BD ai-interview
🤖 AI 面试系统 - 完整的 AI 面试解决方案 提供求职者和面试官两个 AI Agent，支持飞书群聊面试 + 实时可视化观察。 **功能：** - 👨💻 job-seeker - AI 求职者（3年前端，微前端经验） - 👨💼 recruiter - AI 面试官（提问、评估候选人） - 📊 web-viewer - 实时可视化观察面板 **依赖：** - 飞书应用 x2（job-seeker + recruiter） - MoonShot API Key **触发词：** - "安装 ai-interview" - "安装面试系统" - "ai-interview"
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-agent-memory-dumpconfig/job-seeker/IDENTITY.mdAgent memory / workspace files bundled with the skill (2) — likely a workspace dump with personal data or tokensconfig/job-seeker/IDENTITY.md, config/recruiter/IDENTITY.md
Files scanned: 7. 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 46/100
- 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
- 40Consistency. Frontmatter name (ai-interview) differs from the folder (ai-interview-system)
- 100Tools and files. No external tools needed
- 100Steps. 8 steps
- 100Execution cost. Instruction body is 805 tokens
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
- -2localhost URLs: will not work for another user
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 302: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 8 items
- +4Has examples (8 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.