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Real-time Base chain alpha intelligence for ZHAO (CryptoZhaoX). Use when scanning Base memecoins for second-wave setups or early gem launches; checking GMGN smart money flows; analyzing holder distribution for a Base token; scanning Clanker or Bankr.fun for high-quality narrative token deployments; monitoring VIRTUAL Protocol AI agent launches; running the AI narrative scanner on Base; generating trade alerts on Base memecoins or mainstream assets (BTC/ETH/UNI); any on-chain analysis task on Base chain.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 812 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Real-time Base chain alpha intelligence for ZHAO (CryptoZhaoX).

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureAI and agentstype 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
95/100
safety, quality, tests
Safety 60%
97
Quality 40%
91
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Secrets in code secret-high-entropy-token references/api-endpoints.md:87
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      VIRTUAL token address (Base): `0x0b…E1b`
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:65
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - VIRTUAL token (Base): `0x0b…E1b`
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:66
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - cbBTC (Base): `0xcb…3Bf`
      quoted

    Files scanned: 5. 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 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 85Steps. 32 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 812 tokens

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 508: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.