adaptive-learner

Adaptive Learner

A meta-skill that enables continuous self-improvement by creating domain-specific learning skills. Each domain skill accumulates validated, actionable principles from user feedback.

How It Works

META-SKILL (this file)              DOMAIN SKILLS (generated)
Contains the METHODOLOGY    --->    Contains ACCUMULATED KNOWLEDGE
  - Discovery questions               - Domain profile
  - Analysis framework                 - Validated principles
  - Skill generation                   - Learning history

Workflow Decision Tree

1. Determine the operation type: Creating a new domain skill? --> Follow "Phase 1: Discovery" below Receiving feedback on existing domain? --> Follow "Phase 2: Feedback Analysis" below Producing a deliverable in a learned domain? --> Follow "Phase 3: Apply Principles" below

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Phase 1: Discovery (New Domain Skill Creation)

When the user wants to improve on a new domain, run this structured interview. Ask questions in batches of 2-3 MAX to avoid overwhelming the user.

Step 1 — Understand the Domain

Ask:

• "What domain do you want me to continuously improve on?"
• "What is the business context? Who is the target audience?"

Goal: Identify the domain name and its context.

Step 2 — Understand the Deliverable

Ask:

• "What exactly do I produce in this domain?" (video, text, code, recommendations...)
• "What are the format constraints?" (resolution, duration, file type, structure...)
• "What tools/stack do I use?" (Remotion, React, Python, plain text...)

Goal: Identify the deliverable type, format, and technical stack. The technical stack is critical — every principle must have an implementation in this stack.

Step 3 — Define Success

Ask:

• "How do we know if the output is successful or not?" (metrics, subjective score, client feedback...)
• "Who judges?" (user directly, metrics, both?)
• "How will you give me feedback?" (global score, annotations on specific parts, comparison of examples...)

Goal: Establish clear success criteria and feedback format.

Step 4 — Define Analysis Categories

Based on the domain, PROPOSE a list of analysis categories and ask the user to validate, reorder, add, or remove.

Example categories by domain:

• Video/VSL: Hook, Timing & Rhythm, Visual Composition, Copywriting, Effects & Animations, Audio, Narrative Structure
• Content curation: Relevance, Presentation format, Depth, Source quality, Actionability
• Sales coaching: Opening, Objection handling, Closing technique, Tone, Personalization
• Code review: Architecture, Readability, Performance, Error handling, Testing

Ask the user to prioritize them (priority #1 = most important for success).

Step 5 — Recap & Generate

Summarize all answers and ask for validation. Then generate the domain skill. See references/domain-skill-template.md for the exact structure to generate.

The domain skill must be placed in the current workspace or the user's skills directory.

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Phase 2: Feedback Analysis (Core Learning Loop)

When the user provides feedback (examples, scores, comments), follow this process:

Step 1 — Collect

Gather all feedback: scores, comments, comparisons, specific annotations.

Step 2 — Extract Observations

List every raw observation from the feedback. Be exhaustive.

Step 3 — Apply the 3-Level Filter (MANDATORY)

For EACH observation, apply these 3 filters in order:

Observation --> "Winning video uses red"
  |
  +- FILTER 1: CONTEXTUAL?
  |  "Would this change if the product/subject changed?"
  |   YES --> REJECT (color depends on product)
  |   NO  --> Continue
  |
  +- FILTER 2: GENERALIZABLE?
  |  "Can this be expressed as a rule WITHOUT referencing
  |   specific content?"
  |   NO  --> REJECT (too specific)
  |   YES --> Continue
  |
  +- FILTER 3: STRUCTURAL?
     "Is this about HOW (structure, timing, hierarchy)
      rather than WHAT (subject, color, specific image)?"
      NO  --> REJECT
      YES --> ACCEPT as principle

Step 4 — Formulate Validated Principles

For each accepted observation, create a principle with TWO LEVELS:

1. Conceptual level: The rule in plain language

   • Example: "No shot should remain static for more than 2 seconds"

2. Implementation level: The concrete code/config in the domain's tech stack

   • Example: <Sequence durationInFrames={60}> (2 sec at 30fps)
   • Include an anti-pattern when relevant (what NOT to do)

Step 5 — Evaluate Confidence

For each principle:

• How many positive examples confirm it?
• How many negative examples confirm it?
• Are there counter-examples?
• Assign confidence: 1 to 5 stars

Step 6 — Update the Domain Skill

Update references/principles.md in the domain skill:

• New principles: Add with initial confidence
• Existing principles: Increase/decrease confidence based on new evidence
• Contradicted principles: Add counter-example, lower confidence
• Log the session in references/learning-log.md

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Phase 3: Apply Principles (Producing a Deliverable)

When producing output in a domain where a learning skill exists:

1. Read references/principles.md from the domain skill
2. Apply all principles with confidence >= 3 stars
3. Prioritize by category order (as defined in domain-profile.md)
4. If a principle conflicts with the current context, flag it to the user
5. After delivery, remind the user they can provide feedback to improve future outputs

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Important Rules

• NEVER confuse contextual with structural: Colors, specific images, and subjects depend on the product. Timing, composition, hierarchy, and proportions are structural.
• Every principle needs BOTH levels: A conceptual understanding without implementation leads to bad code. Implementation without understanding leads to blind copy-paste.
• Anti-patterns are as valuable as patterns: Knowing what NOT to do prevents regression.
• Confidence is dynamic: A 5-star principle can drop if new evidence contradicts it.
• The domain profile is the source of truth: Always refer to domain-profile.md before analyzing feedback.