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 agentmods add skills/alonf/mcppythondemo/foundry-runtime-auditnpx skills add alonf/MCPPythonDemo --skill foundry-runtime-auditgit clone --depth 1 https://github.com/alonf/MCPPythonDemoWhat 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 | $0.00000 | $0.00403 |
| Opus 5 | $0.00000 | $0.00201 |
| Sonnet 5 | $0.00000 | $0.00081 |
| Haiku 4.5 | $0.00000 | $0.00040 |
Grade A, and why
foundry-runtime-audit 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.
What it actually says
Foundry Runtime Audit
Purpose
Determine whether a codebase is truly using an Azure AI Foundry project/runtime abstraction or merely calling Azure OpenAI directly.
When to use
- A repo claims “Foundry” usage, but behavior is unclear
- Migration parity depends on matching auth/client/runtime semantics
- You need evidence from real code, not naming or branch assumptions
Procedure
-
Inspect package references
- C#: check
.csprojforAzure.AI.Projectsor project-runtime packages - Python: check
pyproject.toml/ requirements for Foundry/project SDKs
- C#: check
-
Inspect runtime construction
- Direct Azure OpenAI usually looks like:
- C#:
new AzureOpenAIClient(endpoint, credential).GetChatClient(deployment) - Python:
AzureOpenAI(azure_endpoint=..., api_version=...)
- C#:
- Foundry-project usage should create a project/client/runtime object, not just endpoint + deployment
- Direct Azure OpenAI usually looks like:
-
Check endpoint shape
*.cognitiveservices.azure.comusually indicates direct Azure OpenAI resource usage- Project-style config should expose explicit project/runtime concepts
-
Search the full repo history if needed
- Search for
Foundry,AIProject,Azure.AI.Projects, project-runtime types - Do not infer architecture from README wording alone
- Search for
-
Run the real entrypoint
- Confirm what the live path actually tries to construct
- Even if startup fails, early initialization often proves whether the path is direct Azure OpenAI or project-backed
Output template
- Current runtime type: direct Azure OpenAI / Foundry project runtime
- Evidence: package refs, constructor calls, endpoint form, live run notes
- Parity invariant: what downstream ports must match
- Gap call: whether the gap is in the port, or already in the source system
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 First seen · 43 lines · 0 tokens per session scan A a42232a0fda3
foundry-runtime-audit is a skill published in the GitHub repository alonf/MCPPythonDemo (0 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 403 tokens. 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.
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