SKILLEMALL.ai

BC rd-ip-accelerator

研发IP全栈加速器——面向全行业研发创新型企业,输入企业名称自动驱动六大IP模块(全部内置,用户只需安装本技能): ①诉讼情报预警、②友商情报监控(完整内置tech-intel-monitor逻辑·Eureka Monitor风格)、③FTO产品防侵权、 ④技术方案探索(5改进+4创新+6白点+工程可靠性)、⑤查新检索(8步严谨分析·PatentBench认证X检出率81%)、 ⑥技术交底书(九章标准格式+3实施例+Word自动下载),形成从IP意识唤醒到高质量专利申请的完整闭环。 无需安装其他技能,一包搞定全部功能。

ClawHub Agent Skills author: yuanzhian-patsnap v1.0.0 MIT-0 2 files body ≈ 1 892 tokens Open the sourceclawhub.ai analyzed 3 d ago

研发IP全栈加速器——面向全行业研发创新型企业,输入企业名称自动驱动六大IP模块(全部内置,用户只需安装本技能): ①诉讼情报预警、②友商情报监控(完整内置tech-intel-monitor逻辑·Eureka Monitor风格)、③FTO产品防侵权、…

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

IntegrationAI and agentsData and analyticsLegaltype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
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

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 "copyright"

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. 2 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 75 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1892 tokens
  • low 15 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
  • -232 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 262: enough signal without eating the budget
  • +4Structure: 51 headings
  • +3Step-by-step instructions: 75 items
  • +4Has examples (13 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
The skill is a coherent IP-research assistant, but it can launch broad external searches and create multiple reports from very broad prompts without enough user confirmation.
LLM: suspicious (high) · 13 Aug 2026