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 agentmods add skills/aiopshwang/data-analysis-ml-agent-skills/using-data-analysisnpx skills add aiopshwang/data-analysis-ml-agent-skills --skill using-data-analysisgit clone --depth 1 https://github.com/aiopshwang/data-analysis-ml-agent-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/aiopshwang/data-analysis-ml-agent-skills/using-data-analysis)<a href="https://agentmods.dev/skills/aiopshwang/data-analysis-ml-agent-skills/using-data-analysis"><img src="https://agentmods.dev/badge/skills/aiopshwang/data-analysis-ml-agent-skills/using-data-analysis.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.00300 |
| Opus 5 | $0.00026 | $0.00150 |
| Sonnet 5 | $0.00011 | $0.00060 |
| Haiku 4.5 | $0.00005 | $0.00030 |
Grade A, and why
using-data-analysis 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 5d 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
Using Data Analysis Skills
Pick the narrowest skill that covers the current task. When the work spans multiple lifecycle stages, start from the orchestrator and let it call the others.
| Situation | Skill |
|---|---|
| An end-to-end project or an ambiguous modeling request | running-decision-grade-data-science |
| Data meaning, joins, labels, or ground truth may be untrustworthy | auditing-data-and-ground-truth |
| Designing splits, feature eligibility, baselines, or comparisons | designing-leakage-safe-experiments |
| A metric dropped, results disagree, or training-serving mismatch | diagnosing-ml-failures |
| Reviewing whether results support a claim or launch decision | validating-models-and-claims |
| Packaging finished work for independent reproduction | shipping-reproducible-results |
Each skill states its own non-goals in its description; respect them. The
orchestrator running-decision-grade-data-science already routes to the
other five at the right lifecycle stage, so do not stack it with them
manually for the same step.
What ships with it
1 file 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.
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.
- 5d ago First seen · 25 lines · 53 tokens per session scan A 346f4a24d410
using-data-analysis is a skill published in the GitHub repository aiopshwang/data-analysis-ml-agent-skills (12 stars, last pushed 9d ago), licensed MIT. It adds 53 tokens to every session and 300 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
eu-ai-act-readiness
Build a preliminary, evidence-based EU AI Act readiness assessment across AI-system inventory, territorial scope, operator roles, prohibited-practice screening, risk classification, transparency, high-risk controls, general-purpose AI obligations, governance, and implementation milestones. Use when an organization…
data-labeling
Set up and manage data labeling workflows using manual annotation tools, semi-automated pipelines, active learning, and programmatic weak supervision. Use when the user requests data labeling or provides relevant inputs for this workflow.
hyperparameter-tuning
Optimize machine learning model hyperparameters using grid search, random search, Bayesian optimization, and Hyperband to maximize model performance within a compute budget. Use when the user requests hyperparameter tuning or provides relevant inputs for this workflow.
model-training
Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.
context-injection
Place trusted contextual information into prompts or agent state using explicit boundaries, provenance, and templates. Use when relevant context has already been selected and must be inserted safely; use context-retrieval to find it or context-optimization to choose and order it.
knowledge-graph-creation
Build structured knowledge graphs from unstructured text by extracting entities, mapping relationships, generating graph triples, and visualizing the result. Use when the user requests knowledge graph creation or provides relevant inputs for this workflow.