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Agent skill

Optimizing Staking Rewards

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Compare and optimize staking rewards across validators, protocols, and blockchains with risk assessment.

MITAuto-check passed

Install Optimizing Staking Rewards

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill optimizing-staking-rewards -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace optimizing-staking-rewards --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/optimizing-staking-rewards .claude/skills/optimizing-staking-rewards && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
optimizing-staking-rewards
GitHub stars
2.8k
Token cost
~1.1k tokens
SKILL.md length
235 words
Files
12 (incl. scripts, references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Compare and optimize staking rewards across validators, protocols, and blockchains with risk assessment.

  • Works in 4 steps: Python 3.8+ installed → Dependencies: pip install requests → Network access to DeFiLlama APIs → …
  • Analyzing staking opportunities
  • SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
  • Runs Python scripts from its folder; calls python and pip

What it does

Optimizing Staking Rewards is an agent skill from jeremylongshore/tons-of-skills-marketplace. Compare and optimize staking rewards across validators, protocols, and blockchains with risk assessment. Use when analyzing staking opportunities, comparing validators, calculating staking rewards, or optimizing PoS yields. Trigger with phrases like "optimize staking", "compare staking", "best staking APY", "liquid staking", "validator comparison", "staking rewards", or "ETH staking options".

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts and reference files (for example `ARD.md`, `PRD.md` and `config/settings.yaml`). Compatibility notes: Designed for Claude Code

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. The licence is MIT.

When your agent uses it

  • Analyzing staking opportunities
  • Comparing validators
  • Calculating staking rewards
  • Optimizing PoS yields

Example prompts

  • “optimize staking”
  • “compare staking”
  • “best staking APY”
  • “/optimizing-staking-rewards”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(crypto:staking-*)

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Python 3.8+ installed
  2. Dependencies: pip install requests
  3. Network access to DeFiLlama APIs
  4. Optional: CoinGecko API key for higher rate limits

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash(crypto:staking-*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 5 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • defillama.com
    • stakingrewards.com
    • lido.fi
    • rocketpool.net

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Optimizing Staking Rewards loads about 1.1k tokens when it runs, and up to ~5.4k if it reads all its reference files. Until then it costs about 106 tokens; SKILL.md has 235 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~106
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 235 words, ~1,150 tokens.

Download SKILL.mdSave it as .claude/skills/optimizing-staking-rewards/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
optimizing-staking-rewards
description
Compare and optimize staking rewards across validators, protocols, and blockchains with risk assessment. Use when analyzing staking opportunities, comparing validators, calculating staking rewards, or optimizing PoS yields. Trigger with phrases like "optimize staking", "compare staking", "best staking APY", "liquid staking", "validator comparison", "staking rewards", or "ETH staking options".
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(crypto:staking-*)
compatibility
Designed for Claude Code
version
1.26.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
crypto, optimizing-staking

Optimizing Staking Rewards

Overview

Analyze staking opportunities across PoS blockchains and liquid staking protocols. Compares APY/APR, calculates net yields after fees, assesses protocol risks, and recommends optimal allocations.

Prerequisites

  1. Python 3.8+ installed
  2. Dependencies: pip install requests
  3. Network access to DeFiLlama APIs
  4. Optional: CoinGecko API key for higher rate limits

Instructions

  1. Compare staking options for a specific asset:

    bash
    python ${CLAUDE_SKILL_DIR}/scripts/staking_optimizer.py --asset ETH

    Shows protocol name, type (native vs liquid), gross/net APY, risk score, TVL, and lock-up period.

  2. Analyze with position size for gas-adjusted yields:

    bash
    python ${CLAUDE_SKILL_DIR}/scripts/staking_optimizer.py --asset ETH --amount 10

    Calculates effective APY accounting for gas costs and projects returns at 1M, 3M, 6M, and 1Y.

  3. Optimize existing portfolio with current positions:

    bash
    python ${CLAUDE_SKILL_DIR}/scripts/staking_optimizer.py --optimize \
  --positions "10 ETH @ lido 4.0%, 100 ATOM @ native 18%, 50 DOT @ native 14%"

    Suggests higher-yield alternatives with projected improvement and switching costs.

