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BC professional-patent-agents

📜 专利专业代理 - Patent Professional Agents 一个专业的多代理专利撰写与优化技能套件，覆盖专利申请全流程。 🎯 核心功能： • 场景一：用户想法 → 技术挖掘 → 检索分析 → 专利撰写 → 质量审核 • 场景二：用户初稿 → 问题分析 → 优化建议 → 强化权利要求 • 场景三：代理机构反馈 → 风险评估 → 优化/取消建议 🤖 9个专业代理： tech-miner (技术挖掘)、prior-art-researcher (现有技术检索)、inventiveness-evaluator (创造性评估)、patent-drafter (专利撰写)、claims-architect (权利要求架构)、patent-analyst (专利分析)、patent-auditor (专利审核)、patent-value-appraiser (价值评估)、patent-converter (文档转换) ✨ 特色： • 中英文双语支持 • 7章节标准专利模板 • 授权率预判 • 自动Word文档转换 • 持续学习能力 🔍 搜索关键词：专利, patent, 专利撰写, 专利优化, 现有技术检索, 知识产权, IP, 权利要求, 技术交底书, 创造性评估, 授权率, patent drafting, prior art search, claims, patent prosecution

ClawHub Agent Skills author: 皮皮华 v1.0.2 MIT-0 14 files body ≈ 5 128 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 50/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

AnalyzerWordAI and agentsInfrastructuretype 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
81/100
safety, quality, tests
Safety 60%
93
Quality 40%
62
Run on models
none yet
Process rating
C
50/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
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.

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.
  2. 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 · 3

✓ No critical or high findings

Medium and low: 3
  • medium Broad scope meta-agent-memory-dump agents/claims-architect/SOUL.md
    Agent memory / workspace files bundled with the skill (9) — likely a workspace dump with personal data or tokens
    agents/claims-architect/SOUL.md, agents/inventiveness-evaluator/SOUL.md, agents/patent-analyst/SOUL.md, agents/patent-auditor/SOUL.md, agents/patent-converter/SOUL.md
  • low Dangerous commands cmd-privilege agents/patent-converter/convert_patents.py:123
    Privilege escalation / world-writable permissions (detector / deny-list definition; string literal in code, not executed)
    "args": ["--no-sandbox", "--disable-setuid-sandbox"]
    detectorcode literal
  • low Dangerous commands cmd-pipe-to-shell-known-host SKILL.md:679
    Pipe-to-shell installer from a well-known host (still executes remote code) (documentation of a security skill)
    curl -fsSL https://deb.nodesource.com/setup_18.x | bash -
    security skill

Files scanned: 13. 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 ≈ 5128 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "dependencies"

Process rating: all ten parameters 50/100

  • 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. 3 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 5128 tokens
  • 100Steps. 31 steps
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • 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)
  • -246 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 619: enough signal without eating the budget
  • +4Structure: 44 headings
  • +3Step-by-step instructions: 31 items
  • +3Output format is stated explicitly
  • +4Has examples (25 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
This patent-assistant skill is mostly coherent, but it asks for broad learning/persistence over patent work, local work-record mining, and runtime skill installation without enough scoping or consent controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026