Prepare

Prepare is an agent for coding agents from aktsmm/Agent-Skills. It costs 28 tokens per session (636 once invoked), scanned A, original, no licence file.

A conversion step that turns results from Azure Updates MCP into categories for a customer presentation. It does not unpack or analyse the source PowerPoint files.

In plain words
What is it for?
Preparing Azure update results for customer-deck categorisation.
Why use it?
It separates the classification input from the existing presentation workflow, so the source files are not handled as part of this step.

Agent

Install

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.

agentmods
npx agentmods add agents/aktsmm/agent-skills/prepare
Clone the repo
git clone --depth 1 https://github.com/aktsmm/Agent-Skills

Wrote 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.

agentmods badge for Prepare

README.md
[![agentmods](https://agentmods.dev/badge/agents/aktsmm/agent-skills/prepare.svg)](https://agentmods.dev/agents/aktsmm/agent-skills/prepare)
Your own site
<a href="https://agentmods.dev/agents/aktsmm/agent-skills/prepare"><img src="https://agentmods.dev/badge/agents/aktsmm/agent-skills/prepare.svg" alt="Measured on agentmods" height="20"></a>
Per session 28 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 636 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00028 $0.00636
Opus 5 $0.00014 $0.00318
Sonnet 5 $0.00006 $0.00127
Haiku 4.5 $0.00003 $0.00064

Measured 3d ago against content hash 6629f6da9c14, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Prepare 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 3d 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.

azure-update-customer-pptx/agents/prepare.agent.md · 61 lines

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.

Read it on GitHub

Changes

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.

  1. 3d ago First seen · 61 lines · 28 tokens per session scan A 6629f6da9c14

Subscribe to this mod's changes

Prepare is an agent published in the GitHub repository aktsmm/Agent-Skills (26 stars, last pushed today), with no licence file. It adds 28 tokens to every session and 636 once invoked, about $0.0001 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 2026-08-30.

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