Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add DamingDong/opencode-skills-hub --skill data-augmentation-pipelinegit clone --depth 1 https://github.com/DamingDong/opencode-skills-hubWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/damingdong/opencode-skills-hub/data-augmentation-pipeline)<a href="https://agentmods.dev/skills/damingdong/opencode-skills-hub/data-augmentation-pipeline"><img src="https://agentmods.dev/badge/skills/damingdong/opencode-skills-hub/data-augmentation-pipeline.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.00436 |
| Opus 5 | $0.00032 | $0.00218 |
| Sonnet 5 | $0.00013 | $0.00087 |
| Haiku 4.5 | $0.00006 | $0.00044 |
Grade A, and why
data-augmentation-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Data Augmentation Pipeline
Overview
This skill provides automated assistance for data augmentation pipeline tasks within the ML Training domain.
When to Use
This skill activates automatically when you:
- Mention "data augmentation pipeline" in your request
- Ask about data augmentation pipeline patterns or best practices
- Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking.
Instructions
- Provides step-by-step guidance for data augmentation pipeline
- Follows industry best practices and patterns
- Generates production-ready code and configurations
- Validates outputs against common standards
Examples
Example: Basic Usage Request: "Help me with data augmentation pipeline" Result: Provides step-by-step guidance and generates appropriate configurations
Prerequisites
- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml training concepts
Output
- Generated configurations and code
- Best practice recommendations
- Validation results
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Resources
- Official documentation for related tools
- Best practices guides
- Community examples and tutorials
Related Skills
Part of the ML Training skill category. Tags: ml, training, pytorch, tensorflow, sklearn
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 73 lines · 65 tokens per session scan A 8951fbb3199e
data-augmentation-pipeline is a skill published in the GitHub repository DamingDong/opencode-skills-hub (11 stars, last pushed 7mo ago), licensed MIT. It adds 65 tokens to every session and 436 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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