Simpo Training
CheckedSimpo Training simulates realistic customer interactions for agent practice, boosting confidence and improving real-world performance.
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 davila7/claude-code-templates --skill simpo-training -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
davila7
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 davila7/claude-code-templates at 79182c51fb.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 79182c51fb
- Scanned
- 4 files, 1,297 lines
About this skill
What it does
The simpo-training skill provides AI agents with the ability to generate training data for various machine learning tasks. It can create synthetic datasets, augment existing datasets, and label data according to specified criteria. This allows users to rapidly build or improve models when real-world data is scarce or expensive to acquire.
When to use it
- Data Augmentation: Expand a small dataset of images by generating variations (rotations, crops, color adjustments) for improved model robustness.
- Synthetic Data Generation: Create datasets for rare events where collecting real examples is difficult, such as fraud detection or anomaly identification.
- Labeling Assistance: Generate initial labels for unlabeled data to accelerate the training process, which can then be refined by human annotators.
- Model Evaluation: Produce diverse test cases to thoroughly evaluate a model's performance across different scenarios.
Key capabilities
- Synthetic dataset generation
- Data augmentation techniques (e.g., rotations, crops)
- Customizable labeling criteria
- Support for various data types (images, text, etc.)
Example prompts
- "Generate 100 synthetic images of cats in different poses and lighting conditions."
- "Augment this dataset of handwritten digits by rotating each image by up to 30 degrees."
- "Create a training set for sentiment analysis with 50 positive and 50 negative movie reviews."
Tips & gotchas
The quality of the generated data depends heavily on the prompt's clarity. Be specific about desired characteristics when requesting synthetic datasets or augmentation techniques.