Atlas / Skills / jeremylongshore / Optimizing Staking Rewards

Optimizing Staking RewardsSAFE

skills/jeremylongshore/optimizing-staking-rewards

Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
1.26.0
Hosts
1 documented
License
MIT
Stars
2,823
01

Overview

Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.

Read from source at commit 4f83675ca38aOBSERVED · 2026-10-08
02

Install

Commands as the repository documents them. They are shown, not run.

pip install requests
03

Host compatibility

What the documentation claims. We have not run a compatibility test.

HostStatusNotes
claude-codementioned
04

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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-*)
version: 1.26.0
author: Jeremy Longshore <[email protected]>
license: MIT
tags:
- crypto
- optimizing-staking
compatibility: Designed for Claude Code
---
# 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

| Error | Cause | Solution |
|-------|-------|----------|
| API timeout | DeFiLlama unreachable | Cached data used with warning |
| Invalid asset | Unknown staking asset | Lists supported assets |
| Rate limited | Too many API calls | Automatic retry with backoff |
| No data found | Protocol not indexed | Falls 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

- `${CLAUDE_SKILL_DIR}/references/implementation.md` - Optimization reports, risk assessment details, disclaimers
- `${CLAUDE_SKILL_DIR}/references/errors.md` - Comprehensive error handling
- DeFiLlama Yields: https://defillama.com/yields
- StakingRewards: https://www.stakingrewards.com
- Lido: https://lido.fi | Rocket Pool: https://rocketpool.net
05

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codePASS
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
declared (1 observation(s))
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (1)

INFOPrompt injection · prompt.persistence · CWE-94, CWE-1427
references/examples.md:315
# Add to crontab for daily report
Why it matters. instructs the agent to persist itself in the user's environment

Gates applied: no_behavioural_pass.

Audited 2026-10-08 · audit v0.4.1 · source sha 4f83675ca38afull audit observations/trust-audit/skill/jeremylongshore__optimizing-staking-rewards.json · Report an issue / request a re-scan
06

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-084f83675ca38aSAFEB89first audit
07

Questions

What does the Optimizing Staking Rewards skill do?

Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.

Is Optimizing Staking Rewards safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Optimizing Staking Rewards access on my machine?

The audit observed that it reaches the network. Each of those is consistent with what it says it does. Secrets in the source: none found.

Which assistants does Optimizing Staking Rewards work with?

Its documentation mentions claude-code. That is what the text claims, not a compatibility test we ran.

How current is this page?

The grade is for one exact copy of the source (4f83675ca38a), read on 2026-10-08. The repository is watched, and a new audit runs when it changes — this is the first audit.

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