nitro-imessage-agent-template: Durable iMessage AI agent template

by vercel-labs · Agent Tool · ★ 56

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

Security audit verdict: CAUTION · quality 71/100

Flagged: opens a tunnel service. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

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.

agentai-agentai-sdkchat-sdkevlogimessagenitrovercelvercel-workflow

Quick Facts

Stars56
Forks4
LanguageTypeScript
CategoryAgent Tool
LicenseApache-2.0
Quality Score71/100
Last Updated2026-05-21
Created2026-05-05
Platformsnode
Est. Tokens~14k

About nitro-imessage-agent-template

nitro-imessage-agent A durable iMessage AI agent built on: Nitro v3 — the API server Chat SDK + — message routing over Sendblue Vercel AI SDK + AI Gateway — LLM replies, swap models with one constant Vercel Workflow — durable orchestration with retryable units evlog — structured wide-event logging with first-class AI SDK integration (token usage, tool calls, cost estimation) A user texts your...

Compatible Skills

These tools work well together with nitro-imessage-agent-template for enhanced workflows:

  • ai-sdk-provider-claude-code — semantic(0.31)+complementary+shared_fw(vercel)+rare_topics+same_lang+similar_pop+shared_platform (69%)
  • sdk — semantic(0.25)+complementary+shared_fw(vercel)+rare_topics+same_lang+similar_pop+shared_platform (66%)
  • ai-sdk-provider-codex-cli — semantic(0.21)+complementary+shared_fw(vercel)+rare_topics+same_lang+similar_pop+shared_platform (65%)

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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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