Score breakdown0 Version history1 Source
Category Weight Category score Contribution
Security prompt, exec, net, exfil, eval
35%
100
35.0 pts
Supply chain hash, typosquat, maintainer, lockfile
20%
100
20.0 pts
Maintenance staleness, pinning, CI
15%
100
15.0 pts
Transparency SKILL.md, perms, README
15%
100
15.0 pts
Community installs, verify, response
15%
100
15.0 pts
Findings & checks · 0 flagged
Security score 100 · 0 findings
✓ — All security checks passedNo findings in this category for the latest scan. pass
Supply chain score 100 · 0 findings
✓ — All supply chain checks passedNo findings in this category for the latest scan. pass
Maintenance score 100 · 0 findings
✓ — All maintenance checks passedNo findings in this category for the latest scan. pass
Transparency score 100 · 0 findings
✓ — All transparency checks passedNo findings in this category for the latest scan. pass
Community score 100 · 0 findings
✓ — All community checks passedNo findings in this category for the latest scan. pass
Every scanned point with the score it earned and what moved between them.
1 scans · 90 days d97a2e4 latest
Jun 23, 2026 100 d97a2e4
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
library/specializations/ai-agents-conversational/skills/llm-classifier/SKILL.md · 1 file library/specializations/ai-agents-conversational/skills/llm-classifier/SKILL.md 1.2 KB · Markdown Rendered Raw ⧉ Copy
name : llm-classifier
description : LLM-based zero-shot and few-shot classification for flexible intent detection
allowed-tools :
- Read
- Write
- Edit
- Bash
- Glob
- Grep
LLM Classifier Skill Capabilities Implement zero-shot classification with LLMs Design few-shot classification prompts Configure structured output for labels Implement confidence scoring Design classification taxonomies Handle multi-label classification Target Processes intent-classification-system dialogue-flow-design Implementation Details Classification Patterns Zero-Shot : No examples, description-basedFew-Shot : Example-based classificationStructured Output : JSON schema for labelsChain-of-Thought : Reasoning before classificationEnsemble : Multiple prompts/modelsConfiguration Options LLM model selection Label descriptions Example selection strategy Output format specification Confidence calibration Best Practices Clear label descriptions Representative examples Consistent output format Calibrate confidence scores Test with edge cases Dependencies langchain-core LLM provider