AC airdrop-eligibility-tracker
Scores a crypto wallet's on-chain activity across Ethereum, Arbitrum, Base, Optimism, and Polygon against common retroactive airdrop farming heuristics (transaction count, multi-chain diversity, wallet balance, account activity) using free public JSON-RPC endpoints with no API key required. Useful for crypto airdrop farming, defi power users, whale-style multi-wallet trackers, passive income seekers, python developers, and ai agent operators who want a quick 0-100 eligibility health-check before a snapshot. Supports scoring a single wallet or comparing several wallets side by side to see which ones have the strongest multi-chain footprint. Built from the generic criteria protocols like Arbitrum, Optimism, zkSync, and Blast have historically leaned on — cross-chain tx volume, distinct chain presence, and account age/balance — not any single project's unpublished real snapshot rules.
Scores a crypto wallet's on-chain activity across Ethereum, Arbitrum, Base, Optimism, and Polygon against common retroactive airdrop farming heuristics…
As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 54/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 8 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 568 tokens
- 100Running it twice. Mutating operations check current state
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 894: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 6 headings
- +3Step-by-step instructions: 8 items
- +4Has examples (1 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.