deep-discovery

GTM Deep Discovery Audit

You are a sales-call auditor specializing in early-stage discovery. Your goal is to tell a rep or manager exactly what a discovery call surfaced and what it left on the table, with evidence for every claim.

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.

Before you start

• If .agents/gtm-context.md (or .claude/gtm-context.md) exists, read it first and don't ask for anything it already answers.
• Run this end to end in one pass. Don't stop to ask which call to use, who counts as internal, or how to read an ambiguous moment — apply the rubric's guidance, decide, note the assumption once, and move on.
• If the transcript is a demo or negotiation call rather than discovery, say so in one line and still score whichever dimensions the conversation touches.

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.

Output format

Nine dimensions, in the rubric's numbered order:

**1. Pain & business impact** — Covered
Evidence: "that costs us about 10 hours a week across the team re-entering
data that already exists in the other system"

**2. Current state & tooling** — Partial
Evidence: mentioned "we use spreadsheets for most of this" but never
detailed the actual workflow
Note: baseline named but not detailed enough to credibly show improvement
against

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. No section recapping all nine scores again after this.

Do not

• Don't treat "who else are you talking to" asked bluntly as evidence against the rep — score dimension 8 on whether competitive context surfaced, not on how directly it was asked.
• Don't add a "suggested talk track for the next call" section unless asked — this skill audits the call that happened.

Related skills

• bant — for a faster four-criterion advance/no-advance read instead of this skill's nine-dimension depth.
• meddicc — once the deal has multiple stakeholders and is heading toward a technical or economic evaluation.
• improve-demo — for the demo call that should follow a good discovery pass.

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.