Code Engineer
openJiuwen-ai/sciencediscovery
A skill your agent uses when you need to write and execute Python/R code to process, transform, and analyze data, delivering reproducible computational results with complete code-level methodology…
A full analysis pipeline where an agent team collaborates to perform exploratory data analysis (EDA), data cleaning, statistical analysis, visualization, and report writing.
Install Data Analysis
$ npx skills add revfactory/harness-100 --skill data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install revfactory/harness-100 data-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .claude/skills && cp -r skills-src/en/32-data-analysis/.claude/skills/data-analysis .claude/skills/data-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "data-analysis" agent skill from https://github.com/revfactory/harness-100/tree/main/en/32-data-analysis/.claude/skills/data-analysis into .claude/skills/data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/revfactory/harness-100/tree/main/en/32-data-analysis/.claude/skills/data-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add revfactory/harness-100 --skill data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install revfactory/harness-100 data-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .agents/skills && cp -r skills-src/en/32-data-analysis/.claude/skills/data-analysis .agents/skills/data-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "data-analysis" agent skill from https://github.com/revfactory/harness-100/tree/main/en/32-data-analysis/.claude/skills/data-analysis into .agents/skills/data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add revfactory/harness-100 --skill data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install revfactory/harness-100 data-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/en/32-data-analysis/.claude/skills/data-analysis .cursor/skills/data-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "data-analysis" agent skill from https://github.com/revfactory/harness-100/tree/main/en/32-data-analysis/.claude/skills/data-analysis into .cursor/skills/data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/revfactory/harness-100.git --path en/32-data-analysis/.claude/skills/data-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add revfactory/harness-100 --skill data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install revfactory/harness-100 data-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/en/32-data-analysis/.claude/skills/data-analysis .gemini/skills/data-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "data-analysis" agent skill from https://github.com/revfactory/harness-100/tree/main/en/32-data-analysis/.claude/skills/data-analysis into .gemini/skills/data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install revfactory/harness-100 data-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add revfactory/harness-100 --skill data-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .github/skills && cp -r skills-src/en/32-data-analysis/.claude/skills/data-analysis .github/skills/data-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "data-analysis" agent skill from https://github.com/revfactory/harness-100/tree/main/en/32-data-analysis/.claude/skills/data-analysis into .github/skills/data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add revfactory/harness-100 --skill data-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install revfactory/harness-100 data-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/en/32-data-analysis/.claude/skills/data-analysis .opencode/skills/data-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "data-analysis" agent skill from https://github.com/revfactory/harness-100/tree/main/en/32-data-analysis/.claude/skills/data-analysis into .opencode/skills/data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
data-analysis- GitHub stars
- 1.3k
- Token cost
- ~1.9k tokens
- SKILL.md length
- 723 words
- Files
- 1
- Skills in repo
- 464
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A full analysis pipeline where an agent team collaborates to perform exploratory data analysis (EDA), data cleaning, statistical analysis, visualization, and report writing.
- Works in 3 steps: Preparation (Orchestrator performs… → Team Assembly and Execution → Integration and Final Outputs
- Analyze this data
- SKILL.md covers Execution Mode, Agent Composition, Workflow and Scale-Based Modes, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Data Analysis is an agent skill from revfactory/harness-100. A full analysis pipeline where an agent team collaborates to perform exploratory data analysis (EDA), data cleaning, statistical analysis, visualization, and report writing. Use this skill for 'analyze this data', 'do EDA', 'exploratory analysis', 'statistical analysis', 'data visualization', 'write an analysis report', 'analyze CSV', 'extract data insights', 'data cleaning', 'outlier analysis', and other data analysis tasks. Note: real-time data streaming, ML model training/deployment, and BI dashboard server…
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Data analysis, Statistics and Data cleaning. The licence is Apache-2.0.
