optimizing-gas-fees
Checks real-time gas prices, estimates transaction costs, and identifies optimal timing for executing on-chain operations.
Install
mkdir -p .claude/skills/optimizing-gas-fees && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/6614" && unzip -o skill.zip -d .claude/skills/optimizing-gas-fees && rm skill.zipInstalls to .claude/skills/optimizing-gas-fees
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Optimize blockchain gas costs by analyzing prices, patterns, and timing.Key capabilities
- →Fetch real-time gas prices across multiple chains
- →Estimate transaction costs in ETH and USD
- →Analyze historical hourly and daily gas patterns
- →Predict future gas prices for specific hours
- →Compare gas fees across different blockchains
How it works
The skill utilizes Python scripts to query RPC endpoints for real-time gas metrics and historical block data. It processes this data to provide cost estimations and predictive analysis for various blockchain operations.
Inputs & outputs
When to use optimizing-gas-fees
- →Estimate gas costs before initiating transactions
- →Find optimal windows for low-cost token swaps
- →Compare gas fees across different chains
- →Automate monitoring of gas price spikes
About this skill
Optimizing Gas Fees
Overview
Gas fee optimization skill that:
- Fetches real-time gas prices from multiple sources
- Estimates transaction costs in ETH and USD
- Analyzes historical patterns to find optimal timing
- Predicts future gas prices
- Compares gas across multiple chains
Prerequisites
- Python 3.8+ with requests library
- Network access to RPC endpoints
- Optional:
ETHERSCAN_API_KEYfor higher rate limits - Optional: Custom RPC URLs via environment variables
Instructions
-
Check current gas prices (optionally for a specific chain):
cd ${CLAUDE_SKILL_DIR}/scripts && python3 gas_optimizer.py current cd ${CLAUDE_SKILL_DIR}/scripts && python3 gas_optimizer.py current --chain polygon -
Estimate transaction cost for known operations or custom gas limits (available operations:
eth_transfer,erc20_transfer,erc20_approve,uniswap_v2_swap,uniswap_v3_swap,sushiswap_swap,curve_swap,nft_mint,nft_transfer,opensea_listing,aave_deposit,aave_withdraw,compound_supply,compound_borrow,bridge_deposit):cd ${CLAUDE_SKILL_DIR}/scripts && python3 gas_optimizer.py estimate --operation uniswap_v2_swap --all-tiers cd ${CLAUDE_SKILL_DIR}/scripts && python3 gas_optimizer.py estimate --gas-limit 150000 --tier fast # 150000 = configured value -
Find the optimal transaction window with lowest expected gas:
cd ${CLAUDE_SKILL_DIR}/scripts && python3 gas_optimizer.py optimal -
View gas patterns (hourly or daily):
cd ${CLAUDE_SKILL_DIR}/scripts && python3 gas_optimizer.py patterns cd ${CLAUDE_SKILL_DIR}/scripts && python3 gas_optimizer.py patterns --daily -
Predict future gas prices for a given hour:
cd ${CLAUDE_SKILL_DIR}/scripts && python3 gas_optimizer.py predict --time 14 -
Compare gas prices across multiple chains:
cd ${CLAUDE_SKILL_DIR}/scripts && python3 gas_optimizer.py compare -
View base fee history for recent blocks:
cd ${CLAUDE_SKILL_DIR}/scripts && python3 gas_optimizer.py history --blocks 50
Output
- Current: Base fee, priority fee, and tier prices (slow/standard/fast/instant)
- Estimate: Gas cost in native token and USD for each tier
- Patterns: Historical hourly/daily patterns with low-gas markers
- Optimal: Recommended transaction window with expected savings
- Predict: Gas forecast for specific time with confidence
- Compare: Side-by-side gas prices across chains
Supported Chains
| Chain | Native Token | Block Time |
|---|---|---|
| Ethereum | ETH | ~12 sec |
| Polygon | MATIC | ~2 sec |
| Arbitrum | ETH | ~0.25 sec |
| Optimism | ETH | ~2 sec |
| Base | ETH | ~2 sec |
Price Tiers
| Tier | Percentile | Confirmation Time |
|---|---|---|
| Slow | 10th | 10+ blocks (~2+ min) |
| Standard | 50th | 3-5 blocks (~1 min) |
| Fast | 75th | 1-2 blocks (~30 sec) |
| Instant | 90th | Next block (~12 sec) |
Error Handling
See ${CLAUDE_SKILL_DIR}/references/errors.md for:
- RPC connection issues
- API rate limiting
- Price feed errors
- Pattern analysis errors
Examples
See ${CLAUDE_SKILL_DIR}/references/examples.md for:
- Quick start commands
- Cost estimation scenarios
- Multi-chain comparison
- Practical workflows
Resources
- EIP-1559 - Fee market specification
- Etherscan Gas Tracker - Reference oracle
- L2Fees - L2 cost comparison
Prerequisites
Limitations
- →Requires network access to RPC endpoints
- →Dependent on RPC connection stability and API rate limits
How it compares
Unlike manual checking of block explorers, this skill automates multi-chain comparisons and provides predictive modeling for optimal transaction timing.
Compared to similar skills
optimizing-gas-fees side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| optimizing-gas-fees (this skill) | 1 | 25d | Review | Intermediate |
| pdf-processing-pro | 17 | 9mo | Review | Intermediate |
| python-repl | 6 | 4mo | Review | Beginner |
| math-router | 6 | 7mo | Review | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by jeremylongshore
View all by jeremylongshore →You might also like
pdf-processing-pro
davila7
Production-ready PDF processing with forms, tables, OCR, validation, and batch operations. Use when working with complex PDF workflows in production environments, processing large volumes of PDFs, or requiring robust error handling and validation.
python-repl
gptme
Interactive Python REPL automation with common helpers and best practices
math-router
parcadei
Deterministic router for math cognitive stack - maps user intent to exact CLI commands
tooluniverse-sdk
mims-harvard
Build AI scientist systems using ToolUniverse Python SDK for scientific research. Use when users need to access 1000++ scientific tools through Python code, create scientific workflows, perform drug discovery, protein analysis, genomics analysis, literature research, or any computational biology task. Triggers include requests to use scientific tools programmatically, build research pipelines, analyze biological data, search literature, predict drug properties, or create AI-powered scientific workflows.
airflow
ComeOnOliver
Apache Airflow lets you define workflows as Directed Acyclic Graphs (DAGs) in Python. Each DAG consists of tasks connected by dependencies, scheduled and monitored via a web UI.
ahc-optuna
toast-uz
AHC のハイパーパラメータ最適化（`eval.py -o`）を安全に回すための skill。