AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sickn33/agentic-awesome-skills --skill ai-agent-tool-routinggit clone --depth 1 https://github.com/sickn33/agentic-awesome-skillsWrote 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/sickn33/agentic-awesome-skills/ai-agent-tool-routing)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/ai-agent-tool-routing"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-agent-tool-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/sickn33/agentic-awesome-skills/ai-agent-tool-routing"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-agent-tool-routing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00036 | $0.01218 |
| Opus 5.5 | $0.00014 | $0.00487 |
| Sonnet 5.5 | $0.00007 | $0.00244 |
| Haiku 4.5 | $0.00004 | $0.00122 |
Grade A, and why
ai-agent-tool-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 yesterday.
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
2 near-identical copies found in the catalogue:
- ai-agent-tool-routing — 100% identical, 0 lines differ
- ai-agent-tool-routing — 100% identical, 190 lines differ
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Tool Routing Register
When to Use
- Define which tools an agent may call, their parameter schemas and runtime limits.
- Review a proposed tool registration before connecting it to an agent runtime.
- Document timeout, retry and authorization contracts for an existing router.
Scope and prerequisites
This skill produces a register and a verification plan. A register does not implement a router or establish that a tool is safe. Read the actual server/tool schema and the application's identity and permission model. Record observed configuration separately from proposed policy. Do not invoke registered tools, read credentials or enable a production registration merely to fill the table.
Workflow
- Identify a task, the calling principal/tenant, the intended tool and its read or write effect. Tool names, model output and descriptions are not authorization.
- Read the advertised schema and implementation boundaries. Define required fields, additional-field rejection, length/range limits and schema version. Prefer rejection to guessed or lossy coercion. Schema validation does not enforce row-level authorization, path containment or network restrictions.
- Describe identity propagation and server-side resource checks. Bind a request to the verified principal; never let a tool argument select an arbitrary tenant.
- Set measured/project-approved timeout, rate, concurrency, response-size and cost limits. Cancellation must propagate to downstream work where supported; returning a timeout alone does not prove a write did not finish.
- Classify retries by effect. Retry only classified transient failures within a bounded budget. A write requires a verified server-side deduplication contract and reconciliation for ambiguous completion; a random key alone is insufficient.
- Define adversarial and failure cases: missing/unknown arguments, wrong tenant, denied resources, unavailable tool, oversized response and late completion.
- Record actual test evidence and unresolved gaps. Keep status Draft until the required checks ran against the exact runtime and schema revision. A register label never grants execution permission.
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.
- yesterday Changed · -18 lines · +5 tokens per session c83227fece5c
- 13d ago First seen · 137 lines · 31 tokens per session scan A 3d16ef0181a2
ai-agent-tool-routing is a skill published in the GitHub repository sickn33/agentic-awesome-skills (47,686 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 1,218 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-03.
Other skills, from other repositories
ai-agent-tool-routing
Autonomous AI agent tool router register: schema registration, runtime parameter coercion, idempotency keys, and error recovery policies.
ai-agent-tool-routing
Use when designing or reviewing an AI agent tool registry or MCP router: document parameter schemas, caller authorization, execution limits, replay-safe retries and verification evidence.
anthropic-api
Integrate the Anthropic Messages API with system prompts, tool calls, streaming, and client error handling.
playwright
Use when the task requires capturing or automating a real browser from the terminal.
pydantic-ai
Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support.
prior-auth-packet-builder
Build a concise prior authorization packet from local case files and payer policy docs.