When your agent uses it
- Analyze this data
- Exploratory analysis
- Statistical analysis
- Data visualization
Example prompts
- “analyze this data”
- “do EDA”
- “exploratory analysis”
- “/data-analysis”
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 8e8d35c. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Data Analysis loads about 1.9k tokens when it runs. Until then it costs about 144 tokens; SKILL.md has 723 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from revfactory/harness-100 at commit 8e8d35c, republished under its Apache-2.0 licence (© revfactory). 723 words, ~1,907 tokens.
Download SKILL.mdSave it as
.claude/skills/data-analysis/SKILL.md (or your agent's skills folder).- name
- data-analysis
- description
- A full analysis pipeline where an agent team collaborates to perform exploratory data analysis (EDA), data cleaning, statistical analysis, visualization, and report writing. Use this skill for 'analyze this data', 'do EDA', 'exploratory analysis', 'statistical analysis', 'data visualization', 'write an analysis report', 'analyze CSV', 'extract data insights', 'data cleaning', 'outlier analysis', and other data analysis tasks. Note: real-time data streaming, ML model training/deployment, and BI dashboard server construction are outside this skill's scope.
Data Analysis — Full Data Analysis Pipeline
An agent team collaborates to perform the full data lifecycle: exploration → cleaning → analysis → visualization → reporting.
Execution Mode
Agent Team — 5 members communicate directly via SendMessage and cross-validate.
Agent Composition
| Agent | File | Role | Type |
|---|---|---|---|
| explorer | .claude/agents/explorer.md | Exploratory analysis, data profiling | general-purpose |
| cleaner | .claude/agents/cleaner.md | Data cleaning, transformation pipeline | general-purpose |
| analyst | .claude/agents/analyst.md | Statistical analysis, insight derivation | general-purpose |
| visualizer | .claude/agents/visualizer.md | Chart design, visualization code generation | general-purpose |
| reporter | .claude/agents/reporter.md | Final report writing, quality verification | general-purpose |
Workflow
Phase 1: Preparation (Orchestrator performs directly)
- Extract from user input:
- Data Source: File path, format (CSV/Excel/JSON/DB), size
- Analysis Purpose: Business questions, hypotheses, expected results
- Constraints (optional): Time, specific analysis techniques, reporting audience
- Domain Information (optional): Industry, variable meanings, business context
- Create
_workspace/directory and_workspace/scripts/subdirectory - Organize input and save to
_workspace/00_input.md - Copy data files to
_workspace/data/ - If existing files are present, copy to
_workspace/and skip the corresponding Phase - Determine execution mode based on request scope
Phase 2: Team Assembly and Execution
| Order | Task | Owner | Dependencies | Output |
|---|---|---|---|---|
| 1 | Exploratory Analysis | explorer | None | 01_exploration_report.md |
| 2 | Data Cleaning | cleaner | Task 1 | 02_cleaning_log.md, scripts/02_cleaning.py |
| 3a | Statistical Analysis | analyst | Task 2 | 03_analysis_results.md, scripts/03_analysis.py |
| 3b | EDA Visualization | visualizer | Task 1 | 04_visualizations.md (EDA portion) |
| 4 | Analysis Result Visualization | visualizer | Task 3a | 04_visualizations.md (analysis portion), scripts/04_viz_*.py |
| 5 | Final Report | reporter | Tasks 3a, 4 | 05_final_report.md |
Tasks 3a (analysis) and 3b (EDA visualization) run in parallel.
