Slash Command

Optimize Staking

Analyze and optimize staking rewards across protocols

Type: Slash Command
Repository: jeremylongshore/tons-of-skills-marketplace
GitHub stars: 2.8k
License: MIT
Repo last updated: Sep 27, 2026
Source file: plugins/crypto/staking-rewards-optimizer/commands/optimize-staking.md

What Optimize Staking is

Optimize Staking is a slash command published in the jeremylongshore/tons-of-skills-marketplace repository on GitHub, which has about 2.8k stars. The repository describes itself as: “Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.”

A slash command is a reusable prompt saved as a markdown file and run by typing its name after a slash. In Claude Code, custom commands have been merged into skills: a file in .claude/commands/ and a skill folder in .claude/skills/ both create the same kind of command, and existing command files keep working.

Optimize Staking gives you a repeatable way to run the same instructions without retyping them, optionally with arguments.

How to install Optimize Staking

Claude Code

Download optimize-staking.md from the repository.
Save it to ~/.claude/commands/ (all projects) or .claude/commands/ (one project). As a skill, you can instead save it as ~/.claude/skills/<name>/SKILL.md.
Run it by typing / followed by its name.

Claude Cowork

Turn the command into a skill: create a folder with the file saved as SKILL.md and zip it.
In Customize → Skills, click +, then upload the ZIP.
Run it from any task with / and the skill name.

New to extending Cowork? Our plugins guide and Customize guide explain how skills, plugins, and connectors fit together.

Inside the source file

An excerpt from plugins/crypto/staking-rewards-optimizer/commands/optimize-staking.md, shared under the repository's MIT license. Read the full file on GitHub.

You are a staking rewards optimization specialist. When this command is invoked, help users maximize their staking returns across different protocols and chains.

Your Task

Analyze staking opportunities and provide optimization recommendations:

Current Position Analysis (if user provides portfolio):

Review current staking positions
Calculate actual APY/APR being earned
Identify lock-up periods and liquidity constraints
Assess risk levels of current positions

Market Opportunity Scan:

Compare staking rates across major protocols:
Ethereum (ETH staking, Lido, Rocket Pool)
Cosmos ecosystem (ATOM, OSMO, JUNO)
Polkadot (DOT, parachains)
Solana (SOL validators)
Layer 2s (Arbitrum, Optimism, Polygon)
Include liquid staking derivatives
Factor in validator commissions

Optimization Strategy:

Recommend optimal allocation percentages
Consider risk diversification
Account for gas fees and transaction costs
Suggest rebalancing frequency
Identify compounding opportunities

Risk Assessment:

Smart contract risks
Validator slashing risks
Lock-up period risks
Protocol-specific risks
Regulatory considerations

Implementation Plan:

Step-by-step instructions for reallocation
Estimated costs (gas fees, bridging fees)
Expected improvement in returns
Timeline for execution

Output Format

Present your analysis in this structure:

## Staking Portfolio Optimization Report

### Current Position Summary
[If provided, analyze current staking positions]

### Top Opportunities
| Protocol | Asset | APY | Lock Period | Risk Level | Recommendation |
|----------|-------|-----|-------------|------------|----------------|
| ...      | ...   | ... | ...         | ...        | ...            |

### Optimization Strategy
**Recommended Allocation:**
- Protocol A: X% (Reason)
- Protocol B: Y% (Reason)
- Protocol C: Z% (Reason)

**Expected Improvement:**
- Current effective APY: X%
…

Data Sources to Reference

When making recommendations, consider data from:

DefiLlama (staking yields)
StakingRewards.com
Individual protocol documentation
Validator performance metrics
Historical APY trends

Important Notes

Always emphasize that APYs are variable and historical performance doesn't guarantee future returns
Warn about smart contract risks, especially for newer protocols
Consider the user's risk tolerance and investment timeline
Factor in opportunity costs and liquidity needs
Note that this is educational information, not financial advice

Example Usage

User might ask:

"What are the best staking opportunities for ETH right now?"
"I have 10 SOL staked at 7% APY - can I do better?"
"Compare liquid staking options across different chains"
"Optimize my staking portfolio: 50 ATOM, 100 DOT, 5 ETH"

Before you install

Read the whole file first. Skills, commands, and subagents are instructions Claude will follow, so make sure they match what you want.
Check which tools, scripts, or MCP servers it uses. Local servers and scripts run with your permissions.
Try it in a test project or a copy of your files before pointing it at real work.
Pin the version you tested, and review changes before updating.
Watch for instructions that fetch web content or run shell commands; those are where prompt injection risks start. See our prompt injection guide.

FAQ

What is Optimize Staking?

Optimize Staking is a slash command for Claude Code and Claude Cowork from the jeremylongshore/tons-of-skills-marketplace repository on GitHub. Analyze and optimize staking rewards across protocols

How do I install Optimize Staking in Claude Code?

Download optimize-staking.md from the repository. Save it to ~/.claude/commands/ (all projects) or .claude/commands/ (one project). As a skill, you can instead save it as ~/.claude/skills/<name>/SKILL.md. Run it by typing / followed by its name.

Can I use Optimize Staking in Claude Cowork?

Turn the command into a skill: create a folder with the file saved as SKILL.md and zip it. In Customize → Skills, click +, then upload the ZIP. Run it from any task with / and the skill name.

Is Optimize Staking safe to install?

It is a third-party community resource, not reviewed by Anthropic or this site. Read the source file first, check which tools and connectors it uses, and install only from sources you trust.

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Listing data comes from the public GitHub repository and was last checked in September 2026. Excerpts are © their authors and shared under MIT. This directory is independent and not affiliated with Anthropic or the resource's authors.