Structured Product Interview Prep with experience-deepthink

Where this fits

A product manager candidate has a real project story — an internship, a hackathon, an AI coding side project — but can't yet articulate it in a way that survives interview scrutiny. This skill accepts the user's natural, messy narration and drives a staged, one-question-at-a-time dialogue to reconstruct the actual context, goals, actions, results, and learnings, producing a fixed eight-chapter detail document and optionally a seven-question interview transcript.

Why agents benefit

• Enforces a two-phase pipeline: reconstruct facts first, generate interview transcripts only from confirmed facts — no hallucinated project details.
• Refuses to package non-product work as product experience, and never backfills facts from job common sense or sample answers.
• Maintains a clear epistemic model: distinguishes time-of-event facts, then-current rationale, current retrospective, redo scenarios, future plans, and unknown info.
• Reads specialized references only when the experience involves AI, Agent, RAG, or AI Coding, keeping the prompt minimal.
• Produces deterministic, fixed-schema outputs that are easy to review, edit, and hand back to the user.

Practical scenarios

Internship interview preparation

A user leaves a rambling voice note about a product intern stint at a startup. The agent runs the staged Q&A, produces the detail reconstruction, then generates a mock interview transcript the user can rehearse from.

Reframing a competition project

A user participated in a business or hackathon competition but never framed it as product work. The skill surfaces the real product decisions and trade-offs, without inflating the role.

AI Coding project deep-dive

A user built a small AI tool with RAG or AI Coding workflows. The skill routes to the specialized reference and extracts the product judgment behind technical choices.

Add it to your agent workflow

Install with npx:

npx skills add riwonswain-ovo/OfferLoop --skill experience-deepthink

Invoke it by asking the user to narrate a real experience naturally, e.g., "Tell me about a product project you worked on, start from the beginning." The agent will switch into one-question-at-a-time mode, produce the detail reconstruction, then offer the interview transcript phase.

Compare before adopting

Compare against: output quality (does the eight-chapter schema actually capture the user's real facts?), maintenance freshness (is the repo active and responsive to issues?), alternatives (generic interview-prep skills that don't enforce fact boundaries), and workflow fit (does your agent already have a structured conversation flow?).

Why it is worth tracking

The skill sits at 13 stars and is early-stage, but the design philosophy — strict fact reconstruction before any generation — is a strong quality signal. Watch for updates to the schema references and the runtime mode check. Evaluate it when you need a disciplined, non-hallucinating interview-prep skill for product roles. For more context, see the skill page: https://www.openagentskill.com/skills/riwonswain-ovo-experience-deepthink