AI-generated skills fail in predictable ways:
- Over-fitted — Too specific to generalize
- Ignored — AI doesn't follow its own procedures
This isn't a model quality issue. It's a structural limitation. Here are 5 rules to fix it.
Why This Happens
AI cannot recognize "I wrote this" as a source of authority. Generation context ≠ execution context. The skill it created is just another document.
AI also struggles with generalization: extracting principles from examples, distinguishing "this is one case" from "this is the rule."
Rule 1: Use Meta-Skills to Compensate
Don't fix AI limitations in the same layer. Create separate skills that compensate.
Generalization Skill
Instead of "make this more general," pass the intent:
× "Make this more general"
○ "This skill will be used for [context]. Remove specifics that won't apply."
When AI understands why generalization matters, it judges the appropriate abstraction level.
Review Skill (Sequenced)
Make review mandatory by embedding it in a sequence:
Skill A (Generate) → Skill B (Generalize) → Skill C (Review)
You can't forget what's structurally enforced. Different AI instances checking each other bypasses the self-reference limitation.
Rule 2: Share the Goal, Run the Loop
Every skill follows this cycle:
Share Goal → Generate → Operate → Detect Problems → Share Problems → Solve
↑ ↓
└─────────────────────────────────────────────────────────────────┘
| Phase | Owner | Why |
|---|---|---|
| Share Goal | Human | Intent must come from humans |
| Generate | AI | Execution |
| Operate | AI | Execution |
| Detect Problems | Human | Judgment |
| Share Problems | Human | Coordination |
| Solve | AI | Once articulated, fixing is AI's work |
Human role: detection and articulation. Name the problem, AI solves it.
Rule 3: Keep Skills Under 100 Lines
When skills fail inconsistently, suspect context overflow:
Failure patterns:
├─ Skips specific steps → Extract those steps into separate skill
├─ Quality degrades toward the end → Split into parts
├─ Gets confused at conditionals → One skill per branch
└─ Random failures → Context overload
The rule: Keep skills under 100 lines.
This constraint forces good design. Can't fit in 100 lines? Multiple responsibilities — split. Too many conditionals? Separate by condition. Too many examples? You haven't generalized.
Unix philosophy: Do one thing well.
Rule 4: Write Meta-Information, Not Whitelists
A common mistake: listing every step.
× Whitelist approach
- Read the file
- Report errors
- Suggest fixes
This breaks on any scenario you didn't anticipate.
Instead, write meta-information:
○ Meta-information approach
Goal: Improve code quality
Priority: Readability > Performance
Constraint: Don't break existing APIs
| Approach | Known cases | Unknown cases |
|---|---|---|
| Whitelist (steps) | Works | Fails |
| Meta-info (intent) | Works | Can reason through |
AI generalizes intent better than procedures. Given a goal and judgment criteria, it handles edge cases. Given only steps, it's lost when something unexpected happens.
Rule 5: Design Around Limitations, Not Against Them
AI limitations aren't bugs to work around — they're design constraints to build with.
| Limitation | Design Response |
|---|---|
| Can't self-reference | Use separate instances to check each other |
| Can't generalize unprompted | Provide the "why" explicitly |
| Forgets steps | Make them structurally unforgettable |
| Context overflow | Smaller, focused units |
Stop asking AI to transcend its limitations. Design systems that don't require it to.
Summary
| Rule | Effect |
|---|---|
| 1. Use meta-skills | Compensates for self-reference gap |
| 2. Share goal, run loop | Continuous improvement without expecting perfection |
| 3. Under 100 lines | Prevents context overflow, enforces single responsibility |
| 4. Meta-info over whitelists | Handles unexpected cases |
| 5. Design around limitations | Systems that work with AI, not against it |
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