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/few-shot-example-gen/SKILL.md · 1 file library/specializations/ai-agents-conversational/skills/few-shot-example-gen/SKILL.md 1.2 KB · Markdown Rendered Raw ⧉ Copy
name : few-shot-example-gen
description : Few-shot example generation and optimization for improved LLM performance
allowed-tools :
- Read
- Write
- Edit
- Bash
- Glob
- Grep
Few-Shot Example Generation Skill Capabilities Generate diverse few-shot examples Implement example selection strategies Optimize example ordering for performance Create dynamic example retrieval Design example formats for specific tasks Implement example quality validation Target Processes prompt-engineering-workflow intent-classification-system Implementation Details Example Selection Strategies Semantic Similarity : Select similar examplesMMR Selection : Diverse example selectionN-Gram Overlap : Lexical similarityRandom Sampling : Baseline selectionLength-Based : Control example sizesConfiguration Options Number of examples Selection algorithm Example format (input/output structure) Max token limits Example store backend Best Practices Cover edge cases in examples Balance example diversity Optimize example ordering Test with varied inputs Monitor token usage Dependencies langchain sentence-transformers (for semantic selection)