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AB okx-dex-market

Use this skill for on-chain market data: token prices/价格, K-line/OHLC charts, index prices, and wallet PnL/盈亏分析 (win rate, my DEX trade history, realized/unrealized PnL per token). Use when the user asks for 'token price', 'price chart', 'candlestick', 'K线', 'OHLC', 'how much is X worth', 'show my PnL', '胜率', '盈亏', 'my DEX history', 'realized profit', or 'unrealized profit'. Do NOT use for smart-money/whale/KOL signal tracking — use okx-dex-signal. Do NOT use for meme/pump.fun token scanning — use okx-dex-trenches. Do NOT use for token search, holder distribution, liquidity pools, or honeypot checks — use okx-dex-token.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 3 files body ≈ 5 729 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Use this skill for on-chain market data: token prices/价格, K-line/OHLC charts, index prices, and wallet PnL/盈亏分析 (win rate, my DEX trade history…

As a process B 69/100 · Nearly there — weak spots: result and completion, running it twice

ProcedureGitHubInfrastructureSoftware developmentData 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
90/100
safety, quality, tests
Safety 60%
95
Quality 40%
83
Run on models
none yet
Process rating
B
69/100
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

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 5

✓ No critical or high findings

Medium and low: 5
  • low Secrets in code secret-high-entropy-token SKILL.md:110
    High-entropy token-like string (may be an id, hash or a credential)
    onchainos market kline --address EPjF…t1v --chain solana --bar 1H --limit 24
  • low Secrets in code secret-high-entropy-token SKILL.md:116
    High-entropy token-like string (may be an id, hash or a credential)
    onchainos market portfolio-overview --address 0xd8…045 --chain ethereum --time-frame 3
  • low Secrets in code secret-high-entropy-token SKILL.md:119
    High-entropy token-like string (may be an id, hash or a credential)
    onchainos market portfolio-dex-history --address 0xd8…045 --chain ethereum \
  • low Secrets in code secret-high-entropy-token SKILL.md:123
    High-entropy token-like string (may be an id, hash or a credential)
    onchainos market portfolio-recent-pnl --address 0xd8…045 --chain ethereum
  • low Secrets in code secret-high-entropy-token SKILL.md:126
    High-entropy token-like string (may be an id, hash or a credential)
    onchainos market portfolio-token-pnl --address 0xd8…045 --chain ethereum \

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5729 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 69/100

  • 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
  • 70Execution cost. Instruction body is 5729 tokens
  • 85Steps. 62 steps, 1 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
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 15 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (4 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
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 627: enough signal without eating the budget
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 62 items
  • +4Has examples (6 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: 83.