LoopyLuci Skill Relevance Scoring
Trigger: Use when multiple skills could match a task and you need to pick the best one(s) without loading them all.
Scoring Dimensions
Score each candidate skill from 0-10 on these axes:
| Dimension | What it measures | Weight |
|---|---|---|
| Semantic match | How closely the skill name/description matches the task | 3x |
| Trigger alignment | Whether the skill's trigger condition matches | 3x |
| Domain overlap | Category of skill matches task domain | 2x |
| Technology match | Tags, framework, or library matches | 2x |
| Task type match | Debugging, deploying, creating, analyzing | 1x |
| Complexity fit | Skill complexity aligns with task complexity | 1x |
Scoring Algorithm
Total Score = (semantic × 3) + (trigger × 3) + (domain × 2)
+ (tech × 2) + (task_type × 1) + (complexity × 1)
MAX = 120
RANKING:
90-120: EXACT MATCH — load immediately
60-89: STRONG MATCH — load if no exact match exists
30-59: WEAK MATCH — load only for complex tasks
0-29: NO MATCH — don't load
Real-World Example
Task: "Set up a CI pipeline that runs tests on PRs for my Python project"
Scanning skills_list():
| Skill | Semantic | Trigger | Domain | Tech | Type | Total |
|---|---|---|---|---|---|---|
| github-actions-workflows | 10 | 9 | 10 | 8 | 10 | 108 ✅ |
| ci-cd-pipeline-setup | 9 | 8 | 9 | 7 | 9 | 98 ✅ |
| github-actions-secrets | 5 | 3 | 7 | 4 | 4 | 55 ❓ |
| python-testing-advanced | 4 | 2 | 3 | 7 | 3 | 42 ❌ |
| docker-compose-patterns | 1 | 1 | 2 | 2 | 2 | 16 ❌ |
Decision: Load github-actions-workflows (108) + ci-cd-pipeline-setup (98). Skip the rest.
Description Scanning Heuristics
When reading skill descriptions (from skills_list), look for:
- Action verbs in the description: "Use when deploying..." → task is deployment
- Technology names: "docker", "kubernetes", "fastapi", "react"
- Process nouns: "migration", "debugging", "optimization", "testing"
- Exclusions: "without X" / "not for Y" — inverse matches
Quick Ranking (Low-Token Version)
When you have very limited context, use this abbreviated flow:
1. Read ALL skill descriptions once (skills_list = ~3K tokens)
2. Pick the TOP 3 that seem relevant
3. Load their full content with skill_view()
4. If the first one fits perfectly — stop there
5. If not — load the next one
Never load more than 5 skills for a single task.
Pitfalls
- Over-scoring popular skills: "python" skills score highly on any Python task even when inappropriate
- Missing cross-domain skills: A networking task might benefit from a security skill — scoring can miss this
- Score anchoring: The first high-scoring skill you see should not prevent considering combinations
- Description quality: A poorly described skill may be more useful than its score suggests — consider loading if category matches
Verification
After loading your top-scoring skill, answer:
- Does the first step actually address my task?
- If not, which other skill from my ranked list should I try next?