AC citation-competitor-intelligence
Discover hidden competitors through academic citation network analysis. Forward: given a paper/professor, trace citation threads (backward to prior art, forward to derivative work) to find researchers who may have commercialized similar technology. Reverse: given a company, map its academic footprint and trace the citation graph to uncover unlisted competitors. 通过学术论文引用网络发现隐性竞品。 正向：给定论文/教授，追踪引用线索（反向至已有成果、正向至衍生研究）找到可能已产业化的同类研究者。 反向：给定公司，绘制其学术足迹并从引用网络挖掘未被媒体覆盖的竞品。Use when researching deep-tech startups, university spin-offs, professor-founded companies, or technology commercialization in hardware/pharma/materials/optics. Triggers: 教授创业、 成果转化、学术竞品、论文引用竞品分析、citation competitor、university spin-off competitor、academic competitor discovery、论文→竞品、引用网络分析
Discover hidden competitors through academic citation network analysis.
As a process C 60/100 · Has gaps — weak spots: result and completion, failures and branches, progress reporting
How to improve
- 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: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "agent_created" - note
frontmatter-keyunknown frontmatter key "disable"
Process rating: all ten parameters 60/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 31 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1642 tokens
- 100Running it twice. Mutating operations check current state
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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 754: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 31 items
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.