Deep discovery
Audits an early-stage B2B sales discovery call transcript against a discovery-quality rubric, or sweeps a CRM deal export for missing discovery fields. Use when reviewing a discovery/early-stage sales call, coaching a rep on discovery quality, or checking a pipeline export for deals with thin discovery.From its SKILL.md
npx -y skills add zime-ai/zime-gtm-skills --skill deep-discoveryAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 14 stars14 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its file declares
Copied from the file, not written here
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
3.2 KB, as published. Nobody here has run it
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.