Borrowing it
Nothing to install: this file belongs to atherio-danp/cde-dotnetcc. 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/atherio-danp/cde-dotnetcc/main/.claude/skills/dotnet-ai-stack/SKILL.mdgit clone --depth 1 https://github.com/atherio-danp/cde-dotnetccWrote 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/atherio-danp/cde-dotnetcc/dotnet-ai-stack)<a href="https://agentmods.dev/skills/atherio-danp/cde-dotnetcc/dotnet-ai-stack"><img src="https://agentmods.dev/badge/skills/atherio-danp/cde-dotnetcc/dotnet-ai-stack/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/atherio-danp/cde-dotnetcc/dotnet-ai-stack"><img src="https://agentmods.dev/badge/skills/atherio-danp/cde-dotnetcc/dotnet-ai-stack.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 | $0.00076 | $0.01278 |
| Opus 5.5 | $0.00030 | $0.00511 |
| Sonnet 5.5 | $0.00015 | $0.00256 |
| Haiku 4.5 | $0.00008 | $0.00128 |
Grade A, and why
dotnet-ai-stack 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 56 lines · 76 tokens per session scan A 3e5f0c87797a
dotnet-ai-stack is a skill published in the GitHub repository atherio-danp/cde-dotnetcc (109 stars, last pushed 3mo ago), with no licence file. It adds 76 tokens to every session and 1,278 once invoked, about $0.0003 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.
Other skills, from other repositories
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…
azure-search-documents-ts
Build search applications using Azure AI Search SDK for JavaScript (@azure/search-documents). Use when creating/managing indexes, implementing vector/hybrid search, semantic ranking, or building agentic retrieval with knowledge bases.
convex-agent
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
wegent-knowledge
Knowledge base management and search tools for Wegent. Provides capabilities to list, create, update, and search knowledge bases and documents using RAG retrieval. Use this skill when the user wants to manage knowledge bases, documents, or search for information programmatically.
azure-search-documents-py
Full-text, vector, and hybrid search with AI enrichment capabilities.
rag-web-fallback
Only reach for external web search when the local corpus comes back empty or clearly insufficient. Forces the agent to try knowledge-rag first, then explicitly document why it needed to escalate. Prevents wasted API cost, latency, and (in air-gapped deployments) accidental network calls.