learning-rate-scheduler

learning-rate-scheduler is a skill for Claude Code from DamingDong/opencode-skills-hub. It costs 64 tokens per session (434 once invoked), scanned A, original, MIT.

A guide for adjusting a machine-learning model's learning rate during training. The learning rate controls how much the model changes after each training step.

In plain words
What is it for?
Use it to create learning-rate scheduling configurations and get guidance for model-training setups.
Why use it?
It helps avoid training that changes too quickly, too slowly, or stops improving.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to create learning-rate scheduling configurations and get guidance for model-training setups.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/damingdong/opencode-skills-hub/learning-rate-scheduler
Install

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.

Any agent
npx skills add DamingDong/opencode-skills-hub --skill learning-rate-scheduler
Clone the repo
git clone --depth 1 https://github.com/DamingDong/opencode-skills-hub

Made for: Claude Code.

Wrote 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.

agentmods badge for learning-rate-scheduler

README.md
[![agentmods](https://agentmods.dev/badge/skills/damingdong/opencode-skills-hub/learning-rate-scheduler/github.svg)](https://agentmods.dev/skills/damingdong/opencode-skills-hub/learning-rate-scheduler)
Your own site
<a href="https://agentmods.dev/skills/damingdong/opencode-skills-hub/learning-rate-scheduler"><img src="https://agentmods.dev/badge/skills/damingdong/opencode-skills-hub/learning-rate-scheduler/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for learning-rate-scheduler

Your own site · 80×15
<a href="https://agentmods.dev/skills/damingdong/opencode-skills-hub/learning-rate-scheduler"><img src="https://agentmods.dev/badge/skills/damingdong/opencode-skills-hub/learning-rate-scheduler.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 434 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00064 $0.00434
Opus 5.5 $0.00026 $0.00174
Sonnet 5.5 $0.00013 $0.00087
Haiku 4.5 $0.00006 $0.00043

Measured 2mo ago against content hash 5669e3c3c2db, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

learning-rate-scheduler 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 2mo 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.

skills/community/learning-rate-scheduler/SKILL.md · 73 lines

What it actually says

Learning Rate Scheduler

Overview

This skill provides automated assistance for learning rate scheduler tasks within the ML Training domain.

When to Use

This skill activates automatically when you:

  • Mention "learning rate scheduler" in your request
  • Ask about learning rate scheduler patterns or best practices
  • Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking.

Instructions

  1. Provides step-by-step guidance for learning rate scheduler
  2. Follows industry best practices and patterns
  3. Generates production-ready code and configurations
  4. Validates outputs against common standards

Examples

Example: Basic Usage Request: "Help me with learning rate scheduler" 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

Part of the ML Training skill category. Tags: ml, training, pytorch, tensorflow, sklearn

Changes

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.

  1. 2mo ago First seen · 73 lines · 64 tokens per session scan A 5669e3c3c2db

Subscribe to this mod's changes

learning-rate-scheduler is a skill published in the GitHub repository DamingDong/opencode-skills-hub (12 stars, last pushed 9mo ago), licensed MIT. It adds 64 tokens to every session and 434 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.

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