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 Microck/ordinary-claude-skills --skill modalgit clone --depth 1 https://github.com/Microck/ordinary-claude-skillsWrote 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/microck/ordinary-claude-skills/modal)<a href="https://agentmods.dev/skills/microck/ordinary-claude-skills/modal"><img src="https://agentmods.dev/badge/skills/microck/ordinary-claude-skills/modal/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/microck/ordinary-claude-skills/modal"><img src="https://agentmods.dev/badge/skills/microck/ordinary-claude-skills/modal.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 | $0.00046 | $0.02455 |
| Opus 5 | $0.00023 | $0.01228 |
| Sonnet 5 | $0.00009 | $0.00491 |
| Haiku 4.5 | $0.00005 | $0.00246 |
Grade A, and why
modal 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 9d 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
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/api_reference.md 956 B
- references/examples.md 9.6 KB
- references/functions.md 4.4 KB
- references/getting-started.md 1.9 KB
- references/gpu.md 3.5 KB
- references/images.md 5.3 KB
- references/resources.md 2.6 KB
- references/scaling.md 5.2 KB
- references/scheduled-jobs.md 5.9 KB
- references/secrets.md 3.8 KB
- references/volumes.md 5.9 KB
- references/web-endpoints.md 6.8 KB
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.
- 9d ago First seen · 378 lines · 46 tokens per session scan A 2b3918692d5a
modal is a skill published in the GitHub repository Microck/ordinary-claude-skills (394 stars, last pushed 6d ago), with no licence file. It adds 46 tokens to every session and 2,455 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
modal
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.
ml-pipeline
Use when building or operating a machine learning pipeline. Covers feature engineering, training reproducibility, train/serve skew, deployment, monitoring for drift, and retraining.
infra-ragflow-ops
An operations guide for RAGFlow, an application that lets teams build systems that answer questions from a knowledge base. It covers service checks and the connected models, databases, vector stores, and file storage.
companion-clis
Companion CLIs for Runpod workflows — HuggingFace, GitHub, Docker, and AWS.
windows-local-ai-services
Run local AI services on Windows — port binding, firewall, WSL2 networking, and common pitfalls.
mlops-engineer
Provides MLOps patterns for ML CI/CD pipelines, model registries, monitoring, and data drift detection. Use when setting up ML infrastructure or when the user mentions MLOps, model deployment, ML pipeline, or model monitoring.