Fs Fixture
privatenumber/fs-fixture
Create disposable file system test fixtures from objects, templates, or empty directories with automatic cleanup.
A skill your agent uses when the user wants to run synthetic data generation via scripts — detect environment, execute a flow, and present results.
Install Data Generation
$ npx skills add Red-Hat-AI-Innovation-Team/sdg_hub --skill data-generation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Red-Hat-AI-Innovation-Team/sdg_hub data-generation --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/Red-Hat-AI-Innovation-Team/sdg_hub.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/data-generation .claude/skills/data-generation && 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-generation" agent skill from https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub/tree/main/.claude/skills/data-generation into .claude/skills/data-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-generation", 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/Red-Hat-AI-Innovation-Team/sdg_hub/tree/main/.claude/skills/data-generationType 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 Red-Hat-AI-Innovation-Team/sdg_hub --skill data-generation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Red-Hat-AI-Innovation-Team/sdg_hub data-generation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/data-generation .agents/skills/data-generation && 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-generation" agent skill from https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub/tree/main/.claude/skills/data-generation into .agents/skills/data-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-generation", 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 Red-Hat-AI-Innovation-Team/sdg_hub --skill data-generation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Red-Hat-AI-Innovation-Team/sdg_hub data-generation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/data-generation .cursor/skills/data-generation && 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-generation" agent skill from https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub/tree/main/.claude/skills/data-generation into .cursor/skills/data-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-generation", 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/Red-Hat-AI-Innovation-Team/sdg_hub.git --path .claude/skills/data-generation--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 Red-Hat-AI-Innovation-Team/sdg_hub --skill data-generation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Red-Hat-AI-Innovation-Team/sdg_hub data-generation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/data-generation .gemini/skills/data-generation && 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-generation" agent skill from https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub/tree/main/.claude/skills/data-generation into .gemini/skills/data-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-generation", 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 Red-Hat-AI-Innovation-Team/sdg_hub data-generationInstalls 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 Red-Hat-AI-Innovation-Team/sdg_hub --skill data-generation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/data-generation .github/skills/data-generation && 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-generation" agent skill from https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub/tree/main/.claude/skills/data-generation into .github/skills/data-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-generation", 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 Red-Hat-AI-Innovation-Team/sdg_hub --skill data-generation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Red-Hat-AI-Innovation-Team/sdg_hub data-generation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/data-generation .opencode/skills/data-generation && 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-generation" agent skill from https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub/tree/main/.claude/skills/data-generation into .opencode/skills/data-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-generation", 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-generation- GitHub stars
- 164
- Token cost
- ~381 tokens
- SKILL.md length
- 119 words
- Files
- 1
- Skills in repo
- 4
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses when the user wants to run synthetic data generation via scripts — detect environment, execute a flow, and present results.
- Works in 3 steps: Check Environment → Execute Generation → Present Results
- The user wants to run synthetic data generation via scripts — detect environment
- SKILL.md covers Step 1: Check Environment, Step 2: Execute Generation and Step 3: Present Results
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Data Generation is an agent skill from Red-Hat-AI-Innovation-Team/sdg_hub. Use when the user wants to run synthetic data generation via scripts — detect environment, execute a flow, and present results. For detailed guidance on approaches, blocks, flow authoring, and troubleshooting, consult the synthetic-data-generation skill.
Its SKILL.md is about 380 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 Testing & QA, covering Test data and fixtures. The repository describes itself as: Synthetic Data Generation Toolkit for LLMs. The licence is Apache-2.0.
When your agent uses it
- The user wants to run synthetic data generation via scripts — detect environment
- Present results
Example prompts
- “/data-generation”
Requirements
- Pre-approved tools (allowed-tools): Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_generate.sh:*), Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_detect.sh:*), Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_flows.sh:*)
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 31efcbe. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_generate.sh:*)Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_detect.sh:*)Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_flows.sh:*)
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 Generation loads about 381 tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 119 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 Red-Hat-AI-Innovation-Team/sdg_hub at commit 31efcbe, republished under its Apache-2.0 licence (© Red-Hat-AI-Innovation-Team). 119 words, ~381 tokens.
Download SKILL.mdSave it as
.claude/skills/data-generation/SKILL.md (or your agent's skills folder).- name
- data-generation
- description
- Use when the user wants to run synthetic data generation via scripts — detect environment, execute a flow, and present results. For detailed guidance on approaches, blocks, flow authoring, and troubleshooting, consult the synthetic-data-generation skill.
- allowed-tools
- Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_generate.sh:*), Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_detect.sh:*), Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_flows.sh:*)
Run Data Generation
Execute synthetic data generation using sdg_hub flows. For approach selection, custom flow authoring, and block reference, consult the synthetic-data-generation skill.
Step 1: Check Environment
"${CLAUDE_PLUGIN_ROOT}/scripts/sdg_detect.sh"If not ready
library=missingorconfig=missing: invoke thesetup-guideskill.
If ready (library=installed, config=found)
Proceed to Step 2.
Step 2: Execute Generation
If the user doesn't specify a flow, invoke the flow-browser skill to find one.
Recommend starting with --sample 2 for a dry run.
