Deep Discovery

GTM Deep Discovery Audit

Audits early-stage discovery calls against nine dimensions of what a thorough discovery conversation covers. Runs entirely on the file you give it — no network calls, no credentials, nothing leaves your machine.

When to use this

• A rep just finished a discovery call and wants a structured read on what

they covered and missed.

• A manager is reviewing a call before a coaching session.
• RevOps wants to sweep a pipeline export for deals that never got a proper

discovery pass.

Modes

Dispatch on the input file's extension.

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

claude "run deep-discovery on ./calls/acme-discovery.txt"

Read the transcript, 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 from the transcript. If you

cannot point to a specific line that justifies the status, mark the dimension Unclear instead of guessing — an uncited finding is worse than no finding, because it's untrustworthy the first time it's wrong.

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

Close with 2-3 highest-leverage next steps — not a summary of every gap, the ones that would have moved this specific deal forward most.

Run references/rubric.md's "reads well too" check before finalizing: if a transcript that clearly covered discovery thoroughly still comes back with several Missed dimensions, the read is biased toward finding fault — widen what counts as evidence before reporting.

CSV mode (.csv)

claude "run deep-discovery on ./exports/pipeline.csv"

This is a structural hygiene sweep, not a call-quality audit — CRM fields can't show whether a rep actually probed pain on a call, only whether someone typed something into a field. Say this explicitly in the output.

For each deal row, check whether fields corresponding to the rubric's dimensions (pain/impact notes, decision process, budget, timeline, competition) are present and non-trivial (not a single word, not a placeholder). Output a table: deal name, deal value, dimensions missing, sorted by deal value descending so the highest-value gaps surface first.

Sample data

assets/sample-transcript.txt is a short synthetic discovery call — run the skill against it first to see real output before pointing it at anything of your own. assets/sample-pipeline.csv is a synthetic pipeline export (deliberately missing fields) 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.