Microsoft Multi-Agent Custom Automation Engine Solution Accelerator is a reference application for coordinating specialized AI agents to complete complex tasks from user input. Organizations use it as a starting point for automating business processes with Microsoft Agent Framework and Azure services. The catalogue entry provides an agent for working with this accelerator.
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/Multi-Agent-Custom-Automation-Engine-Solution-AcceleratorWrote 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/multi-agent-custom-automation-engine-solution-accelerator/speckit.contentpack)<a href="https://agentmods.dev/commands/microsoft/multi-agent-custom-automation-engine-solution-accelerator/speckit.contentpack"><img src="https://agentmods.dev/badge/commands/microsoft/multi-agent-custom-automation-engine-solution-accelerator/speckit.contentpack/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/multi-agent-custom-automation-engine-solution-accelerator/speckit.contentpack"><img src="https://agentmods.dev/badge/commands/microsoft/multi-agent-custom-automation-engine-solution-accelerator/speckit.contentpack.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 | $0.00010 |
| Opus 5 | $0.00000 | $0.00005 |
| Sonnet 5 | $0.00000 | $0.00002 |
| Haiku 4.5 | $0.00000 | $0.00001 |
Grade A, and why
speckit.contentpack 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 4d 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
Licensed MIT
The repository is licensed MIT, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 4d ago First seen · 4 lines · 0 tokens per session scan A 79a7a944ed37
speckit.contentpack is a command published in the GitHub repository microsoft/Multi-Agent-Custom-Automation-Engine-Solution-Accelerator (881 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 10 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 2026-09-08.
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review-and-refactor
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checklist
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clarify
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specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.