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 h4vzz/awesome-ai-agent-skills --skill data-analysisgit clone --depth 1 https://github.com/h4vzz/awesome-ai-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/h4vzz/awesome-ai-agent-skills/data-analysis)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/data-analysis"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/data-analysis/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/h4vzz/awesome-ai-agent-skills/data-analysis"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/data-analysis.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 | $0.00022 | $0.01544 |
| Opus 5 | $0.00011 | $0.00772 |
| Sonnet 5 | $0.00004 | $0.00309 |
| Haiku 4.5 | $0.00002 | $0.00154 |
Grade A, and why
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 10d 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.
This is a copy
91% identical to data-analysis — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis
This skill enables an AI agent to perform rigorous statistical analysis on structured datasets. The agent loads data, computes descriptive and inferential statistics, identifies trends and correlations, tests hypotheses, and produces actionable insights. It supports CSV, Excel, Parquet, and JSON inputs and leverages pandas, scipy, and statsmodels for analysis.
Workflow
-
Load and profile the data. Read the dataset into a pandas DataFrame and inspect its shape, column types, and memory usage. Display the first and last rows to confirm the data loaded correctly. Check for obvious structural issues such as shifted columns or encoding problems.
-
Compute descriptive statistics. Generate summary statistics for all numeric columns including mean, median, standard deviation, skewness, and kurtosis. For categorical columns, compute value counts and mode. This step establishes a baseline understanding of each variable's distribution.
-
Identify trends and patterns. Apply rolling averages, percentage changes, and seasonal decomposition to time-indexed data. For non-temporal data, use group-by aggregations and pivot tables to surface patterns across categories. Flag any monotonic trends or cyclical behavior.
-
Perform correlation and hypothesis testing. Calculate Pearson and Spearman correlation matrices to quantify relationships between variables. Conduct hypothesis tests (t-tests, chi-square, ANOVA) where appropriate to determine statistical significance. Report p-values and confidence intervals alongside effect sizes.
-
Detect anomalies and outliers. Use the IQR method and z-scores to identify data points that deviate significantly from the norm. Cross-reference outliers with domain context to determine whether they represent errors, rare events, or meaningful signals.
-
Synthesize findings into a report. Summarize the key insights in plain language, supported by specific numbers. Rank findings by business impact or statistical significance. Include limitations and caveats such as sample size constraints or confounding variables.
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.
- 10d ago First seen · 124 lines · 22 tokens per session scan A f7bffef9d60f
data-analysis is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (34 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 1,544 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to data-analysis, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
cost-optimizer
Trigger when the user asks to audit Claude Code costs, reduce token spend, says "my Claude bill is too high", "optimize my CLAUDE.md", "why is this project burning tokens", or "/cost-optimizer". Scans a project for the common Claude Code cost leaks and returns a prioritized fix list.
excalidraw-architecture
Trigger when the user asks for an architecture diagram, says "draw the system", "update the architecture diagram", "give me an excalidraw of this codebase", or "/excalidraw-architecture". Generates or updates an Excalidraw JSON file at docs/architecture.excalidraw by reading the codebase's key entry points.
model-cost-compare
Trigger when the user asks which model to use, wants to compare model costs, says "what's cheapest for this task", "should I use Opus or Sonnet", "can a smaller model handle this", or "/model-cost-compare". Estimates token cost across Opus 4.6, Sonnet 4.6, GLM-5.1, Minimax M2.7, and local Gemma 4, then recommends the…
openclaw-debugger
Trigger when an OpenClaw agent is broken, silent, crashing, stuck, not responding, returning empty output, or the user says "my agent is down", "agent not working", "/openclaw-debugger". Walks through the standard OpenClaw 2026.4 diagnosis checklist and prints a report.
meeting-notes
Paste raw meeting notes and get a clean summary with key decisions and action items.
pricing-advisor
Describe your product and target customer, get a pricing strategy with three tiers and rationale.