Persona Based Discovery

GTM Persona-Based Discovery Audit

Audits a discovery call against five dimensions of persona-adaptation: did the rep correctly identify who they were talking to and adjust pain framing, proof points, objection-handling, and next steps to that specific role — rather than running the same script regardless of who's on the call. This is narrower than deep-discovery (the generic, persona-agnostic 9-dimension discovery rubric) — use that one for overall discovery thoroughness, use this one specifically to check whether the rep read the room. Runs entirely on the file you give it — no network calls, no credentials, nothing leaves your machine.

When to use this

• A rep had a discovery call with a named persona (a technical evaluator, an

economic buyer, an end user) and a manager wants to know if the call was actually adapted to that person, or just the standard pitch.

• A call had multiple stakeholders on it and you want to check whether the

rep addressed each persona's concerns distinctly, or treated the room as one audience.

• RevOps wants to sweep a pipeline export for deals whose contacts have no

role/title data, which usually means discovery never identified who's actually in the deal.

Modes

Dispatch on the input file's extension.

Transcript mode (.txt, .vtt, .json, .md)

claude "run persona-based-discovery on ./calls/acme-discovery.txt"

First identify who was on the call and what persona each speaker maps to (technical evaluator, economic buyer, end user, champion, etc.) — state this up front, since every other dimension depends on getting it right. Then score the call against each dimension in references/rubric.md. For every dimension, output:

• Status — Covered / Partial / Missed
• Evidence — a direct quote or timestamp tying the finding to a specific

persona on the call. If you cannot point to a specific line, mark the dimension Unclear rather than guessing.

• Note — one line, only if the status is Partial or Missed

If the call only had one persona in the room, say so and score the dimensions against that one persona rather than penalizing the call for not covering personas that were never present.

Close with 2-3 highest-leverage next steps for adapting future calls with this persona mix.

CSV mode (.csv)

claude "run persona-based-discovery on ./exports/pipeline.csv"

This is a structural hygiene sweep, not a call-quality audit — a CRM export can only show whether contact role/title data was ever captured, not whether a rep actually adapted to it on a call. Say this explicitly in the output.

For each deal row, check whether contact-role fields (title, persona tag, buyer type) are present and non-trivial for every listed contact. Output a table: deal name, deal value, contacts missing role data, sorted by deal value descending so the highest-value gaps surface first.

Sample data

assets/sample-transcript.txt is a short synthetic discovery call with a technical evaluator and an economic buyer both on the line — run the skill against it first. assets/sample-pipeline.csv is a synthetic pipeline export (deliberately missing contact-role data) for trying CSV mode.

What this does not do

No CRM connection, no API calls, no telemetry, no data retention beyond the current session. It reads the file you point it at and nothing else.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: zime-ai
• Source: zime-ai/zime-gtm-skills
• License: MIT
• Homepage: https://zime.ai

Install and usage instructions live in the source repository linked above.