"${CLAUDE_PLUGIN_ROOT}/scripts/sdg_generate.sh" $ARGUMENTSStep 3: Present Results
- Generation status — Whether generation completed successfully
- Row counts — Input rows processed and output rows generated
- Output location — Path to the generated dataset
- Errors — If any rows failed, report the count
If generation failed, consult the synthetic-data-generation skill for troubleshooting.
© Red-Hat-AI-Innovation-Team, 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 .claude/skills/data-generation of Red-Hat-AI-Innovation-Team/sdg_hub.
Open the folder on GitHubat commit 31efcbe
Compare with similar skills
Data Generation 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 Generation this skillRed-Hat-AI-Innovation-Team/sdg_hub | 164 | — | ~381 | Automated safety check: Pass | Apache-2.0 | |
| Fs Fixtureprivatenumber/fs-fixture | 100 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Dev Tenant APInightscout/nocturne | 139 | — | ~1.4k | Automated safety check: Pass | None | |
| Rsibench Data Factoryevolvent-ai/RSIBench-Data | 171 | — | ~640 | Automated safety check: Notes | None | |
| Eval Designagentscope-ai/OpenJudge | 871 | — | ~2.8k | Automated safety check: Warn | Apache-2.0 | |
| Migrate Mstest V1v2 To V3dotnet/skills | 5.6k | 1 repos | ~5.5k | Automated safety check: Pass | MIT |
Similar skills
Dev Tenant API
nightscout/nocturne
Interact with Nocturne's local dev-only API: seed a loginable tenant preloaded with realistic sample data, obtain a browser session (loginLink) or bearer token headlessly, export/re-seed the dev…
Rsibench Data Factory
evolvent-ai/RSIBench-Data
Use inside RSIBench-Data when testing whether an automation agent can improve a target model on a configured benchmark through synthetic Tinker SFT data, Tinker sampling, and E2B-based Harbor…
Eval Design
agentscope-ai/OpenJudge
A skill your agent uses when the user needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a…
- Official
Migrate Mstest V1v2 To V3
dotnet/skills
Use this skill before answering or editing whenever an MSTest v1/v2 project is being upgraded or repaired for v3.
App Verification
mblode/agent-skills
Builds and maintains a repo's own verification harness (verify CLI, doctor, worktree isolation, feature map, seed data) and a reproduce-first bug handoff.
More from Red-Hat-AI-Innovation-Team/sdg_hub
Synthetic Data Generation
Red-Hat-AI-Innovation-Team/sdg_hub
Generate synthetic data using sdghub with composable blocks and YAML flows.
Setup Guide
Red-Hat-AI-Innovation-Team/sdg_hub
A skill your agent uses when the user wants to set up synthetic data generation for the first time, or when sdghub is not yet installed/configured in the current environment.
Flow Browser
Red-Hat-AI-Innovation-Team/sdg_hub
A skill your agent uses when the user wants to list, search, or inspect available SDG flows and data generation pipelines.
Related
Categories
Questions about Data Generation
What does Data Generation do?
A skill your agent uses when the user wants to run synthetic data generation via scripts — detect environment, execute a flow, and present results. Data Generation is an agent skill from Red-Hat-AI-Innovation-Team/sdg_hub. Use when the user wants to run synthetic data generation via scripts — detect environment, execute a flow, and present results.
When should I use Data Generation?
Data Generation fits situations like: the user wants to run synthetic data generation via scripts — detect environment; present results.
How do I install Data Generation in Claude Code?
Run `npx skills add Red-Hat-AI-Innovation-Team/sdg_hub --skill data-generation -a claude-code`. Or copy the skill folder (.claude/skills/data-generation in Red-Hat-AI-Innovation-Team/sdg_hub) into .claude/skills/data-generation in your project. Claude Code loads it when a task matches its description.
How do I install Data Generation in Codex?
Run `npx skills add Red-Hat-AI-Innovation-Team/sdg_hub --skill data-generation -a codex`. Or copy the skill folder (.claude/skills/data-generation in Red-Hat-AI-Innovation-Team/sdg_hub) into .agents/skills/data-generation in your project. Codex loads it when a task matches its description.
Can I use Data Generation 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 Red-Hat-AI-Innovation-Team/sdg_hub --skill data-generation -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-generation, .gemini/skills/data-generation, .github/skills/data-generation and .opencode/skills/data-generation in your project.
What does Data Generation need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Generation is instructions for the agent only. Its frontmatter pre-approves these tools: Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_generate.sh:*), Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_detect.sh:*), Bash(${CLAUDE_PLUGIN_ROOT}/scripts/sdg_flows.sh:*).
Does Data Generation 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 Generation 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 Generation use?
Data Generation 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 Generation use?
About 381 tokens (SKILL.md is roughly 1.5k 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 Generation?
Skills that share tags, products or a category with Data Generation: Fs Fixture (privatenumber/fs-fixture, 100 stars), Dev Tenant API (nightscout/nocturne, 139 stars), Rsibench Data Factory (evolvent-ai/RSIBench-Data, 171 stars) and Eval Design (agentscope-ai/OpenJudge, 871 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Generation?
Red-Hat-AI-Innovation-Team (a GitHub organization) maintains it in Red-Hat-AI-Innovation-Team/sdg_hub, which has 164 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on October 9, 2026.
Source: Red-Hat-AI-Innovation-Team/sdg_hub on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.