serverless-genai-food-analyzer-app: Personalized nutritional app, serverless, tailored to your shopping and cooking needs, empowered by generative

by aws-samples · Agent Tool · ★ 96

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 64/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

amazon-bedrockanthropicawsaws-lambdabedrockcdkclaude-3claude-3-haikuclaude-3-sonnetgenai

Quick Facts

Stars96
Forks22
LanguageTypeScript
CategoryAgent Tool
LicenseMIT-0
Quality Score64/100
Open Issues21
Last Updated2026-04-06
Created2024-05-24
Platformsaws, claude-code, node
Est. Tokens~1629k

About serverless-genai-food-analyzer-app

Food analyzer app Food analyzer app is a personalized GenAI nutritional webapp for your shopping and cooking recipes built with serverless architecture and generative AI capabilities. It was first created as the winner of the AWS Hackathon France 2024 and then introduced at several AWS Summit.

Compatible Skills

These tools work well together with serverless-genai-food-analyzer-app for enhanced workflows:

  • MCP2Lambda — semantic(0.42)+complementary+shared_fw(anthropic)+rare_topics+similar_pop+shared_platform (71%)
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  • aws-mcp — semantic(0.37)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)
  • Lambda-MCP-Server — semantic(0.35)+complementary+rare_topics+similar_pop+shared_platform (56%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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