A data-analysis workflow that answers questions ranging from a single metric to a detailed report. It can gather data from a connected data warehouse, which is a system that stores data for reporting and analysis.

Use it to look up metrics, investigate trends or drops, compare groups over time, or prepare a formal report for stakeholders.

It removes the need to plan every step of an investigation manually. It helps move from a question to the required data, analysis, and an appropriate output such as a number, table, chart, or report.

analyze — Skill for Claude Code by sun2443/designer-skills

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/sun2443/designer-skills

agentmods

npx agentmods add skills/sun2443/designer-skills/analyze

Made for: Claude Code.

Or install data, the plugin that ships this one along with the rest of its 10 skills.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/sun2443/designer-skills/analyze/github.svg)](https://agentmods.dev/skills/sun2443/designer-skills/analyze)

Your own site

<a href="https://agentmods.dev/skills/sun2443/designer-skills/analyze"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/analyze/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/sun2443/designer-skills/analyze"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/analyze.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 955 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 100% copy Near-identical to another mod in the catalogue.

Token cost

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	$0.00045	$0.00955
Opus 5.5	$0.00018	$0.00382
Sonnet 5.5	$0.00009	$0.00191
Haiku 4.5	$0.00005	$0.00096

Measured 13d ago against content hash 4ee816363d94, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

analyze 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 13d 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.

Origin

This is a copy

100% identical to analyze — 0 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.

data/skills/analyze/SKILL.md · 120 lines

How it starts

The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/analyze - Answer Data Questions

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Answer a data question, from a quick lookup to a full analysis to a formal report.

Usage

/analyze <natural language question>

Workflow

1. Understand the Question

Parse the user's question and determine:

Complexity level:
- Quick answer: Single metric, simple filter, factual lookup (e.g., "How many users signed up last week?")
- Full analysis: Multi-dimensional exploration, trend analysis, comparison (e.g., "What's driving the drop in conversion rate?")
- Formal report: Comprehensive investigation with methodology, caveats, and recommendations (e.g., "Prepare a quarterly business review of our subscription metrics")
Data requirements: Which tables, metrics, dimensions, and time ranges are needed
Output format: Number, table, chart, narrative, or combination

2. Gather Data

If a data warehouse MCP server is connected:

Explore the schema to find relevant tables and columns
Write SQL query(ies) to extract the needed data
Execute the query and retrieve results
If the query fails, debug and retry (check column names, table references, syntax for the specific dialect)
If results look unexpected, run sanity checks before proceeding

If no data warehouse is connected:

Ask the user to provide data in one of these ways:
- Paste query results directly
- Upload a CSV or Excel file
- Describe the schema so you can write queries for them to run
If writing queries for manual execution, use the sql-queries skill for dialect-specific best practices
Once data is provided, proceed with analysis

3. Analyze

Calculate relevant metrics, aggregations, and comparisons
Identify patterns, trends, outliers, and anomalies
Compare across dimensions (time periods, segments, categories)
For complex analyses, break the problem into sub-questions and address each

Read the full file on GitHub · 120 lines

Changes

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.

13d ago First seen · 120 lines · 45 tokens per session scan A 4ee816363d94

Subscribe to this mod's changes

analyze is a skill published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 45 tokens to every session and 955 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to analyze, differing in 0 lines, and is treated as a copy.

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