Experiment Design Kit
gtmagents/gtm-agents
Toolkit for structuring hypotheses, variants, guardrails, and measurement plans.
[production-grade internal] Optimizes AI/ML/LLM usage when you need model selection, prompt engineering, cost reduction, or experiment design.
Install Data Scientist
$ npx skills add nagisanzenin/production-grade --skill data-scientist -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install nagisanzenin/production-grade data-scientist --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/nagisanzenin/production-grade.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/data-scientist .claude/skills/data-scientist && 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-scientist" agent skill from https://github.com/nagisanzenin/production-grade/tree/main/skills/data-scientist into .claude/skills/data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-scientist", 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/nagisanzenin/production-grade/tree/main/skills/data-scientistType 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 nagisanzenin/production-grade --skill data-scientist -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install nagisanzenin/production-grade data-scientist --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nagisanzenin/production-grade.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/data-scientist .agents/skills/data-scientist && 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-scientist" agent skill from https://github.com/nagisanzenin/production-grade/tree/main/skills/data-scientist into .agents/skills/data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-scientist", 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 nagisanzenin/production-grade --skill data-scientist -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install nagisanzenin/production-grade data-scientist --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nagisanzenin/production-grade.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/data-scientist .cursor/skills/data-scientist && 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-scientist" agent skill from https://github.com/nagisanzenin/production-grade/tree/main/skills/data-scientist into .cursor/skills/data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-scientist", 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/nagisanzenin/production-grade.git --path skills/data-scientist--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 nagisanzenin/production-grade --skill data-scientist -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install nagisanzenin/production-grade data-scientist --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nagisanzenin/production-grade.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/data-scientist .gemini/skills/data-scientist && 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-scientist" agent skill from https://github.com/nagisanzenin/production-grade/tree/main/skills/data-scientist into .gemini/skills/data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-scientist", 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 nagisanzenin/production-grade data-scientistInstalls 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 nagisanzenin/production-grade --skill data-scientist -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/nagisanzenin/production-grade.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/data-scientist .github/skills/data-scientist && 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-scientist" agent skill from https://github.com/nagisanzenin/production-grade/tree/main/skills/data-scientist into .github/skills/data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-scientist", 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 nagisanzenin/production-grade --skill data-scientist -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install nagisanzenin/production-grade data-scientist --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nagisanzenin/production-grade.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/data-scientist .opencode/skills/data-scientist && 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-scientist" agent skill from https://github.com/nagisanzenin/production-grade/tree/main/skills/data-scientist into .opencode/skills/data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-scientist", 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-scientist- GitHub stars
- 181
- Token cost
- ~3k tokens
- SKILL.md length
- 1,135 words
- Files
- 7
- Skills in repo
- 13
- Repo updated
- First seen
- Licence
- None found
At a glance
[production-grade internal] Optimizes AI/ML/LLM usage when you need model selection, prompt engineering, cost reduction, or experiment design.
- Works in 6 steps: Projected monthly AI/ML spend exceeds… → Any LLM feature has quality score below… → A/B test shows significant regression on… → …
- Tasks that involve Experimental design
- SKILL.md covers Preprocessing, Engagement Mode, Progress Output and Fallback Protocol Summary, plus 11 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Data Scientist is an agent skill from nagisanzenin/production-grade. [production-grade internal] Optimizes AI/ML/LLM usage when you need model selection, prompt engineering, cost reduction, or experiment design. Routed via the production-grade orchestrator.
Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files (for example `phases/01-system-audit.md`, `phases/02-llm-optimization.md` and `phases/03-experiment-framework.md`).
It sits in AI & LLM Engineering, covering Experimental design. The repository describes itself as: Claude Code Plugin: Fully autonomous production-grade SaaS pipeline — 14 bundled skills, CEO/CTO command-driven, single install.
When your agent uses it
- Tasks that involve Experimental design
Example prompts
- “/data-scientist”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Projected monthly AI/ML spend exceeds $10,000 at current growth rate
- Any LLM feature has quality score below 7.0/10.0
- A/B test shows significant regression on guardrail metric
- Data quality check failure rate exceeds 1%
- System design requires infrastructure not yet provisioned
- PII detected in training data, prompts, or analytics pipelines
What it can do on your machine
Read from SKILL.md and the folder at commit 4b2f13f. 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 Scientist loads about 3k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 1,135 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
Without a licence we can't republish the file, so here is its outline and opening line. It has 1,135 words (~3,008 tokens).
