Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/zime-ai/zime-gtm-skillsnpx agentmods add skills/zime-ai/zime-gtm-skills/bantWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zime-ai/zime-gtm-skills/bant)<a href="https://agentmods.dev/skills/zime-ai/zime-gtm-skills/bant"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/bant/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zime-ai/zime-gtm-skills/bant"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/bant.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00069 | $0.01152 |
| Opus 5 | $0.00034 | $0.00576 |
| Sonnet 5 | $0.00014 | $0.00230 |
| Haiku 4.5 | $0.00007 | $0.00115 |
Grade A, and why
bant scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BANT Qualification Audit
You are a sales-call qualification auditor. Your goal is to give a rep or RevOps a fast, evidence-backed advance/no-advance read on a first call.
Audits a sales conversation against the four BANT criteria. Lighter-weight
than meddicc — BANT is built for a fast early-stage "should this deal
advance" read, not a full late-stage qualification pass. Use meddicc
instead once a deal is past first qualification and heading toward a
technical or economic evaluation.
When to use this
- A rep just finished a first call and wants a quick advance/no-advance read.
- An SDR handed off a lead and the AE wants a structured gut-check before investing more time.
- RevOps wants to sweep a pipeline export for deals sitting in "Qualified" that never actually had BANT covered.
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 file, which call, or whether a borderline quote counts — decide from the transcript, note the call once in the output, and move on.
- If the transcript is a demo, renewal, or support call rather than a first qualification call, say so in one line and still score whichever BANT criteria the conversation happens to touch.
- If the file is truncated or a section is inaudible/unclear, score what's there and mark the affected criteria Unclear rather than guessing at what was probably said.
Modes
Transcript mode (.txt, .vtt, .json, .md)
claude "run bant on ./calls/first-call.txt"
- Read the whole transcript before scoring anything — a Budget number or a Timeline trigger often surfaces late, after the criterion looks Missed from the first half alone.
- Score each of the four criteria in
references/rubric.mdindependently. Don't let a strong Need inflate a weak Budget, or vice versa. - Run the rubric's reads-well-too check before finalizing.
- Write the output in the exact shape under
## Output format.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 123 lines · 69 tokens per session scan A 16a7accfe9fc
bant is a skill published in the GitHub repository zime-ai/zime-gtm-skills (14 stars, last pushed 17d ago), licensed MIT. It adds 69 tokens to every session and 1,152 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
go-to-market-playbook
A reusable Go-to-Market strategy template for both B2B and B2C launches. Covers positioning, messaging, ICP definition, channel selection, and competitive analysis frameworks. By @WeiYipei.
category-point-of-view
Create a differentiated B2B category point of view that leads with the customer problem, defines the market shift, names the category or strategic frame, and turns it into content, distribution, and measurement guidance.
b2b-pmm-orchestrator
Route vague B2B product marketing requests to the right PMM skill, sequence multiple skills into intelligent GTM workflows, and keep the agent focused on the smallest useful artifact that moves the business forward.
ai-pmm-reviewer
Review AI-generated B2B marketing and PMM drafts for strategic sharpness, customer truth, positioning quality, plain English, and AI tells; diagnose gaps and rewrite only where judgment is clear.
customer-story-engine
Capture true customer stories and turn them into plain-spoken B2B story assets: story briefs, case studies, one-page PDFs, website posts, and sales proof.
demo-storyline
Create a buyer-centered B2B SaaS demo storyline that maps product moments to buyer pain, uses realistic data, prompts discovery throughout, and ends with a clear recap and next step.