Learning Capture
Overview
This skill enables continual learning by recognizing valuable patterns during work and capturing them as new skills. It focuses on high-ROI captures: patterns that will save significant context window tokens through frequent reuse.
Recognition Framework
Monitor for these five types of learning moments:
1. Novel Problem-Solving Approaches
Trigger: Develop a creative, non-obvious solution to a complex problem that could apply to similar future problems.
Strong signals:
- Solution required multi-step reasoning or novel tool combinations
- Approach is generalizable beyond this specific instance
- User expresses satisfaction with the results
- Similar problem type likely to recur
2. Repeated Patterns
Trigger: User requests similar tasks 2-3 times and a consistent approach emerges.
Strong signals:
- Pattern has repeated 2+ times with consistent structure
- User asks "can you do the same thing as before?"
- Task type is clearly ongoing (e.g., weekly reports, monthly communications)
- Each instance requires re-explaining the approach
3. Domain-Specific Knowledge
Trigger: User explains company processes, terminology, schemas, or standards that span multiple conversations.
Strong signals:
- Information accumulates across 2+ conversations
- Knowledge is stable (won't change weekly)
- User frequently asks questions in this domain
- Re-explaining costs 1000+ tokens each time
4. Effective Reasoning Patterns
Trigger: Discover a particular way of structuring thinking that consistently produces better results.
Strong signals:
- Pattern applies to a category of problems, not just one instance
- Results are notably better than simpler approaches
- Structure is teachable and reproducible
- Problem category recurs frequently
5. Workflow Optimizations
Trigger: Figure out an efficient way to chain tools or steps together that produces comprehensive results.
Strong signals:
- Workflow chains 3+ distinct steps
- Pattern generalizes to similar task types
- User appreciates the thoroughness
- Similar workflows likely needed regularly
Decision Framework
Offer capture when ALL of the following are true:
- High confidence (>95%) of significant ROI:
- Pattern will be reused 10+ times across future conversations - Each reuse saves 500+ tokens of re-explanation - The skill itself costs <5000 tokens to load
- Strong reusability signal present:
- Pattern has repeated 2+ times already, OR - User explicitly indicates ongoing need ("I do this weekly"), OR - Complex domain knowledge worth formalizing, OR - Novel workflow with clear generalizability
- Not redundant with existing capabilities:
- No existing skill already covers this pattern - Adds meaningful value beyond general knowledge
Do NOT offer capture when:
- First instance of a pattern (wait for repetition)
- Highly context-specific solution (won't generalize)
- Simple task using existing capabilities (no marginal value)
- Creative/one-off work (low reuse probability)
- Ambiguous reusability (unclear if it will recur)
Consult references/decision-examples.md for concrete examples of high-confidence vs. low-confidence scenarios.
Capture Process
Step 1: Recognize the Learning Moment
While working, monitor for recognition triggers from the framework above. Track:
- Is this a repeated pattern?
- Does this generalize beyond this instance?
- Would formalizing this save significant tokens in future uses?
Step 2: Evaluate Against Decision Framework
Before offering capture, verify:
- ROI calculation: (Expected_reuses × Tokens_saved) >> Skill_cost
- Strong reusability signal is present
- Not redundant with existing capabilities
If all checks pass, proceed to offer. If uncertain, do NOT offer.
Step 3: Offer Capture Conservatively
Timing: Offer after completing the immediate task, not mid-task.
Phrasing: Be concise and specific about what would be captured and why it's valuable.
Good examples:
- "I notice I've structured the last three internal comms documents similarly. Would it be helpful to capture this as a skill for future communications?"
- "I've built up understanding of your data architecture across our conversations. Should I formalize this as a skill for more efficient future reference?"
- "The validation workflow I developed seems applicable to your other messy datasets. Worth capturing as a skill?"
Avoid:
- Over-explaining the decision reasoning
- Offering when confidence is <95%
- Interrupting task flow to offer
Step 4: Structure the Draft Skill
When user agrees to capture, create a draft skill file following these steps:
- Select appropriate template from references/skill-templates.md based on learning moment type
- Structure the skill using the template as a guide
- Keep it concise: Focus on what's non-obvious and reusable
- Include specific triggers: Make it clear when to use this skill
- Add examples where helpful for clarity
- Save to outputs: Create the draft at
/mnt/user-data/outputs/[skill-name].skill/
The draft skill should be ready for user review and upload with minimal editing needed.
Step 5: Present the Draft
After creating the draft skill:
- Provide context: Briefly explain what the skill captures and why it will be valuable
- Highlight key sections: Point out the most important parts of the skill
- Suggest refinements: Note any areas where user input would improve the skill
- Explain next steps: User reviews, potentially edits, then uploads via the UI for future conversations
Key Principles
Conservative by default: Better to capture 80% of truly valuable patterns than create noise. Only offer when confidence is very high.
ROI-focused: Prioritize patterns with high reuse frequency and high token savings per reuse.
Context window awareness: Skills cost tokens to load. A skill should pay for itself within 10 uses.
Interpretable: Skills are plain text and easy to review, correct, and refine. This transparency is a feature.
User-controlled: The manual upload step ensures quality control and user agency over what gets added to the knowledge base.
Resources
references/skill-templates.md
Templates for structuring different types of skills based on the learning moment type. Includes:
- Workflow/Process skill template
- Domain Knowledge skill template
- Task Pattern skill template
- Reasoning/Prompt Pattern skill template
- Template selection guide
Read this file when structuring a captured skill to use the appropriate template.
references/decision-examples.md
Detailed examples of high-confidence capture scenarios (where to offer) and low-confidence scenarios (where NOT to offer). Includes:
- Concrete examples with signal analysis
- Recognition pattern checklists
- Decision threshold guidelines
- ROI calculation examples
Read this file when uncertain whether a learning moment meets the capture threshold.