Faint

FAINT Qualification Audit

Audits a sales conversation against the five FAINT criteria. FAINT departs from bant in one deliberate way: it scores Interest ahead of Need, and reads Funds as general financial capacity rather than an allocated budget. It's built for prospects who haven't stated a need yet, or don't have budget set aside, but who show real financial capacity and can be made curious. Use bant instead once a prospect has already named a specific problem — FAINT is for the call before that, where the rep's job is to generate interest, not confirm an existing one.

When to use this

• A lead came in through outbound or a campaign with no stated problem, and

the question is whether they're worth continuing to work rather than whether they're already sold.

• A rep wants credit for generating genuine curiosity on a call, rather than

having that call marked as a qualification miss just because the prospect never said "we need X."

• RevOps wants to sweep a pipeline export for demand-driven leads sitting

in "Qualified" that never actually had FAINT covered.

Modes

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

claude "run faint on ./calls/acme-call.txt"

Read the transcript, then score it against each criterion in references/rubric.md. For every criterion, output:

• Status — Covered / Partial / Missed
• Evidence — a direct quote from the transcript
• Note — one line, only if Partial or Missed

Score Need last, and read it against what Interest already established on the call — see the "Interest before Need" section of the rubric before marking Need Missed on a call where genuine curiosity was clearly built.

Close with the single biggest qualification risk if this deal were forecast today, and what call or action would close it.

CSV mode (.csv)

claude "run faint on ./exports/pipeline.csv"

Structural hygiene sweep, not a call-quality claim — say so explicitly. For each deal row, check whether fields corresponding to the five FAINT criteria are present and non-trivial. Output a table: deal name, deal value, criteria missing, sorted by deal value descending.

Sample data

assets/sample-transcript.txt is a synthetic outbound discovery call — run the skill against it first.

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.