SKILLEMALL.ai
GitHub ↗ RU Sign in

BC auto-bookkeeping

智能自动记账助手。一句话即可完成记账，自动识别时间、金额、分类。 支持自然语言输入（如"昨天午饭30"），无需手动填写日期/类型。 本地 SQLite 存储，生成月度可视化 HTML 报告。 触发词：记账, 消费, 花了, 买了, 收到, 今天花, 记一笔, 查账, 账单报告, auto-bookkeeping, 支出, 收入。

ClawHub Agent Skills author: bettermen v1.0.0 MIT-0 2 files body ≈ 654 tokens Open the sourceclawhub.ai↗ analyzed 33 h ago

智能自动记账助手。一句话即可完成记账，自动识别时间、金额、分类。 支持自然语言输入（如"昨天午饭30"），无需手动填写日期/类型。 本地 SQLite 存储，生成月度可视化 HTML 报告。 触发词：记账, 消费, 花了, 买了, 收到, 今天花, 记一笔, 查账, 账单报告, auto-bookkeeping…

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

ProcedureSoftware developmentData and analyticsWriting and documentstype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 2. 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")
  • note frontmatter-key unknown frontmatter key "display_name"

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
  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 13 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 654 tokens
  • 100Running it twice. No mutating operations
  • low 10 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 165: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 13 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
This bookkeeping skill is not malicious, but it should be reviewed because it can record, reveal, report, and delete personal financial data from broad conversational triggers.
LLM: suspicious (high) · VirusTotal: · 14 Jun 2026