Borrowing it
Nothing to install: this file belongs to aget-framework/aget. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/aget-framework/aget/main/.claude/skills/aget-analyze-data/SKILL.mdgit clone --depth 1 https://github.com/aget-framework/agetWrote 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/aget-framework/aget/aget-analyze-data)<a href="https://agentmods.dev/skills/aget-framework/aget/aget-analyze-data"><img src="https://agentmods.dev/badge/skills/aget-framework/aget/aget-analyze-data.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 | $0.00013 | $0.00557 |
| Opus 5 | $0.00006 | $0.00279 |
| Sonnet 5 | $0.00003 | $0.00111 |
| Haiku 4.5 | $0.00001 | $0.00056 |
Grade A, and why
aget-analyze-data 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aget-analyze-data
Perform analysis on datasets to discover patterns, trends, and anomalies. Generate actionable insights from data.
Instructions
When this skill is invoked:
-
Profile the Dataset
- Identify data structure (fields, types, record count)
- Assess data quality (completeness, consistency)
- Note any limitations or caveats
-
Compute Statistics
- For numerical fields: mean, median, range, distribution
- For categorical fields: frequency counts, top values
- For temporal fields: trends, seasonality
-
Discover Patterns
- Identify correlations between fields
- Detect outliers and anomalies
- Find trends over time (if applicable)
-
Generate Insights
- Translate findings into actionable statements
- Prioritize by potential impact
- Distinguish correlation from causation
Output Format
## Data Analysis: [Dataset/Topic]
### Dataset Profile
| Attribute | Value |
|-----------|-------|
| Records | [N] |
| Fields | [N] |
| Date Range | [Start - End] |
| Quality | [Good/Fair/Poor] |
### Key Statistics
| Field | Type | Summary |
|-------|------|---------|
| [Field 1] | Numeric | Mean: X, Median: Y, Range: [A-B] |
| [Field 2] | Categorical | Top: [Value] (N%), [Value] (N%) |
### Patterns Discovered
1. **[Pattern Name]**: [Description]
- Evidence: [Data points supporting this]
- Confidence: [High/Medium/Low]
### Anomalies
- [Anomaly 1]: [Description and potential significance]
### Actionable Insights
1. **[Insight]**: [Recommended action based on finding]
### Limitations
- [Limitation 1]: [How it affects conclusions]
Constraints
- C1: NEVER present correlation as causation — statistical rigor required
- C2: NEVER ignore outliers without explicit justification — anomalies may be valuable
- C3: NEVER overfit conclusions to limited data — acknowledge sample limitations
Related
- SKILL-016: aget-analyze-data specification
- ONTOLOGY_analyst.yaml: Dataset, Analysis, Insight concepts
- CAP-ANL-001: Data Analysis capability
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.
- 6d ago First seen · 91 lines · 13 tokens per session scan A 9c8886fbf5ec
aget-analyze-data is a skill published in the GitHub repository aget-framework/aget (11 stars, last pushed today), licensed Apache-2.0. It adds 13 tokens to every session and 557 once invoked, about $0.0001 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 2026-08-30.
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