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Get Started Free →Monitor blockchain mempools for pending transactions, gas analysis, and MEV opportunities. Use when analyzing pending transactions, optimizing gas prices, or researching MEV. Trigger with phrases like "check mempool", "scan pending txs", "find MEV", "gas price analysis", or "pending swaps".
.claude/skills/jeremylongshore-analyzing-mempool/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-12 | ✗→✓ | ▲ Improved | -50% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -49% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 75% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 73% | 0% |
Overview | Prerequisites | Instructions | Output | Error Handling | Examples | Resources
Monitor Ethereum mempool for pending transactions, analyze gas prices, detect DEX swaps, and identify potential MEV opportunities. Useful for traders, MEV researchers, and protocol developers.
ETH_RPC_URL)bashcd ${CLAUDE_SKILL_DIR}/scripts
python mempool_analyzer.py pendingpython mempool_analyzer.py gaspython mempool_analyzer.py swapspython mempool_analyzer.py mevpython mempool_analyzer.py summarypython mempool_analyzer.py watch 0x7a250d...Alternatively, customize with flags:
bashpython mempool_analyzer.py pending --limit 100 # Limit results python mempool_analyzer.py --chain polygon gas # Use different chain python mempool_analyzer.py --chain arbitrum pending # Or use Arbitrum
Gas Recommendations:
MEV Warnings:
--format json)See ${CLAUDE_SKILL_DIR}/references/errors.md for:
Example 1: Check gas before sending transaction:
bashpython mempool_analyzer.py gas # Use "Fast" for quick confirmation
Example 2: Monitor for large pending swaps:
bashpython mempool_analyzer.py swaps --limit 200 # 200: max results to scan
Example 3: Research MEV opportunities:
bashpython mempool_analyzer.py mev -v
See ${CLAUDE_SKILL_DIR}/references/examples.md for more usage patterns.
${CLAUDE_SKILL_DIR}/references/implementation.md - Gas analysis, MEV detection, multi-chain details| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-12 | fail→pass | 12,034 | 2,291 | -81% | 1 | 1 | 0% | 2,357 | 1,176 | -50% | 0 | 0 | — |
case-13 | fail→pass | 12,532 | 1,893 | -85% | 1 | 1 | 0% | 2,231 | 1,133 | -49% | 0 | 0 | — |
case-01 | fail→pass | 6,822 | 7,557 | +11% | 1 | 1 | 0% | 1,270 | 2,217 | +75% | 0 | 0 | — |
case-02 | fail→pass | 11,392 | 4,927 | -57% | 1 | 1 | 0% | 2,124 | 1,659 | -22% | 0 | 0 | — |
case-03 | fail→pass | 11,591 | 16,205 | +40% | 1 | 1 | 0% | 2,129 | 3,679 | +73% | 0 | 0 | — |
case-04 | fail→pass | 15,917 | 3,271 | -79% | 1 | 1 | 0% | 3,388 | 1,407 | -58% | 0 | 0 | — |
case-05 | fail→pass | 10,025 | 1,713 | -83% | 1 | 1 | 0% | 2,079 | 1,099 | -47% | 0 | 0 | — |
case-06 | fail→pass | 13,525 | 3,840 | -72% | 1 | 1 | 0% | 2,872 | 1,621 | -44% | 0 | 0 | — |
case-07 | pass→pass | 8,679 | 1,969 | -77% | 1 | 1 | 0% | 1,548 | 1,165 | -25% | 0 | 0 | — |
case-08 | fail→pass | 9,600 | 1,659 | -83% | 1 | 1 | 0% | 1,655 | 1,102 | -33% | 0 | 0 | — |
case-09 | fail→pass | 6,365 | 1,581 | -75% | 1 | 1 | 0% | 1,099 | 1,058 | -4% | 0 | 0 | — |
case-10 | pass→pass | 6,690 | 948 | -86% | 1 | 1 | 0% | 1,171 | 928 | -21% | 0 | 0 | — |
case-11 | fail→pass | 10,971 | 2,204 | -80% | 1 | 1 | 0% | 2,047 | 1,254 | -39% | 0 | 0 | — |
case-14 | fail→pass | 11,118 | 1,549 | -86% | 1 | 1 | 0% | 1,902 | 1,107 | -42% | 0 | 0 | — |
case-15 | pass→pass | 7,833 | 1,611 | -79% | 1 | 1 | 0% | 1,270 | 1,106 | -13% | 0 | 0 | — |
case-16 | fail→pass | 13,001 | 2,848 | -78% | 1 | 1 | 0% | 2,504 | 1,360 | -46% | 0 | 0 | — |
case-17 | fail→pass | 10,904 | 2,987 | -73% | 1 | 1 | 0% | 1,953 | 1,291 | -34% | 0 | 0 | — |
case-18 | fail→pass | 6,664 | 1,348 | -80% | 1 | 1 | 0% | 1,251 | 974 | -22% | 0 | 0 | — |
case-19 | pass→pass | 7,107 | 1,976 | -72% | 1 | 1 | 0% | 1,202 | 1,064 | -11% | 0 | 0 | — |
case-20 | pass→pass | 7,988 | 5,688 | -29% | 1 | 1 | 0% | 1,656 | 2,014 | +22% | 0 | 0 | — |
case-21 | pass→pass | 9,056 | 23,493 | +159% | 1 | 1 | 0% | 1,949 | 2,597 | +33% | 0 | 0 | — |
case-22 | pass→pass | 12,164 | 13,484 | +11% | 1 | 1 | 0% | 2,456 | 3,452 | +41% | 0 | 0 | — |
case-23 | fail→pass | 4,347 | 1,751 | -60% | 1 | 1 | 0% | 683 | 1,088 | +59% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +70 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.