Borrowing it
Nothing to install: this file belongs to HaoZhang615/ads-copilot. 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/HaoZhang615/ads-copilot/main/.github/skills/foundry-ads-session/SKILL.mdgit clone --depth 1 https://github.com/HaoZhang615/ads-copilotWrote 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/haozhang615/ads-copilot/foundry-ads-session)<a href="https://agentmods.dev/skills/haozhang615/ads-copilot/foundry-ads-session"><img src="https://agentmods.dev/badge/skills/haozhang615/ads-copilot/foundry-ads-session.svg" alt="Measured on agentmods" 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 | $0.00148 | $0.02887 |
| Opus 5 | $0.00074 | $0.01443 |
| Sonnet 5 | $0.00030 | $0.00577 |
| Haiku 4.5 | $0.00015 | $0.00289 |
Grade A, and why
foundry-ads-session 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 8d 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Microsoft Foundry ADS Session
This skill provides domain-specific knowledge for Microsoft Foundry (Microsoft Foundry) to be used within an Architecture Design Session. The ADS methodology (persona, pacing, session structure, decision narration, trade-off framework, self-critique) is defined in the runtime system prompt. This skill supplies the Foundry-specific questions, patterns, components, and references that the methodology operates on.
Domain: Microsoft Foundry
This skill covers the Microsoft Foundry unified AI platform including:
- Resource Model: Foundry resource (
Microsoft.CognitiveServices/accountkind=AIServices) + project subresource; Classic Hub (Microsoft.MachineLearningServices/workspaceskind=Hub) + Project (legacy, still supported as Foundry Classic) - SDK / API:
azure-ai-projectsv1.0.0 (GA Jul 2025, Foundry Classic) and v2.0.0b4+ (Preview Feb 2026, new Foundry portal);azure-ai-inference,azure-ai-evaluation,azure-ai-agents; OpenAI-compatible client viaget_openai_client() - Agent Service: Multi-agent framework (MAF), hosted agents (LangGraph, custom frameworks), standard vs basic agent setup; Foundry MCP Server (
mcp.ai.azure.com) - Model Deployment: GlobalStandard, GlobalProvisionedManaged, GlobalBatch, DataZoneStandard, DataZoneProvisionedManaged, DataZoneBatch, Standard, ProvisionedManaged, DeveloperTier; MaaS (serverless) vs MaaP (managed online endpoint)
- Networking: Public, Private Link (
privatelink.cognitiveservices.azure.com), Managed VNet with outbound rules; agent subnet injection (RFC-1918 /24) - Identity & Security: Managed Identity (system/user-assigned), Entra ID,
DefaultAzureCredential; RBAC roles (Azure AI User, Azure AI Project Manager, Contributor); CMK encryption; Content Safety; Key Vault - Observability: Azure Monitor, Application Insights, OpenTelemetry tracing via
azure-ai-projects; token usage metrics, latency, evaluation scores - Enterprise Patterns: Landing zone integration, hub-spoke networking, multi-region design, dev/test/prod, governance, cost management, chargeback
What ships with it
21 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- foundry-ads-session/references/conversation-framework.md 9.9 KB
- foundry-ads-session/references/foundry-patterns.md 26 KB
- foundry-ads-session/references/industry-templates.md 9.1 KB
- foundry-ads-session/references/migration-patterns.md 9.3 KB
- foundry-ads-session/references/probing-questions.md 9.8 KB
- foundry-ads-session/references/readiness-checklist.md 6.2 KB
- foundry-ads-session/references/technical-deep-dives.md 7.9 KB
- foundry-ads-session/references/trade-offs-and-failure-modes.md 21 KB
- foundry-ads-session/scripts/generate_architecture.py 20 KB runs code
- foundry-ads-session/skill.json 425 B
- foundry-ads-session/SKILL.md 13 KB
- references/conversation-framework.md 9.9 KB
- references/foundry-patterns.md 26 KB
- references/industry-templates.md 9.1 KB
- references/migration-patterns.md 9.3 KB
- references/probing-questions.md 9.8 KB
- references/readiness-checklist.md 6.2 KB
- references/technical-deep-dives.md 7.9 KB
- references/trade-offs-and-failure-modes.md 21 KB
- scripts/generate_architecture.py 20 KB runs code
- skill.json 425 B
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.
- 8d ago First seen · 180 lines · 148 tokens per session scan A b2d7d1718bfc
foundry-ads-session is a skill published in the GitHub repository HaoZhang615/ads-copilot (2 stars, last pushed 6mo ago), licensed MIT. It adds 148 tokens to every session and 2,887 once invoked, about $0.0007 per session on Opus 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.
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