SKILLEMALL.ai

BF system-data-intelligence

专为文件操作、数据分析、可视化、数据库连接、API 接入和敏感数据处理设计的系统级 Agent Skill。 【强制触发场景】: - 用户提及任何文件操作:Excel / WPS / Word / TXT / Markdown / RTZ / CSV / JSON - 「分析」「读取」「提取」「处理」「建模」「预测」「异常检测」 - 「生成图表」「可视化」「做仪表盘」「出报告」 - 「连数据库」「查 SQL」「查 MySQL / PostgreSQL」 - 「调 API」「从接口拉数据」「爬接口」 - 「脱敏」「敏感数据」「隐私处理」「数据安全」 【核心能力】:文件操作(自动降级)× 深度分析 × 专业可视化 × 数据库 × API × 安全脱敏 IMPORTANT: 只要涉及上述任一场景,必须使用此 skill,不得因"任务简单"而跳过。

ClawHub Agent Skills author: zhaojie v1.0.3 MIT-0 26 files body ≈ 2 004 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process F 31/100 · Will not run — weak spots: steps, result and completion, when it triggers

IntegrationPostgreSQLMySQLData and analyticsInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
F
31/100
Will not run
Steps w 15
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.
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: 20. 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")

Process rating: all ten parameters 31/100

  • 0Steps. Prose only: no discrete steps
  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2004 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)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • -32 of 9 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 375: enough signal without eating the budget
  • +4Structure: 13 headings
  • +4Has examples (21 code blocks)
  • +4Reference files are cited in the instructions (4 of 5)

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

External checks

ClawHub: suspicious
This appears to be a legitimate data-analysis skill, but it needs Review because it broadly activates on common data tasks, can use high-impact OS/database/API access, and automatically persists extracted data.
LLM: suspicious (high) · VirusTotal: · 29 May 2026