evo-fuzzy-match
SkillDev toolsFuzzy string matching and confidence calibration for mapping normalized text segments against codebook entries. Implements SequenceMatcher, Levenshtein, Jaccard, cosine similarity, ensemble scoring, and UNKNOWN thresholding.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the evo-fuzzy-match skill
What this skill tells your AI
The instructions your AI receives, as published by openlair/openskill in tasks-evolved/manufacturing-codebook-normalization/environment/skills/evo-fuzzy-match/SKILL.md and read by ahel’s review.
Fuzzy matching algorithms with calibrated confidence for manufacturing codebook matching.
Key Functions
levenshtein_distance(s1, s2)- Edit distancelevenshtein_ratio(s1, s2)- Normalized 0-1 similarityget_ngrams(text, n=2)- Character n-gramsjaccard_similarity(s1, s2, n=2)- Jaccard over n-gram setscosine_similarity(s1, s2, n=2)- Cosine over n-gram vectorsensemble_similarity(noisy, canonical)- 0.6SequenceMatcher + 0.4cosinecalibrate_confidence(raw_score, threshold=0.55)- Map to [0.5,1.0] or [0,0.49]match_segment(clean_text, codebook, standard_texts, threshold=0.55)- Match against codebook
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-fuzzy-match/scripts')
from utils import match_segment, ensemble_similarity, calibrate_confidence
Signals
- GitHub stars
- 89
- Forks
- 4
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
evo-fuzzy-match- Source
- github.com/openlair/openskill