product-os: Command for Claude Code

.claude/commands/interview-feedback.md

interview-feedback is a command for Claude Code from motorway-sandbox/product-os. It costs 0 tokens per session (2,239 once invoked), scanned A, original, MIT.

An interview feedback writer that creates an objective scorecard for a product manager candidate from a meeting transcript and job description. A scorecard is a structured assessment against defined hiring criteria.

In plain words
What is it for?
Gather an interview transcript, compare it with the role's competencies and description, and produce structured hiring feedback.
Why use it?
It removes the need to assemble interview evidence and role requirements manually. It also handles finding the relevant meeting and job description from the named sources.

Command for Claude Code

Written for Claude Code: $ARGUMENTS substitution.

This is motorway-sandbox/product-os's own configuration. It tells Claude Code how to work on product-os itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything product-os configures →

Reuse

Borrowing it

Nothing to install: this file belongs to motorway-sandbox/product-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/motorway-sandbox/product-os/main/.claude/commands/interview-feedback.md
Clone the repo
git clone --depth 1 https://github.com/motorway-sandbox/product-os

Made for: Claude Code.

Wrote this? Show the measurements

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agentmods badge for interview-feedback

README.md
[![agentmods](https://agentmods.dev/badge/commands/motorway-sandbox/product-os/interview-feedback.svg)](https://agentmods.dev/commands/motorway-sandbox/product-os/interview-feedback)
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<a href="https://agentmods.dev/commands/motorway-sandbox/product-os/interview-feedback"><img src="https://agentmods.dev/badge/commands/motorway-sandbox/product-os/interview-feedback.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,239 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.02239
Opus 5 $0.00000 $0.01120
Sonnet 5 $0.00000 $0.00448
Haiku 4.5 $0.00000 $0.00224

Measured 3d ago against content hash aa42fd787af5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

interview-feedback 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 3d 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.

.claude/commands/interview-feedback.md · 188 lines

How it starts

The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Interview Feedback Scorecard

Generate a rigorous, objective interview feedback scorecard for a PM candidate based on the Granola meeting transcript, assessed against our PM hiring competencies and the role's job description.

Instructions

Phase 1: Gather Context

  1. Parse the arguments

    • The user provides arguments in the format: $ARGUMENTS
    • Expected format: [Candidate Name], [Target Level], [Interview Type]
    • Example: Jane Smith, Senior PM, product case study
    • If any of the three required fields are missing or unclear, ask ONLY for the missing information. Do not re-ask for fields already provided.
    • Check the auto-memory (MEMORY.md) for the interviewer's name — use this in the scorecard header
  2. Find the interview in Granola

    • Use mcp__claude_ai_Granola__list_meetings (with a custom date range if the interview date is known, or last_30_days) to find candidate meetings, then filter by the candidate's name in the title or participants
    • If multiple meetings are found, ask the user to confirm which one
    • Use mcp__claude_ai_Granola__get_meeting_transcript to retrieve the full transcript
  3. Fetch the job description from Notion

    • The job library is at: {your-notion-scorecard-url}
    • Use mcp__claude_ai_Notion__notion-fetch to retrieve the job library page
    • Find the job description matching the target role and level, then fetch that specific JD page
    • If the JD is not found in the library, ask the user to provide a Notion link or paste it in. Note this limitation in the output.
  4. Read the PM hiring competencies

    • Read the file at team/hiring/best-practice-pm-hiring.md
    • This contains the full competency framework with excellent/poor indicators for all skills and attitudes

Phase 2: Analyse the Transcript

  1. Map competencies to interview type
    • Not all competencies are equally assessable in every interview type. Based on the interview type, identify which competencies are:
      • Primary (should be thoroughly assessed in this interview type)
      • Secondary (some signal may be present)
      • Not assessable (no meaningful signal expected)
    • Use this mapping as a guide:
      • Screening call: Communication (primary), Delivering Impact (primary), Curiosity (primary), Growth Mindset (secondary), Hunger for Impact (secondary), Product Process (secondary)
      • Product case study: Product Process - all sub-competencies (primary), Delivering Impact (primary), Communication (primary), Experiment Design (primary), Measurement (primary)
      • Stakeholder interview: Collaboration (primary), Communication (primary), Project Management (primary), Ownership (primary), Growth Mindset (secondary)
      • Technical deep-dive: Product Process - Build (primary), Experiment Design (primary), Measurement (primary), Project Management (primary), Solutionisation (primary)
      • Culture/values interview: All Attitude competencies (primary), Communication (secondary), Collaboration (secondary)
    • If the interview type does not match the above, use judgement to assign primary/secondary/not assessable

Read the full file on GitHub · 188 lines

Changes

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.

  1. 3d ago First seen · 188 lines · 0 tokens per session scan A aa42fd787af5

Subscribe to this mod's changes

interview-feedback is a command published in the GitHub repository motorway-sandbox/product-os (9 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,239 tokens. 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 2026-09-04.