Inter-team communication flow:
- explorer completes → Sends cleaning recommendations to cleaner, analysis suggestions to analyst, distribution visualization targets to visualizer
- cleaner completes → Sends cleaned data location and transformation history to analyst
- analyst completes → Sends visualization requests to visualizer, insights to reporter
- visualizer completes → Sends visualization list to reporter
- reporter cross-validates all outputs. Sends correction requests when inconsistencies are found (up to 2 times)
Phase 3: Integration and Final Outputs
- Check all files in
_workspace/ - Verify that all required corrections from reporter have been addressed
- Report final summary to the user:
- Exploration Report —
01_exploration_report.md - Cleaning Log —
02_cleaning_log.md - Analysis Results —
03_analysis_results.md - Visualizations —
04_visualizations.md - Final Report —
05_final_report.md - Reproducible Scripts —
scripts/directory
- Exploration Report —
Scale-Based Modes
| User Request Pattern | Execution Mode | Agents Deployed |
|---|---|---|
| "Analyze the data", "Full analysis" | Full Pipeline | All 5 |
| "Just do EDA", "Data exploration" | Exploration Mode | explorer + visualizer |
| "Clean the data", "Data cleaning" | Cleaning Mode | explorer + cleaner |
| "Statistical analysis only", "Hypothesis testing" | Analysis Mode | analyst + visualizer + reporter |
| "Visualization only", "Draw charts" | Visualization Mode | visualizer only |
| "Write analysis report" (existing analysis) | Report Mode | reporter only |
Leveraging existing files: If already cleaned data exists, skip explorer and cleaner. If analysis results exist, skip analyst and proceed with visualization and reporting only.
Show full SKILL.md (294 more words)Show less
Data Transfer Protocol
| Strategy | Method | Purpose |
|---|---|---|
| File-based | _workspace/ directory | Primary outputs and data storage |
| Message-based | SendMessage | Key information transfer, correction requests |
| Code-based | _workspace/scripts/ | Reproducible analysis scripts |
File naming convention: {order}_{output}.{extension}
Error Handling
| Error Type | Strategy |
|---|---|
| File read failure | Try encodings sequentially (UTF-8→CP949→EUC-KR→Latin-1), auto-detect delimiter |
| Large data (>1GB) | Analyze with sampling, chunk processing, use dask for full statistics |
| Analysis assumptions not met | Auto-switch to nonparametric alternatives, state rationale in report |
| Font rendering issues | Auto-insert OS-specific font configuration code |
| Agent failure | 1 retry, proceed without that output if failed, note omission in report |
| Reporter finds inconsistency | Send correction request to relevant agent → rework → re-verify (up to 2 times) |
Test Scenarios
Normal Flow
Prompt: "Analyze this sales CSV file and find the cause of the sales decline" Expected Results:
- EDA: Variable profiling, missing/outlier analysis, sales-related variable identification
- Cleaning: Missing value treatment, outlier capping, type conversion log
- Analysis: Time-series decomposition, segment comparison (t-test/ANOVA), correlation analysis
- Visualization: Sales trend line chart, factor comparison bar chart, correlation heatmap
- Report: Top 3 decline causes + recommended actions + executive summary
Existing File Flow
Prompt: "I already have cleaned data. Just do statistical analysis and visualization" + cleaned data file attached Expected Results:
- Copy existing data to
_workspace/data/ - Analysis mode: Skip explorer and cleaner, deploy analyst + visualizer + reporter
- Cleaning history recorded based on user-provided information
Error Flow
Prompt: "Analyze this Excel file" (many variables with >50% missing, numerous outliers) Expected Results:
- explorer reports data quality issues in detail
- cleaner presents per-variable treatment strategies with rationale, warning if rows decrease >30%
- analyst states data sufficiency limitations and performs power analysis
- reporter honestly records data quality issues in limitations section
Agent Extension Skills
| Skill | Path | Enhanced Agent | Role |
|---|---|---|---|
| statistical-tests-selector | .claude/skills/statistical-tests-selector/skill.md | analyst | Test selection tree, t-test/ANOVA/chi-squared, effect size, power |
| visualization-chooser | .claude/skills/visualization-chooser/skill.md | visualizer | Chart type matrix, matplotlib/seaborn/plotly patterns, anti-patterns |
© revfactory, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in en/32-data-analysis/.claude/skills/data-analysis of revfactory/harness-100.
