SKILLEMALL.ai

AB okx-wallet-portfolio

Use this skill when the user provides a specific wallet address and wants to check its balance, token holdings, portfolio value, or DeFi positions. Typical triggers: 'check balance of 0xAbc...', 'show tokens in this address', 'what tokens does 0xAbc hold', 'portfolio value of this address', address portfolio value, multi-chain balance lookup for a given address. Supports XLayer, Solana, Ethereum, Base, BSC, Arbitrum, Polygon, and 20+ other chains. Do NOT use when the user asks about their own wallet without providing an address (e.g., 'check my wallet balance', 'show my assets', '查看我的余额') — use okx-agentic-wallet instead, which queries the logged-in wallet. Do NOT use for PnL analysis, DEX history, realized/unrealized profit — use okx-dex-market. Do NOT use for signal tracking — use okx-dex-signal. Do NOT use for meme scanning — use okx-dex-trenches. Do NOT use for programming questions about balance APIs or integration.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 3 files body ≈ 3 938 tokens Open the sourcegithub.com analyzed 2 d ago

Typical triggers: 'check balance of 0xAbc...', 'show tokens in this address', 'what tokens does 0xAbc hold', 'portfolio value of this address', address…

As a process B 70/100 · Nearly there — weak spots: result and completion, running it twice

ProcedureGitHubAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
B
70/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
Tools and files w 18
60
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 70/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 61 steps, 2 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3938 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    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
    • +3Description length 934: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 61 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.