BC tvs-deep-interview
深度需求访谈 Skill。通过苏格拉底式单问题追问、拓扑确认、歧义评分和显式审批闸门，把模糊想法整理成可验证规格。适用于用户说深度访谈、先问我、帮我梳理、不要假设、避免做错方向，或需求复杂到直接实现容易返工的场景。必须优先使用当前 Agent 工具自己的提问、选择、确认能力。
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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-whendescription does not say WHEN to use the skill (no "use when")
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. 1 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 105 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1648 tokens
- low 12 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
- +1No license
- +2Single-language instructions
- +3Description length 138: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 105 items
- +4Has examples (14 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.
External checks
ClawHub: clean
The skill appears to be a conversational clarification/interview helper, with no artifact-backed evidence of hidden execution, data theft, persistence, or destructive behavior.
LLM: benign (medium) · VirusTotal: · 29 May 2026