  4. Compare protocols or run risk assessment:

    bash
    python ${CLAUDE_SKILL_DIR}/scripts/staking_optimizer.py --compare --protocols lido,rocket-pool,frax-ether
python ${CLAUDE_SKILL_DIR}/scripts/staking_optimizer.py --asset ETH --detailed

  5. Export results in JSON or CSV:

    bash
    python ${CLAUDE_SKILL_DIR}/scripts/staking_optimizer.py --asset ETH --format json --output staking.json

Output

Comparison table ranked by risk-adjusted return (Net APY multiplied by Risk Score / 10), showing native and liquid staking options:

  STAKING OPTIONS FOR ETH                              2025-01-15 15:30 UTC  # 2025 timestamp
  Protocol        Type      Gross APY  Net APY  Risk   TVL         Unbond
  Frax (sfrxETH)  liquid      5.10%     4.59%   7/10   $450M       instant
  Lido (stETH)    liquid      4.00%     3.60%   9/10   $15B        instant
  Rocket Pool     liquid      4.20%     3.61%   8/10   $3B         instant
  Coinbase cbETH  liquid      3.80%     3.42%   9/10   $2B         instant
  ETH Native      native      4.00%     4.00%   10/10  $50B        variable

Error Handling

ErrorCauseSolution
API timeoutDeFiLlama unreachableCached data used with warning
Invalid assetUnknown staking assetLists supported assets
Rate limitedToo many API callsAutomatic retry with backoff
No data foundProtocol not indexedFalls back to known protocol list

See ${CLAUDE_SKILL_DIR}/references/errors.md for comprehensive error handling.

Examples

Common staking analysis workflows from single-asset comparison to full portfolio optimization:

bash
# Quick ETH staking comparison
python ${CLAUDE_SKILL_DIR}/scripts/staking_optimizer.py --asset ETH

# Large position with full risk analysis
python ${CLAUDE_SKILL_DIR}/scripts/staking_optimizer.py --asset ETH --amount 100 --detailed

# Multi-asset comparison exported to CSV
python ${CLAUDE_SKILL_DIR}/scripts/staking_optimizer.py --assets ETH,SOL,ATOM --format csv

# Portfolio optimization with current positions
python ${CLAUDE_SKILL_DIR}/scripts/staking_optimizer.py --optimize \
  --positions "50 ETH @ lido 3.6%, 500 SOL @ marinade 7.5%"  # 500 - minimum stake amount in tokens

Resources

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (scripts, references) in skills/.curated/optimizing-staking-rewards of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • ARD.md
  • PRD.md
  • config/settings.yaml
  • references/errors.md
  • references/examples.md
  • references/implementation.md
  • scripts/formatters.py
  • scripts/metrics_calculator.py
  • scripts/risk_assessor.py
  • scripts/staking_fetcher.py
  • scripts/staking_optimizer.py

Open the folder on GitHubat commit cfae287

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Questions about Optimizing Staking Rewards

What does Optimizing Staking Rewards do?

Compare and optimize staking rewards across validators, protocols, and blockchains with risk assessment. Optimizing Staking Rewards is an agent skill from jeremylongshore/tons-of-skills-marketplace. Compare and optimize staking rewards across validators, protocols, and blockchains with risk assessment.

When should I use Optimizing Staking Rewards?

Optimizing Staking Rewards fits situations like: analyzing staking opportunities; comparing validators; calculating staking rewards; optimizing PoS yields.

How do I install Optimizing Staking Rewards in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill optimizing-staking-rewards -a claude-code`. Or copy the skill folder (skills/.curated/optimizing-staking-rewards in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/optimizing-staking-rewards in your project. Claude Code loads it when a task matches its description.

How do I install Optimizing Staking Rewards in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill optimizing-staking-rewards -a codex`. Or copy the skill folder (skills/.curated/optimizing-staking-rewards in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/optimizing-staking-rewards in your project. Codex loads it when a task matches its description.

Can I use Optimizing Staking Rewards in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill optimizing-staking-rewards -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/optimizing-staking-rewards, .gemini/skills/optimizing-staking-rewards, .github/skills/optimizing-staking-rewards and .opencode/skills/optimizing-staking-rewards in your project.

What does Optimizing Staking Rewards need to run?

Going by SKILL.md and its folder, Optimizing Staking Rewards needs Python for the scripts in its folder and the command-line tools its instructions call (python and pip). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(crypto:staking-*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Optimizing Staking Rewards access the network?

SKILL.md names 4 domains. As links in the text: defillama.com, stakingrewards.com, lido.fi and rocketpool.net. This is read from the text; nothing was executed.

Is Optimizing Staking Rewards safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Optimizing Staking Rewards use?

Optimizing Staking Rewards is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Optimizing Staking Rewards use?

About 1.1k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.3k tokens, read only when the agent opens those files.

What are the alternatives to Optimizing Staking Rewards?

Skills that share tags, products or a category with Optimizing Staking Rewards: Polis Protocol A Self Optimizing City Of Agents (sickn33/agentic-awesome-skills, 47k stars), SQL Optimization (github/awesome-copilot, 40k stars), Agent Performance Optimizer (ruvnet/ruflo, 74k stars) and Form Validation (thedaviddias/Front-End-Checklist, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Optimizing Staking Rewards?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.