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 causify-ai/helpers --skill blog.write_ml_tutorialgit clone --depth 1 https://github.com/causify-ai/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/causify-ai/helpers/blog.write_ml_tutorial)<a href="https://agentmods.dev/skills/causify-ai/helpers/blog.write_ml_tutorial"><img src="https://agentmods.dev/badge/skills/causify-ai/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/causify-ai/helpers/blog.write_ml_tutorial"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/blog.write_ml_tutorial.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 3 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00398 |
| Opus 5.5 | $0.00006 | $0.00159 |
| Sonnet 5.5 | $0.00003 | $0.00080 |
| Haiku 4.5 | $0.00001 | $0.00040 |
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 1mo 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.
What it actually says
Goal
- Write a blog post about the use of a machine learning library or technique in a real-world application
- Target a technical audience with a background in machine learning and AI that is not necessarily familiar with the library or technique used
- Promote the use of the library and explain the benefits of using it in simple terms
- Explain why this library or technique is better than others for the given use case
Workflow
Write the Post
- Follow this structure:
- Title
- Subtitle: a short description of the post, written 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 is used to solve it
- Solution: explain the solution, use code snippets, output examples, and screenshots; keep it simple and concise so the reader can understand it without running the code
- Conclusion: summarize the benefits of the library or technique and why it is better than others for the given use case
- References: list the references used to write the post
- For each section, write a title that brings some interest while staying concise
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.
- 1mo ago Changed · +2 lines · -1 tokens per session b6fa6cdb95ad
- 1mo ago First seen · 47 lines · 15 tokens per session scan A 8c2f1f4a365e
blog.write_ml_tutorial is a skill published in the GitHub repository causify-ai/helpers (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 14 tokens to every session and 398 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 2026-08-30.
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