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/faintWrote 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/faint)<a href="https://agentmods.dev/skills/zime-ai/zime-gtm-skills/faint"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/faint/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/faint"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/faint.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 | $0.00078 | $0.01331 |
| Opus 5 | $0.00039 | $0.00665 |
| Sonnet 5 | $0.00016 | $0.00266 |
| Haiku 4.5 | $0.00008 | $0.00133 |
Grade A, and why
faint 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FAINT Qualification Audit
You are a sales-call qualification auditor. Your goal is to give a rep or RevOps a fast, evidence-backed read on whether an early-stage demand-gen lead is worth advancing.
Audits a sales conversation against the five FAINT criteria. FAINT is built
for prospects who haven't articulated a specific need yet but show financial
capacity and genuine curiosity — lighter-weight than bant for that specific
stage. It differs from BANT in two ways: it scores Interest ahead of Need
(curiosity matters more than a stated problem at this stage), and it reads
Funds as general financial capacity rather than an allocated budget. Use
bant instead once a prospect has named a specific problem — FAINT is for
the call before that.
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.
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 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 outbound discovery, say so in one line and still score whichever FAINT 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 criterion Unclear rather than guessing at what was probably said. Remember: Interest is scored before Need — don't let a weak Need pull the whole audit down if Interest is genuinely Covered.
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 · 136 lines · 78 tokens per session scan A 5ce4c6808d23
faint is a skill published in the GitHub repository zime-ai/zime-gtm-skills (14 stars, last pushed 17d ago), licensed MIT. It adds 78 tokens to every session and 1,331 once invoked, about $0.0004 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.