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 open-horizon-labs/repo-native-alignment --skill arch-diagramgit clone --depth 1 https://github.com/open-horizon-labs/repo-native-alignmentWrote 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/open-horizon-labs/repo-native-alignment/arch-diagram)<a href="https://agentmods.dev/skills/open-horizon-labs/repo-native-alignment/arch-diagram"><img src="https://agentmods.dev/badge/skills/open-horizon-labs/repo-native-alignment/arch-diagram/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/open-horizon-labs/repo-native-alignment/arch-diagram"><img src="https://agentmods.dev/badge/skills/open-horizon-labs/repo-native-alignment/arch-diagram.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 | $0.00044 | $0.00933 |
| Opus 5.5 | $0.00018 | $0.00373 |
| Sonnet 5.5 | $0.00009 | $0.00187 |
| Haiku 4.5 | $0.00004 | $0.00093 |
Grade A, and why
arch-diagram 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/arch-diagram
Generate an architecture diagram from RNA data. Teaches the traversal — the user picks the output format.
When to Use
When the user asks for an architecture diagram, system overview, service map, or component diagram of a repo that has an RNA index.
RNA Query Sequence
Execute these in order. Each step builds on the previous.
Step 1: Orientation (entry points, hotspots, outcomes)
repo_map(repo="<path>")
Entry points reveal deployment units (each entry point = a runnable service/script). Hotspot files reveal where complexity concentrates. Active outcomes reveal what the system is for.
Step 2: API Surface
search(query="", kind="api_endpoint", repo="<path>", limit=30, compact=true)
API endpoints reveal the contract between services. Group by file path to identify which deployment unit owns which routes.
Step 3: Frameworks (Infrastructure Stack)
search(query="", kind="framework", repo="<path>", limit=30, compact=true)
Each framework node has a language context. Group them: "this is a C#/ASP.NET service with npgsql" vs "this is a TypeScript/React frontend." LLMs know what each framework does — use that knowledge to identify infrastructure dependencies (databases, caches, message brokers, external APIs).
Step 4: Workspace and Languages
list_roots(repo="<path>")
Extract: root names, languages detected, framework counts. This confirms the language mix and workspace boundaries.
Step 5: Business Context
outcome_progress(repo="<path>")
If outcomes are declared, this reveals what the system is trying to achieve — annotate the diagram with business purpose, not just technical structure.
Step 6: External Service Calls (Optional — for deeper diagrams)
search(query="https://api.", repo="<path>", limit=20)
search(query="connection", kind="const", repo="<path>", limit=10)
String literal constants containing API URLs and connection strings reveal external dependencies and can distinguish different uses of the same service (e.g., OpenAI completions endpoint vs embeddings endpoint).
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 First seen · 102 lines · 44 tokens per session scan A bd756b336cc8
arch-diagram is a skill published in the GitHub repository open-horizon-labs/repo-native-alignment (6 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 933 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…