SKILLEMALL.ai

AC pm-interview-grill

产品经理(PM)高强度面试拷问与即时教练技能。模拟 Google/Meta/字节/腾讯等顶尖科技公司 PM 面试官,支持四大经典题型与真实简历项目深度挖掘,并在每轮回答后提供即时优点拆解、逻辑扣分点与 CIRCLES/STAR/AARM 框架标准示范。当用户提出“PM面试”、“产品经理面试”、“PM烤问”、“产品经理模拟面试”、“PM烤问我”或需要 PM 面试指导时触发。

ClawHub Agent Skills author: 豌豆 v1.0.0 MIT-0 8 files body ≈ 821 tokens Open the sourceclawhub.ai analyzed 35 h ago

产品经理(PM)高强度面试拷问与即时教练技能。模拟 Google/Meta/字节/腾讯等顶尖科技公司 PM 面试官,支持四大经典题型与真实简历项目深度挖掘,并在每轮回答后提供即时优点拆解、逻辑扣分点与 CIRCLES/STAR/AARM…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. 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: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 9 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 821 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 187: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 9 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

External checks

ClawHub: clean
This is a PM interview coaching skill made of documentation and prompt guidance, with no executable code or hidden data access.
LLM: benign (high) · VirusTotal: · 8 Aug 2026