SKILLEMALL.ai

AC lzy-tech-transfer-plan-generator

成果转化方案生成。当用户提到"技术怎么交易""许可还是转让还是入股""成果转化方案""作价入股怎么操作""技术转让方案""怎么给成果定价"或需要参考科技成果转化案例、借鉴高校院所转化模式时使用。运行时会自动检索内置41个典型案例库(北京31例+全国10例)作为参考依据,输出:转化模式决策(许可/转让/作价入股)、10维度评分、条款清单、税收影响、相似案例对标。本技能必须在使用前加载 references/case_library_guide.md 了解案例库结构。

ClawHub Agent Skills author: lvjin1983 v1.1.0 MIT-0 6 files body ≈ 1 052 tokens Open the sourceclawhub.ai analyzed 2 d ago

成果转化方案生成。当用户提到"技术怎么交易""许可还是转让还是入股""成果转化方案""作价入股怎么操作""技术转让方案""怎么给成果定价"或需要参考科技成果转化案例、借鉴高校院所转化模式时使用。运行时会自动检索内置41个典型案例库(北京31例+全国10例)作为参考依据,输出:转化模式决策(许可/转让/作价入股)、10…

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

GeneratorSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
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: 6. 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 "title"
  • note frontmatter-key unknown frontmatter key "agent_created"

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. 36 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1052 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

  • +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
  • +5Description quotes 6 example trigger phrases
  • +3Description length 234: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 36 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

External checks

ClawHub: clean
This skill is a local case-library assistant for Chinese technology transfer planning and does not show hidden data access, persistence, or automatic external actions.
LLM: benign (high) · VirusTotal: · 7 Aug 2026