SKILLEMALL.ai
GitHub ↗ RU Sign in

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.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 2 001 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Query blockchain wallet data, token prices, and transaction history using the Zerion API via its MCP connector.

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

IntegrationAI and agentsData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 3. 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 57/100

    • 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)
    • 50Failures and branches. 0 branches, has a failure section
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 41 steps, 3 vague phrases
    • 100Tools and files. No external tools needed
    • 100Execution cost. Instruction body is 2001 tokens
    • 100Progress reporting. Reports progress

    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.