Meeting To Qualify
Audits a qualification call transcript to assess whether a deal is real — authority, budget plausibility, urgency, and fit — or sweeps a CRM export for deals never properly qualified. Use when deciding whether to invest further in a deal, reviewing a qualification call, or checking pipeline for unqualified deals inflating forecast.
Install
$ agentstack add skill-zime-ai-zime-gtm-skills-meeting-to-qualify ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-zime-ai-zime-gtm-skills-meeting-to-qualify) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
GTM Meeting-to-Qualify Audit
Audits a qualification-stage call against eight dimensions that determine whether a deal is real and worth pursuing further — not a deep discovery pass, a go/no-go read. Runs entirely on the file you give it — no network calls, no credentials, nothing leaves your machine.
When to use this
- A rep just had a first or second call and needs a structured read on
whether to keep investing time in this deal.
- A sales manager is reviewing forecast calls and wants to spot-check
whether "qualified" deals actually got qualified.
- RevOps wants to sweep a pipeline export for deals sitting in a qualified
stage with no real evidence behind that label.
Modes
Dispatch on the input file's extension.
Transcript mode (.txt, .vtt, .json, .md)
claude "run meeting-to-qualify on ./calls/acme-qualify.txt"Score the call against each dimension in references/rubric.md. For every dimension, output:
- Status — Covered / Partial / Missed
- Evidence — a direct quote or timestamp. If you can't point to a
specific line, mark Unclear rather than guess.
- Note — one line, only if Partial or Missed
Close with a single go / no-go / needs one more call read, with the one or two dimensions that decided it.
Before finalizing, run the reads-well-too check in references/rubric.md — if a call that was clearly a strong, real deal still comes back mostly Missed, the rubric is reading too strictly.
CSV mode (.csv)
claude "run meeting-to-qualify on ./exports/pipeline.csv"Structural hygiene sweep, not a call-quality audit. For deals marked "qualified" or past a qualification stage, check whether fields corresponding to authority, budget, timeline, and urgency are populated and non-trivial. Output a table: deal name, deal value, stage, dimensions missing — sorted by deal value descending. State explicitly that this checks whether fields were filled in, not whether the call itself was good.
Sample data
assets/sample-transcript.txt — a short synthetic qualification call. Run against it first before pointing this at anything of your own.
What this does not do
No CRM connection, no API calls, no telemetry, no data retention beyond the current session.
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.
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Versions
- v0.1.0 Imported from the upstream source.