Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add konglong87/superPM --skill pm-interview-prepgit clone --depth 1 https://github.com/konglong87/superPMWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/konglong87/superpm/pm-interview-prep)<a href="https://agentmods.dev/skills/konglong87/superpm/pm-interview-prep"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-interview-prep/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/konglong87/superpm/pm-interview-prep"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-interview-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.00513 |
| Opus 5.5 | $0.00016 | $0.00205 |
| Sonnet 5.5 | $0.00008 | $0.00103 |
| Haiku 4.5 | $0.00004 | $0.00051 |
Grade A, and why
pm-interview-prep scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Preamble (run first)
无需启动时命令。
写入时边界
只讨论时不写文件，启动检查不创建目录。用户要求保存产物时，先确认当前产品、精确输出路径及已有文件的覆盖/追加选择；确认后仅在实际 Write 前创建该输出文件的父目录。完整安装可参见 skills/_shared/artifact-policy.md，单独安装本 skill 也按本段执行。
Overview
Covers the 4 main PM interview categories:
- Product Sense — Design a product, improve a feature, identify opportunities
- Execution — Prioritization, trade-offs, stakeholder management
- Behavioral — Leadership, conflict, failure, influence
- Strategy — Market sizing, growth strategy, competitive analysis
Execution Flow
Step 1: Identify Target
Use AskUserQuestion:
What type of PM role are you interviewing for?
A) Generalist PM B) Technical PM C) Growth PM D) AI/ML PM E) Senior/Director PM F) Other (please describe)
Step 2: Choose Focus Area
Use AskUserQuestion:
Which area would you like to practice first?
A) Product Sense — Design a product or feature B) Execution — Prioritization and trade-offs C) Behavioral — Leadership and conflict stories D) Strategy — Market sizing and growth E) Full mock interview — All areas combined
Step 3: Practice & Feedback
For each practice area, present a realistic question, evaluate the user's answer, and provide structured feedback using the STAR framework and scoring rubric.
Step 4: Track Progress
Optionally save interview prep notes to docs/06-职业发展/面试准备笔记.md.
Step 5: Recommended Next Steps
- /pm-career-coach — Career planning
- /pm-resume — Resume optimization
- Practice another interview category
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +4 lines 53d97fb7d256
- 1mo ago First seen · 72 lines · 40 tokens per session scan A af8d0c8fb3aa
pm-interview-prep is a skill published in the GitHub repository konglong87/superPM (83 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 513 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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