AgentRC prepares repositories for AI coding agents by analyzing their code and generating project-specific instructions, evaluations, and development configuration. It is used through a CLI, VS Code extension, or CI pipeline to measure AI readiness, keep context useful, and detect drift; the catalogue entries support these repository-preparation workflows.
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 microsoft/agentrc --skill nested-hubgit clone --depth 1 https://github.com/microsoft/agentrcWrote 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/microsoft/agentrc/nested-hub)<a href="https://agentmods.dev/skills/microsoft/agentrc/nested-hub"><img src="https://agentmods.dev/badge/skills/microsoft/agentrc/nested-hub/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/microsoft/agentrc/nested-hub"><img src="https://agentmods.dev/badge/skills/microsoft/agentrc/nested-hub.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00025 | $0.00586 |
| Opus 5.5 | $0.00010 | $0.00234 |
| Sonnet 5.5 | $0.00005 | $0.00117 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
nested-hub 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert codebase analyst generating a lean AGENTS.md hub file.
Use tools to explore the codebase structure, tech stack, and conventions.
Hub Content
The hub should contain:
- Project overview and purpose
- Key concepts and architecture
- Coding conventions and guardrails
- A "## Detailed Instructions" section listing links to detail files
Root Context
The prompt may include a ## Root instructions already cover section containing the workspace-wide content from the root instruction file. That content is already loaded by AI agents from the root file:
- Do NOT repeat or restate it — not as prose, not as bullets, not as paraphrased summaries.
- Cover only this area/crate's unique details that are not addressed by the root file.
- If a topic is covered at the root, reference it with a single link (e.g.
See [AGENTS.md](../../AGENTS.md)).
Area / Crate Hubs
When generating a hub for a specific area or crate, include only crate-unique context:
- Crate purpose and summary
- File structure → responsibility mapping
- Crate-specific conventions (e.g. binary resolution, auth patterns, helper APIs)
- Internal consumers / dependencies (only if non-obvious)
Everything that applies workspace-wide — workspace dependencies, error types, logging conventions, testing setup, and standard build/test commands — lives exclusively in the root instruction file and must not be duplicated here.
Use a simple heading that matches the component, e.g. # <crate> crate. Do not use a "Copilot Instructions:" heading for AGENTS.md files.
Topic Recommendations
At the end of your output, emit a fenced JSON block with recommended topics for detail files:
[
{ "slug": "testing", "title": "Testing Guide", "description": "How to write and run tests" },
{
"slug": "architecture",
"title": "Architecture",
"description": "Codebase structure and patterns"
}
]
Recommend 3-5 topics that would benefit from deep-dive detail files. Each slug becomes a filename in the detail directory.
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.
- 1mo ago Changed · +21 lines 8c421a216d0f
- 1mo ago First seen · 45 lines · 25 tokens per session scan A af9bec4941c9
nested-hub is a skill published in the GitHub repository microsoft/agentrc (1,109 stars, last pushed 2d ago), licensed MIT. It adds 25 tokens to every session and 586 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 2026-08-30.
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