AC novelty-validator
Use when the user is in or just finished a meeting/brainstorm and wants to check whether a discussed idea, method, or research direction is novel — e.g. says '这个想法有人做过吗'、'验证下创新性'、'查重这个思路'、'novelty check'、'别人做过没'. Triggers on a Tencent Meeting transcript/智能纪要 or a user-stated idea paired with doubt about originality. Especially during/after academic group meetings, seminars, or cross-lab collaboration sessions.
g. says '这个想法有人做过吗'、'验证下创新性'、'查重这个思路'、'novelty check'、'别人做过没'. Triggers on a Tencent Meeting transcript/智能纪要 or a user-stated idea paired with doubt about…
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 59/100
- 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
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 48 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2358 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
- +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 413: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 48 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.