SKILLEMALL.ai

BC huo15-furniture-mfg

Use this skill whenever the user asks about furniture manufacturing operations on the HeySleep/和栖家居 system — order tracking ("这个订单到哪一步了" "SO-260531-758 什么情况" "梁泽光的单发了吗" "哪些订单还没发货"), production ("在制有多少" "生产进度" "哪些制造单延期了" "MO-xxx 缺什么料" "欠料/缺料"), inventory ("山隐主垫还有几张" "库存查询" "型号 C3 有货吗"), quality ("待检" "质检合格率" "最近有没有不合格"), purchasing ("采购到货了吗" "哪些采购单逾期"), customers ("亿美诺的档案" "这个客户成交多少"), daily overview ("今日总览" "晨报" "今天接了几单/发了几单"), write actions ("给这个单留言" "@张三跟进" "提醒冯广权周一回复客户" "交期改到20号" "生产提前到周三" "改这单报价" "把这个客户存一下" "工艺要求发给车间" "这个跟进做完了"), or documents ("打印合同" "发我唛头标签" "要个施工单 PDF"). Backed by the 和栖家居 manufacturing system (test.heysleep.cn, db=test, 辉火云企业套件 v19) over XML-RPC. Also handles photo QC ("把这几张照片挂到质检单" "质检判合格"), custom-size quotations ("客户要 2 米乳胶床垫报个价" "建个报价草稿"), and channel analysis ("电商单占多少" "渠道分布"). Also triggers on: 和栖, heysleep, 床垫订单, 跟单, 交期, 制造单, 生产单, 发货状态, 山隐, 山宿, 销售合约, 唛头, 拍照质检, 非标报价. First run: python3 scripts/login.py init (地址/数据库/账号/密码 → ~/.huo15/tools.md, chmod 600). Pure standard library, zero dependencies. Write actions are two-step confirm-gated (dry-run preview → user approves → --yes).

ClawHub Agent Skills author: Job Zhao v1.4.0 MIT-0 21 files body ≈ 1 181 tokens Open the sourceclawhub.ai analyzed 2 d ago

Use this skill whenever the user asks about furniture manufacturing operations on the HeySleep/和栖家居 system — order tracking ("这个订单到哪一步了" "SO-260531-758 什么情况"…

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerManufacturingtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
82/100
safety, quality, tests
Safety 60%
100
Quality 40%
54
Run on models
none yet
Process rating
C
52/100
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. Shorten the description to 1024 characters.
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: 21. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1123 chars, limit 1024
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "aliases"
  • note frontmatter-key unknown frontmatter key "dependencies"

Process rating: all ten parameters 52/100

  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 19 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1181 tokens
  • 100Running it twice. No mutating operations

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)
  • +3Description length 1123: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • -32 of 14 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 27 example trigger phrases
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 19 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 54.

External checks

ClawHub: suspicious
The skill is a legitimate ERP assistant, but it needs review because some write and delete capabilities are broader than the documentation claims.
LLM: suspicious (high) · VirusTotal: · 13 Jun 2026