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BC visit-management

走访计划管理与提醒技能。基于真实Excel台账（美兰中心C+服务.xlsx）生成走访计划，提醒走访任务。触发场景：(1) 定时任务每日09:00生成走访提醒，(2) 每周一09:00生成本周走访计划，(3) 手动触发走访管理（@企服助手 走访管理）。

ClawHub Agent Skills author: perrykono-debug v2.0.0 MIT-0 9 files body ≈ 4 609 tokens Open the sourceclawhub.ai↗ analyzed 32 h ago

走访计划管理与提醒技能。基于真实Excel台账（美兰中心C+服务.xlsx）生成走访计划，提醒走访任务。触发场景：(1) 定时任务每日09:00生成走访提醒，(2) 每周一09:00生成本周走访计划，(3) 手动触发走访管理（@企服助手 走访管理）。

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

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
83/100
safety, quality, tests
Safety 60%
99
Quality 40%
59
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. 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token scripts/visit_manager.py:265
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    '企微群ID': 'wrkS…8nQ'
    quoted

Files scanned: 9. 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 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
  • 20When it triggers. No condition that starts the skill
  • 70Execution cost. Instruction body is 4609 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 11 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations
  • low 13 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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -216 emoji in the instructions: noise for the model
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • -35 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 125: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 11 items
  • +4Has examples (14 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.

External checks

ClawHub: clean
This skill openly reads a local customer Excel ledger to create visit plans and reminders, with sensitive but purpose-aligned local persistence and no evidence of hidden exfiltration or malware.
LLM: benign (medium) · VirusTotal: · 8 Jun 2026