusable patterns) for future improvement.

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Updated 1/27/2026

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SKILL.md

🧠 Continuous Learning Protocol (v2)

Goal: Transform temporary session knowledge into permanent "Instincts" to improve future performance.

πŸ“ The Instruction

When to run: At the end of every significant task or session, OR when the user types /learn.

Protocol Steps:

  1. Analyze Session History: Review the interaction between you and the user. Look for:

    • Corrections: "No, don't use X, use Y." -> Strong Signal
    • Preferences: "I prefer small commits." -> Medium Signal
    • Patterns: "We always mock the DB in tests." -> Project pattern
  2. Formulate Instinct: Create a concise "Instinct" entry following this format:

    ### [Instinct Title]
    - **Trigger**: When [situation occurs]...
    - **Action**: Always [do this specific thing].
    - **Confidence**: [High/Medium/Low] based on evidence.
    - **Source**: Session [Date/ID]
    
  3. Refine: Check against existing rules. Is this already covered? Is it a general best practice or specific to this user?

  4. Save:

    • Read: ~/.antigravity/instincts/learned.md (Create if missing).
    • Append: Add your new instinct to the file.
    • Report: Tell the user "I have learned a new instinct: [Title]."

πŸ’‘ Example

User: "Please stop using console.log, use our logger." AI Action:

  1. Detects correction regarding logging.
  2. Formulates Instinct:
    • Title: Use Project Logger
    • Trigger: When logging information.
    • Action: Use lib/logger instead of console.log.
    • Confidence: High (Explicit user instruction).
  3. Writes to learned.md.

🚫 What NOT to learn

  • One-off bugs.
  • Secrets or passwords (NEVER save these).
  • Temporary file paths.

Install

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Requires askill CLI v1.0+β–Ά

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Metadata

Licenseunknown
Version-
Updated1/27/2026
PublisherAtlasRoX

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observabilityprompting