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BC logistics-care

电商物流延迟检测与客户安抚短信发送技能。导入订单CSV或手动输入运单号， 自动查询物流轨迹、检测延迟风险（发货超时/运输停滞/派送异常/预计超时）， AI生成安抚话术，通过阿里云/腾讯云短信发送给客户。支持dry-run预览模式。 Triggers: 物流延迟, 快递延迟, 发货超时, 物流异常, 安抚短信, 催发货, 物流检测, 订单物流查询, 批量查快递, 物流提醒, logistics delay, shipping delay, delivery notification, 电商物流, 发货提醒.

ClawHub Agent Skills author: bettermen v1.0.0 MIT-0 8 files body ≈ 602 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

电商物流延迟检测与客户安抚短信发送技能。导入订单CSV或手动输入运单号， 自动查询物流轨迹、检测延迟风险（发货超时/运输停滞/派送异常/预计超时）， AI生成安抚话术，通过阿里云/腾讯云短信发送给客户。支持dry-run预览模式。 Triggers: 物流延迟, 快递延迟, 发货超时, 物流异常, 安抚短信…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationLogistics and warehousetype 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
85/100
safety, quality, tests
Safety 60%
90
Quality 40%
78
Run on models
none yet
Process rating
C
51/100
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 2

✓ No critical or high findings

Medium and low: 2
  • medium Dangerous commands cmd-eval-dynamic scripts/logistics_checker.py:23
    Dynamic code execution from decoded/untrusted input
    os.system(f"{sys.executable} -m pip install requests -i https://pypi.tuna.tsinghua.edu.cn/simple/ --trusted-host pypi.tuna.tsinghua.edu.cn")
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Write Edit Bash WebFetch WebSearch

Files scanned: 8. 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 "agent_created"

Process rating: all ten parameters 51/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 21 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 602 tokens
  • 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 255: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 21 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: suspicious
The skill matches its logistics/SMS purpose, but it handles customer data and SMS credentials with too few safety guardrails.
LLM: suspicious (high) · VirusTotal: · 15 Jun 2026