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 skills add gpsaggese/helpers --skill blog.write_ml_tutorialgit clone --depth 1 https://github.com/gpsaggese/helpersWrote 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/gpsaggese/helpers/blog.write_ml_tutorial)<a href="https://agentmods.dev/skills/gpsaggese/helpers/blog.write_ml_tutorial"><img src="https://agentmods.dev/badge/skills/gpsaggese/helpers/blog.write_ml_tutorial/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/skills/gpsaggese/helpers/blog.write_ml_tutorial"><img src="https://agentmods.dev/badge/skills/gpsaggese/helpers/blog.write_ml_tutorial.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 | $0.00014 | $0.00355 |
| Opus 5.5 | $0.00006 | $0.00142 |
| Sonnet 5.5 | $0.00003 | $0.00071 |
| Haiku 4.5 | $0.00001 | $0.00036 |
Grade A, and why
blog.write_ml_tutorial 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
Goal
- Write a blog post about using an ML library or technique in a real-world application
- Target a technical audience with an ML and AI background that may not know the library or technique
- Promote the library and explain its benefits in simple terms
- Explain why it is better than alternatives for the use case
Workflow
Write the Post
- Follow this structure:
- Title
- Subtitle: short description of the post, in first person
- Introduction: explain the library or technique and its benefits in simple terms
- Competitors: explain the main competitors and why this library may be better
- Problem Statement: explain the problem and how the library or technique solves it
- Solution: explain the solution with code snippets, output examples, and screenshots, simple enough to follow without running the code
- Conclusion: summarize the benefits and why it is better for the use case
- References: list the references used to write the post
- Give each section a concise, interesting title
Conventions
- Use Markdown and LaTeX for math equations
- Use Graphviz for diagrams
- Use the images referenced in the Markdown and Jupyter Notebook files in the project
Verification
- Confirm the post follows the required structure (Title, Subtitle, Introduction, Competitors, Problem Statement, Solution, Conclusion, References)
- Confirm code snippets and screenshots let the reader follow along without running the code
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 · 14 tokens per session scan A 0faa8b5fc4ba
blog.write_ml_tutorial is a skill published in the GitHub repository gpsaggese/helpers (158 stars, last pushed today), licensed Apache-2.0. It adds 14 tokens to every session and 355 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-11-06.
blog.write_ml_tutorial compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| blog.write_ml_tutorialgpsaggese/helpers | Skill | 158 | today | Apache-2.0 | A | 14 | Claude Code |
| agent-platform-model-registrygoogle/skills | Skill | 23k | today | Apache-2.0 | A | 60 | Claude Code, Codex |
| foundry-config-setupmicrosoft/agent-framework | Skill | 15k | today | MIT | A | 65 | Claude Code, Codex |
| google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skills | Skill | 23k | today | Apache-2.0 | A | 132 | Claude Code, Codex |
| training-checkwanshuiyin/Auto-claude-code-research-in-sleep | Skill | 19k | yesterday | MIT | A | 35 | Claude Code |
| nemo-automodel-launcher-configNVIDIA/skills | Skill | 3.9k | today | Apache-2.0 | A | 30 | Claude Code |
| finetuningmicrosoft/skills | Skill | 3.2k | yesterday | MIT | A | 132 | Claude Code |