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BF ethereum-l2-analytics

以太坊 Layer 2 生态综合分析工具。提供 Optimism、Arbitrum、Base、zkSync、Starknet 等 Ethereum L2 协议的深度分析、TVL监控、技术对比、跨链桥分析和投资机会识别。当用户需要分析 Ethereum L2 生态、评估 Rollup 项目、监控 L2 资金流向、发现 L2 投资机会或获取以太坊二层网络情报时触发此 Skill。

ClawHub Agent Skills author: shenmeng v1.0.0 MIT-0 8 files body ≈ 1 255 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process F 31/100 · Will not run — References files that are not bundled: references/airdrops.md, scripts/tech_comparator.py, scripts/bridge_analyzer.py

ProcedureInfrastructureData 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
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
F
31/100
Will not run
References files that are not bundled: references/airdrops.md, scripts/tech_comparator.py, scripts/bridge_analyzer.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
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: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: references/airdrops.md
  • warning missing-ref reference to a missing file: scripts/tech_comparator.py
  • warning missing-ref reference to a missing file: scripts/bridge_analyzer.py

Process rating: all ten parameters 31/100

Will not run. References files that are not bundled: references/airdrops.md, scripts/tech_comparator.py, scripts/bridge_analyzer.py
  • 0Tools and files. 3 referenced file(s) missing: references/airdrops.md, scripts/tech_comparator.py, scripts/bridge_analyzer.py
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (ethereum-l2-analytics) differs from the folder (shenmeng-ethereum-l2-analytics)
  • 100Steps. 96 steps
  • 100Execution cost. Instruction body is 1255 tokens
  • 100Running it twice. No mutating operations
  • low 12 top-level sections: this looks like several domains in one skill

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 189: enough signal without eating the budget
  • +4Structure: 55 headings
  • +3Step-by-step instructions: 96 items
  • +4Has examples (18 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: 67.

External checks

ClawHub: suspicious
This Ethereum L2 analytics skill mostly matches its stated purpose, but it has an under-disclosed paid SkillPay verification flow that can send wallet data to an external service and uses an embedded API key.
LLM: suspicious (high) · 28 May 2026