SKILLEMALL.ai
RU Sign in

BC ddt-tencent-map-retail-network

零售品牌门店规模、业态结构、区域覆盖与候选点机会分析。 可将腾讯地图中复制出的地点名称和地址文本作为地点输入；基于店店通已发布门店快照生成可核验结论。本 Skill 非腾讯地图官方产品，和腾讯地图不存在合作、授权或数据来源关系。

ClawHub Agent Skills author: horacetu v1.0.0 MIT-0 2 files body ≈ 785 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

零售品牌门店规模、业态结构、区域覆盖与候选点机会分析。 可将腾讯地图中复制出的地点名称和地址文本作为地点输入；基于店店通已发布门店快照生成可核验结论。本 Skill 非腾讯地图官方产品，和腾讯地图不存在合作、授权或数据来源关系。

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

ProcedureMarketingtype 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
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
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 "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "homepage"

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. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 785 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 114: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: clean
This skill is a scoped retail-store analysis connector that uses a disclosed API key and API endpoints without hidden installation, persistence, or unrelated data access.
LLM: benign (high) · VirusTotal: · 10 Aug 2026