“!cat Claude-Production-Grade-Suite/.protocols/ux-protocol.md 2>/dev/null || true !cat Claude-Production-Grade-Suite/.protocols/input-validation.md 2>/dev/null || true !cat Claude-Production-Grade-Suite/.protocols/tool-efficiency.md 2>/dev/null || true !cat Claude-Production-Grade-Suite/.protocols/visual-identity.md 2>/dev/null || true !cat Claude-Production-Grade-Suite/.protocols/freshness-protocol.md 2>/dev/null || true !cat Claude-Production-Grade-Suite/.protocols/receipt-protocol.md 2>/dev/null || true !cat Claude-Production-Grade-Suite/.protocols/boundary-safety.md 2>/dev/null || true !cat Claude-Production-Grade-Suite/.protocols/loop-protocol.md 2>/dev/null || true…”
- name
- data-scientist
- version
- 1.0.0
- author
- nagisanzenin
- tags
- ml, ai, llm, data-science, optimization, analytics, ab-testing, prompt-engineering, mlops
Files
SKILL.md and 6 other files in skills/data-scientist of nagisanzenin/production-grade.
- SKILL.md
- phases/01-system-audit.md
- phases/02-llm-optimization.md
- phases/03-experiment-framework.md
- phases/04-data-pipeline.md
- phases/05-ml-infrastructure.md
- phases/06-cost-modeling.md
Open the folder on GitHubat commit 4b2f13f
Compare with similar skills
Data Scientist 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 Scientist this skillnagisanzenin/production-grade | 181 | — | ~3k | Automated safety check: Pass | None | |
| Experiment Design Kitgtmagents/gtm-agents | 414 | 1 repos | ~837 | Automated safety check: Pass | Apache-2.0 | |
| ML Experiment IterationLeeroo-AI/superml | 195 | — | ~4.8k | Automated safety check: Pass | Apache-2.0 | |
| Statistical Powerspacering-net/codeg | 3.9k | 1 repos | ~3.6k | Automated safety check: Notes | MIT | |
| Data Scientistmagnus919/hermes-profiles | 289 | — | ~3.3k | Automated safety check: Pass | MIT | |
| Research Methodologychekusu/wanman | 688 | — | ~533 | Automated safety check: Pass | Apache-2.0 |
Similar skills
ML Experiment Iteration
Leeroo-AI/superml
Produces ranked, evidence-grounded next steps when an ML experiment has stalled, drawing on a Leeroopedia knowledge base or on fetched docs and issues.
Statistical Power
spacering-net/codeg
Sample-size and statistical power calculations for planning studies.
Data Scientist
magnus919/hermes-profiles
PhD-level expertise in data science, statistics, and machine learning.
Research Methodology
chekusu/wanman
Methodology for market research and data collection, ensuring data quality and source traceability
Data Scientist
magnus919/agent-skills
A skill your agent uses for PhD-level expertise in data science, statistics, and machine learning: rigorous statistical analysis, experimental design, causal inference, advanced modeling, research…
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Related
Categories
Questions about Data Scientist
What does Data Scientist do?
[production-grade internal] Optimizes AI/ML/LLM usage when you need model selection, prompt engineering, cost reduction, or experiment design. Data Scientist is an agent skill from nagisanzenin/production-grade. [production-grade internal] Optimizes AI/ML/LLM usage when you need model selection, prompt engineering, cost reduction, or experiment design.
When should I use Data Scientist?
Data Scientist fits situations like: tasks that involve Experimental design.
How do I install Data Scientist in Claude Code?
Run `npx skills add nagisanzenin/production-grade --skill data-scientist -a claude-code`. Or copy the skill folder (skills/data-scientist in nagisanzenin/production-grade) into .claude/skills/data-scientist in your project. Claude Code loads it when a task matches its description.
How do I install Data Scientist in Codex?
Run `npx skills add nagisanzenin/production-grade --skill data-scientist -a codex`. Or copy the skill folder (skills/data-scientist in nagisanzenin/production-grade) into .agents/skills/data-scientist in your project. Codex loads it when a task matches its description.
Can I use Data Scientist 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 nagisanzenin/production-grade --skill data-scientist -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-scientist, .gemini/skills/data-scientist, .github/skills/data-scientist and .opencode/skills/data-scientist in your project.
What does Data Scientist need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Scientist is instructions for the agent only. Our summary lists: Python 3.
Does Data Scientist 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 Scientist 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 Scientist use?
No licence was found for Data Scientist or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does Data Scientist use?
About 3k tokens (SKILL.md is roughly 12k 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 Scientist?
Skills that share tags, products or a category with Data Scientist: Experiment Design Kit (gtmagents/gtm-agents, 414 stars), ML Experiment Iteration (Leeroo-AI/superml, 195 stars), Statistical Power (spacering-net/codeg, 3.9k stars) and Data Scientist (magnus919/hermes-profiles, 289 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Scientist?
nagisanzenin (a GitHub user) maintains it in nagisanzenin/production-grade, which has 181 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on August 19, 2026.
Source: nagisanzenin/production-grade on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.