Generate Synthetic Data
CheckedHelps with code generation, data as part of agent workflows workflows.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add hamelsmu/evals-skills --skill generate-synthetic-data -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
hamelsmu
Taken from the source listing. We haven't verified who controls this account.
Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned hamelsmu/evals-skills at 22418da2bf.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 22418da2bf
- Scanned
- 1 file, 122 lines
About this skill
What it does
This skill enables AI agents to generate synthetic data for testing, validation, and model training purposes. It creates artificial datasets that mimic real-world patterns without exposing sensitive information or requiring access to live production systems.
When to use it
- You need test data to validate software logic before deploying to production environments.
- You want to train machine learning models when actual labeled data is scarce or expensive to acquire.
- You require anonymized datasets for security testing and penetration analysis without risking privacy violations.
- You need to simulate edge cases or rare scenarios that are difficult to capture in real-world logs.
Key capabilities
- Generates structured and unstructured synthetic records on demand.
- Supports various data formats including JSON, CSV, and relational schemas.
- Maintains statistical distributions and correlations found in source datasets.
- Ensures generated data remains non-sensitive and privacy-compliant by design.
Example prompts
"Generate 100 rows of synthetic user login logs with timestamps, IP addresses, and status codes for testing my authentication module." "Create a JSON dataset of 50 fictional customer profiles including names, ages, and purchase histories to train a recommendation engine." "Produce synthetic network traffic data simulating DDoS attack patterns for stress-testing my firewall rules."
Tips & gotchas
Ensure you define clear constraints on data distribution and format to avoid generating unrealistic or biased samples that could skew model training. Always verify the statistical fidelity of generated data against known benchmarks before using it in critical validation pipelines.