AG Kit is a toolkit that gives Google Antigravity coding agents a structured workspace with rules, skills, specialist agents, workflows, persistent memory, orchestration, MCP guidance, and a safety hook. It is for building and operating agent workflows in Antigravity, and its catalogue entries provide parts of that workspace contract.
Borrowing it
Nothing to install: this file belongs to vudovn/ag-kit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vudovn/ag-kit/main/.agents/skills/intelligent-routing/SKILL.mdgit clone --depth 1 https://github.com/vudovn/ag-kitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vudovn/ag-kit/intelligent-routing)<a href="https://agentmods.dev/skills/vudovn/ag-kit/intelligent-routing"><img src="https://agentmods.dev/badge/skills/vudovn/ag-kit/intelligent-routing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vudovn/ag-kit/intelligent-routing"><img src="https://agentmods.dev/badge/skills/vudovn/ag-kit/intelligent-routing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 308 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.02523 |
| Opus 5 | $0.00016 | $0.01262 |
| Sonnet 5 | $0.00006 | $0.00505 |
| Haiku 4.5 | $0.00003 | $0.00252 |
Grade A, and why
intelligent-routing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
4 near-identical copies found in the catalogue:
- intelligent-routing — 91% identical, 13 lines differ
- intelligent-routing — 88% identical, 14 lines differ
- intelligent-routing — 88% identical, 14 lines differ
- intelligent-routing — 88% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intelligent Agent Routing
Purpose: Automatically analyze user requests and route them to the most appropriate specialist agent(s) without requiring explicit user mentions.
Core Principle
The AI should act as an intelligent Project Manager, analyzing each request and automatically selecting the best specialist(s) for the job.
How It Works
1. Request Analysis
Before responding to ANY user request, perform automatic analysis:
graph TD
A[User Request: Add login] --> B[ANALYZE]
B --> C[Keywords]
B --> D[Domains]
B --> E[Complexity]
C --> F[SELECT AGENT]
D --> F
E --> F
F --> G[security-auditor + backend-specialist]
G --> H[AUTO-INVOKE with context]
2. Agent Selection Matrix
Use this matrix to automatically select agents:
| User Intent | Keywords | Selected Agent(s) | Auto-invoke? |
|---|---|---|---|
| Authentication | "login", "auth", "signup", "password" | security-auditor + backend-specialist |
✅ YES |
| UI Component | "button", "card", "layout", "style" | frontend-specialist |
✅ YES |
| Mobile UI | "screen", "navigation", "touch", "gesture" | mobile-developer |
✅ YES |
| API Endpoint | "endpoint", "route", "API", "POST", "GET" | backend-specialist |
✅ YES |
| Database | "schema", "migration", "query", "table" | database-architect + backend-specialist |
✅ YES |
| Bug Fix | "error", "bug", "not working", "broken" | debugger |
✅ YES |
| Test | "test", "coverage", "unit", "e2e" | test-engineer |
✅ YES |
| Deployment | "deploy", "production", "CI/CD", "docker" | devops-engineer |
✅ YES |
| Security Review | "security", "vulnerability", "exploit" | security-auditor + penetration-tester |
✅ YES |
| Performance | "slow", "optimize", "performance", "speed" | performance-optimizer |
✅ YES |
| Product Def | "requirements", "user story", "backlog", "MVP" | product-owner |
✅ YES |
| New Feature | "build", "create", "implement", "new app" | orchestrator → multi-agent |
⚠️ ASK FIRST |
| Complex Task | Multiple domains detected | orchestrator → multi-agent |
⚠️ ASK FIRST |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 338 lines · 32 tokens per session scan A d0ad66b14912
intelligent-routing is a skill published in the GitHub repository vudovn/ag-kit (8,169 stars, last pushed 2d ago), licensed MIT. It adds 32 tokens to every session and 2,523 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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