AD design-research-scout
Turn fuzzy briefs into a stable pre-design workflow for requirement validation, brief clarification, early-stage research, inspiration direction setting, and approved-site inspiration collection. Use when the team needs 前期需求分析, brief 拆解, 调研框架, 方向梳理, 竞品或跨行业参考, 风格探索, moodboard, 参考图, 灵感图, or wants a workflow that first checks whether the requirement is clear enough, asks a few high-impact questions if needed, and then returns both research conclusions and inspiration references or links from Behance, Zcool, Huaban, and Xiaohongshu.
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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: 13. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 49/100
- 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
- 40Consistency. Frontmatter name (design-research-scout) differs from the folder (design-researh)
- 100Tools and files. No external tools needed
- 100Steps. 107 steps
- 100Execution cost. Instruction body is 726 tokens
- 100Running it twice. No mutating operations
- low 11 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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 534: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 107 items
- +4Reference files are cited in the instructions (8 of 8)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.