Model Mondays is a weekly Microsoft livestream and Discord-based learning series about choosing and using generative AI models. Developers and other AI practitioners use its roundups, spotlight sessions, demonstrations, and office hours to understand model fundamentals, compare options, and plan solutions. The catalogue contains an instruction related to the series.
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.
git clone --depth 1 https://github.com/microsoft/model-mondaysWrote 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/commands/microsoft/model-mondays/2-review-blog)<a href="https://agentmods.dev/commands/microsoft/model-mondays/2-review-blog"><img src="https://agentmods.dev/badge/commands/microsoft/model-mondays/2-review-blog/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/commands/microsoft/model-mondays/2-review-blog"><img src="https://agentmods.dev/badge/commands/microsoft/model-mondays/2-review-blog.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 | $0.00000 | $0.01728 |
| Opus 5.5 | $0.00000 | $0.00691 |
| Sonnet 5.5 | $0.00000 | $0.00346 |
| Haiku 4.5 | $0.00000 | $0.00173 |
Grade A, and why
2-review-blog 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 21d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blog Post Review Meta Prompt
You are a content reviewer for Model Mondays blog posts. Your task is to carefully review the provided blog post and verify that it contains all required elements with proper structure and formatting.
Important Context: Each blog post should reflect content from a specific episode of a given Model Mondays season (e.g., docs/season-02). The blog post should accurately represent the episode's topic, speakers, and key content covered during the livestream and AMA sessions.
Prerequisites Check
IMPORTANT: Before conducting any blog post review, you must first verify that MCP (Model Context Protocol) servers are properly configured to enhance the review process with Microsoft Learn documentation access.
Step 1: Check MCP Configuration
First, check if the .vscode/mcp.json file exists and is properly configured:
- If the file exists and contains the
microsoft-docs-mcpserver configuration, proceed with the review - If the file is missing or incomplete, STOP and instruct the user to run the MCP setup first
Step 2: MCP Setup Required
If MCP is not configured, display this message:
⚠️ MCP Setup Required
To provide the best blog post review with enhanced Microsoft Learn documentation access, please run the MCP setup prompt first:
Run this prompt:
.github/prompts/setup-mcp.prompt.mdThis will configure Model Context Protocol servers that enhance the review process with:
- Microsoft Learn documentation grounding
- GitHub repository context
- Better technical accuracy validation
Once MCP is set up, return to this blog review prompt.
Getting Started
Only proceed after MCP verification above.
Please provide the blog post you would like me to review. You can:
- Share the file path to the blog post in the workspace
- Paste the blog post content directly
- Provide a link to the blog post draft
Once you provide the blog post, I will conduct a comprehensive review using the checklist below.
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.
- 21d ago First seen · 194 lines · 0 tokens per session scan A 14feea7ccaf0
2-review-blog is a command published in the GitHub repository microsoft/model-mondays (301 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,728 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 2026-09-25.
Other commands, from other repositories
review
Review the current diff against AGENTS.md, agentdocs/, and REVIEW-CHECKLIST.md.
review
A review command that summarizes what changed, why it changed, and whether there are risks after files are modified. It can be run directly or triggered automatically after changes.
refactor
Systematic code refactoring with smart scope detection.
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.
init
Install the formatters this repository needs, with every command visible before it runs.
merge-conflict-analysis
You are analyzing merge conflicts for PR #${{ pr-number }}.