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DD madrid-trademark

马德里商标国际注册（马德里体系）全流程实务技能，面向全球商标代理师、知识产权律师、企业法务与品牌权利人；以 WIPO 国际阶段程序为主体、各缔约方通用，另设 CNIPA / 中国衔接专章。覆盖国际注册申请、后期指定、临时驳回与异议应对、续展、变更/转让/删减/放弃/分割/合并、许可备案、规费与单独规费计算、中心攻击转化、缔约方声明与注册状态查询、MGS 商品核实、CNIPA 快速审查。触发词：马德里、马德里商标、马德里国际注册、马德里体系、商标国际注册、马德里后期指定、马德里转让、商标删减、部分放弃、单独规费、国际注册续展、国际注册变更、国际注册驳回、中心攻击、马德里转化、马德里代理人、使用意图声明、Madrid Monitor、ROMARIN、MGS、eMadrid、WIPO 规费计算器、madrid、madrid trademark、madrid protocol、madrid system、madrid renewal、madrid refusal、madrid subsequent designation、madrid transformation、madrid assignment、madrid limitation、madrid division、madrid merger、madrid individual fee、madrid fee calculator、madrid declarations、international trademark。

Not recommendedcritical or high security findings · low grade D
ClawHub Agent Skills author: huiningip v3.7.7 MIT-0 31 files · 7 scripts body ≈ 6 915 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

马德里商标国际注册（马德里体系）全流程实务技能，面向全球商标代理师、知识产权律师、企业法务与品牌权利人；以 WIPO 国际阶段程序为主体、各缔约方通用，另设 CNIPA /…

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorLegaltype 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
D
76/100
safety, quality, tests
Safety 60%
82
Quality 40%
66
Run on models
none yet
Process rating
D
44/100
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

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Obfuscation
If you install

The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.

For the author

Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  3. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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

  • high Obfuscation uni-zero-width references/madrid-regulations.md:175
    Zero-width / invisible characters (possible hidden text) (35 occurrences)
    (xvi) “被指定缔约方”指依议定书第3条之三第(1)或(2)款，已对其请求给予延伸保护（“领土延伸”）、或对其请求的此种延伸已在国际注册簿登记的缔约␀方；

Files scanned: 31. 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")
  • warning body-long SKILL.md body ≈ 6915 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "display_name_en"
  • note frontmatter-key unknown frontmatter key "description_zh"
  • note frontmatter-key unknown frontmatter key "description_en"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 44/100

  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 6915 tokens
  • 100Steps. 4 steps
  • 100Consistency. Name and required fields are in place
  • 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 640: enough signal without eating the budget
  • +4Structure: 36 headings
  • +3Step-by-step instructions: 4 items
  • +4Has examples (7 code blocks)
  • +4Reference files are cited in the instructions (11 of 18)
  • +3All 7 scripts are documented

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

External checks

ClawHub: clean
This skill is a disclosed Madrid trademark practice assistant with purpose-aligned reference files and user-run calculation/update scripts, and no evidence of hidden control, exfiltration, or persistence.
LLM: benign (high) · VirusTotal: · 11 Oct 2026