SKILLEMALL.ai
RU Sign in

BF data-skill

专门处理日常办公场景下的高频、复杂数据分析与处理的助手。使用本地代码执行模式（SQL 或 Python + SQLite）来处理数据导入、清洗、查询、提取、合并拆分及报告生成，支持大数据量且保障数据隐私安全。当用户需要处理 Excel/CSV 文件、跨表查询、生成图表或输出数据分析报告时使用此 Skill。

ClawHub Agent Skills author: lgwanai v1.0.0 MIT-0 80 files body ≈ 1 698 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process F 39/100 · Will not run — References files that are not bundled: scripts/chart_generator.py, references/prompts/line/stacked_area.md, references/metrics.md

ProcedureExcelData 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
82/100
safety, quality, tests
Safety 60%
99
Quality 40%
56
Run on models
none yet
Process rating
F
39/100
Will not run
References files that are not bundled: scripts/chart_generator.py, references/prompts/line/stacked_area.md, references/metrics.md
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token references/prompts/surface/image_surface_sushuang.md:92
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    'data:image/jpeg;charset=utf-8;base64,/9j/4AAQ…AAD/4QFU…UAA
    quoted

Files scanned: 80. 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: scripts/chart_generator.py
  • warning missing-ref reference to a missing file: references/prompts/line/stacked_area.md
  • warning missing-ref reference to a missing file: references/metrics.md
  • warning missing-ref reference to a missing file: scripts/metrics_manager.py

Process rating: all ten parameters 39/100

Will not run. References files that are not bundled: scripts/chart_generator.py, references/prompts/line/stacked_area.md, references/metrics.md
  • 0Tools and files. 4 referenced file(s) missing: scripts/chart_generator.py, references/prompts/line/stacked_area.md, references/metrics.md
  • 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. 9 mutating operations with no state check
  • 65Failures and branches. 3 branches
  • 100Steps. 29 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1698 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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 154: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 29 items
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 56.

External checks

ClawHub: suspicious
This is a real local data-analysis skill, but it needs review because its privacy-focused claims are broader than its actual network, server, cleanup, and persistence behavior.
LLM: suspicious (high) · VirusTotal: · 29 May 2026