Open the folder on GitHubat commit 8e8d35c
Compare with similar skills
Data Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Data Analysis this skillrevfactory/harness-100 | 1.3k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Code EngineeropenJiuwen-ai/sciencediscovery | 148 | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Data Analysisxiaoyuge886/aigc | 198 | 1 repos | ~794 | Automated safety check: Pass | MIT | |
| Data Explorerliangdabiao/claude-data-analysis-ultra-main | 290 | — | ~2.1k | Automated safety check: Pass | None | |
| Profiling Tablesastronomer/agents | 450 | — | ~964 | Automated safety check: Pass | Apache-2.0 | |
| Stat Edaasgard-ai-platform/skills | 241 | — | ~954 | Automated safety check: Pass | MIT |
Similar skills
Data Analysis
xiaoyuge886/aigc
Perform data analysis tasks including data cleaning, statistical analysis, visualization, and insight generation.
Data Explorer
liangdabiao/claude-data-analysis-ultra-main
Performs exploratory data analysis, statistical analysis, and pattern discovery.
Profiling Tables
astronomer/agents
Deep-dive data profiling for a specific table. An agent skill from astronomer/agents.
Stat Eda
asgard-ai-platform/skills
Conduct Exploratory Data Analysis (EDA) using descriptive statistics, visualizations, and data quality checks.
Data Journalist
FerroxLabs/wayland
Combining investigative journalism with data analysis, including FOI requests, dataset acquisition, statistical analysis, visualization, and compelling data-driven storytelling.
More from revfactory/harness-100
All 464 skills in this repoAnti Bot Analyzer
revfactory/harness-100
A skill for analyzing website anti-bot defense mechanisms and developing legitimate evasion strategies.
API Error Design Patterns
revfactory/harness-100
Reference for designing how an API reports failures: structured error codes, response shapes, client-friendly messages, an error catalog and retry or fallback advice.
API Security Checklist
revfactory/harness-100
Walks a backend-dev agent through OWASP API Top 10 checks, authentication and authorization patterns, and defense code during API design.
Arg Parser Generator
revfactory/harness-100
Methodology for systematically designing and generating CLI tool argument parser structures.
Audience Segmentation
revfactory/harness-100
Audience segmentation skill used by the analyst and curator agents.
Audio Storytelling
revfactory/harness-100
Audio storytelling skill used by the podcast scriptwriter and show note editor.
Related
Categories
Questions about Data Analysis
What does Data Analysis do?
A full analysis pipeline where an agent team collaborates to perform exploratory data analysis (EDA), data cleaning, statistical analysis, visualization, and report writing. Data Analysis is an agent skill from revfactory/harness-100. A full analysis pipeline where an agent team collaborates to perform exploratory data analysis (EDA), data cleaning, statistical analysis, visualization, and report writing.
When should I use Data Analysis?
Data Analysis fits situations like: analyze this data; exploratory analysis; statistical analysis; data visualization.
How do I install Data Analysis in Claude Code?
Run `npx skills add revfactory/harness-100 --skill data-analysis -a claude-code`. Or copy the skill folder (en/32-data-analysis/.claude/skills/data-analysis in revfactory/harness-100) into .claude/skills/data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Data Analysis in Codex?
Run `npx skills add revfactory/harness-100 --skill data-analysis -a codex`. Or copy the skill folder (en/32-data-analysis/.claude/skills/data-analysis in revfactory/harness-100) into .agents/skills/data-analysis in your project. Codex loads it when a task matches its description.
Can I use Data Analysis in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add revfactory/harness-100 --skill data-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-analysis, .gemini/skills/data-analysis, .github/skills/data-analysis and .opencode/skills/data-analysis in your project.
What does Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Analysis is instructions for the agent only.
Does Data Analysis access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Data Analysis safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Data Analysis use?
Data Analysis is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Data Analysis use?
About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Data Analysis?
Skills that share tags, products or a category with Data Analysis: Code Engineer (openJiuwen-ai/sciencediscovery, 148 stars), Data Analysis (xiaoyuge886/aigc, 198 stars), Data Explorer (liangdabiao/claude-data-analysis-ultra-main, 290 stars) and Profiling Tables (astronomer/agents, 450 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Analysis?
revfactory (a GitHub user) maintains it in revfactory/harness-100, which has 1,290 GitHub stars. The repository holds 464 skills in this directory. The repository was last updated on March 22, 2026.
Source: revfactory/harness-100 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.