AC zerion-api
Query blockchain wallet data, token prices, and transaction history using the Zerion API via its MCP connector. Use this skill whenever the user asks about: crypto wallet balances, portfolio values, token holdings or positions, DeFi positions (staking, lending, LP), wallet PnL (profit and loss), transaction history, token/fungible asset prices or charts, NFT holdings or NFT portfolio value, or any web3 wallet analytics. Triggers on mentions of wallet addresses (0x...), ENS names, token names/symbols, "portfolio", "positions", "PnL", "transactions", "balance", "holdings", "NFTs", or any crypto/DeFi analytics queries. Also use when building artifacts or dashboards that display wallet or token data.
As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
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 64/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (zerion-api) differs from the folder (zerion-api-skill-2)
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 41 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Execution cost. Instruction body is 1946 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +3Description length 705: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 41 items
- +4Has examples (13 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.