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BD shejian

舌尖香港门店AI助手（支持环境变量） 当用户发送任何与门店运营相关的中文信息时触发，包括但不限于： - 报告商品库存状态，如"番茄卖完了"、"胡萝卜还剩5斤"、"白菜今天卖了20斤" - 查询今日库存、销售情况、进货记录、操作日志 - 录入进货信息，如"今天收到50斤胡萝卜" - 查询门店所在城市天气，如"明天天气如何"、"会下雨吗" API Token 从各 agent workspace 的 `SHEJIAN.md` 中读取（由 AGENTS.md 启动时指引读取）。Base URL 默认 `https://s.xingke888.com`。 通过调用舌尖香港门店管理系统的API，帮助用户完成库存查询、销售补录、进货管理、天气查询等操作。

ClawHub Agent Skills author: Xiaobing Mi v1.0.6 MIT-0 3 files body ≈ 1 217 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentsInfrastructuretype 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
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
75
Run on models
none yet
Process rating
D
41/100
Unfinished process
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
  • 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 Exfiltration net-credential-use SKILL.md:41
    Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)
    curl -s "https://s.xi…com/api/inventory" -H "Authorization: Bearer $SHEJ…5v5"
    vendor-host

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")

Process rating: all ten parameters 41/100

  • 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
  • 40Consistency. Frontmatter name (shejian) differs from the folder (store-ai)
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 100Steps. 88 steps
  • 100Execution cost. Instruction body is 1217 tokens
  • 100Running it twice. No mutating operations
  • medium 5 test cases, all positive: not one "should refuse" or "should ask first"
  • low No test case covers injection arriving through data

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -214 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 326: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 88 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This is a disclosed store-management assistant that uses a bearer token to query and update the intended store API, with operational risk users should understand.
LLM: benign (high) · VirusTotal: · 29 May 2026