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AB okx-dex-token

Use this skill for token-level data: search tokens, trending/hot tokens (热门, 代币榜单), liquidity pools, holder distribution (whale/巨鲸, sniper, bundler-tagged holder %), token risk metadata (riskControlLevel, tokenTags, dev stats, suspicious/bundle holding % via advanced-info), recent buy/sell activity, trade feed/逐笔成交/每笔交易/stream trades, top profit addresses, token trade history, detailed price info with market cap volume liquidity and holder count (price-info), or holder cluster analysis (持仓集中度, cluster overview, cluster rug pull risk/跑路风险, new wallet percentage/新钱包持仓比例, holder clusters, 'are top holders in same cluster'). Also handles Market API payment/计费/x402/402, Basic/Premium tier/quota/额度 questions, and MARKET_API_*_OVER_QUOTA / confirming:true responses on token endpoints (advanced-info, top-trader, cluster-*, trades, hot-tokens). NOTE: if the user wants to write a WebSocket script/脚本/bot, use okx-dex-ws instead.

ClawHub Agent Skills author: ok-james-01 v3.1.3 MIT-0 7 files body ≈ 2 406 tokens Open the sourceclawhub.ai↗ analyzed 16 h ago

Use this skill for token-level data: search tokens, trending/hot tokens (热门, 代币榜单), liquidity pools, holder distribution (whale/巨鲸, sniper, bundler-tagged…

As a process B 71/100 · Nearly there — weak spots: result and completion, when it triggers, progress reporting

ProcedureInfrastructureSales and CRMSoftware developmenttype 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
91/100
safety, quality, tests
Safety 60%
95
Quality 40%
86
Run on models
none yet
Process rating
B
71/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token references/cli-reference.md:401
      High-entropy token-like string (may be an id, hash or a credential)
      onchainos token cluster-overview --address EPjF…t1v --chain solana
    • low Secrets in code secret-high-entropy-token references/cli-reference.md:441
      High-entropy token-like string (may be an id, hash or a credential)
      onchainos token cluster-top-holders --address EPjF…t1v --chain solana --range-filter 3
    • low Secrets in code secret-high-entropy-token references/cli-reference.md:495
      High-entropy token-like string (may be an id, hash or a credential)
      onchainos token cluster-list --address EPjF…t1v --chain solana
    • low Secrets in code secret-high-entropy-token references/ws-protocol.md:159
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "tokenContractAddress": "HeLp…jwC"
      quoted
    • low Secrets in code secret-high-entropy-token references/ws-protocol.md:224
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "tokenContractAddress": "HeLp…jwC"
      quoted

    Files scanned: 7. 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 71/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 28 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2406 tokens
    • 100Running it twice. No mutating operations
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 14 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (3 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 931: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 28 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: suspicious
    The skill is mostly coherent for OKX token research, but it automatically downloads and runs a latest-version installer from GitHub, which should be reviewed before installation.
    LLM: suspicious (high) · VirusTotal: · 11 Sept 2026