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/meddiccWrote 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/meddicc)<a href="https://agentmods.dev/skills/zime-ai/zime-gtm-skills/meddicc"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/meddicc/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/meddicc"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/meddicc.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 | $0.00083 | $0.01340 |
| Opus 5 | $0.00042 | $0.00670 |
| Sonnet 5 | $0.00017 | $0.00268 |
| Haiku 4.5 | $0.00008 | $0.00134 |
Grade A, and why
meddicc 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.
MEDDICC Qualification Audit
You are a sales-call qualification auditor. Your goal is to give a rep or manager a structured, evidence-backed read on qualification depth across the seven MEDDICC dimensions.
Audits a sales conversation against the seven MEDDICC letters — the fuller qualification pass for deals past initial discovery. Unlike BANT's speed-focused four criteria, MEDDICC is built for multiple stakeholders, complex approval chains, and later-stage deal evaluation. MEDDICC isn't tied to one point in the deal — run it against any call (discovery, technical, negotiation) and it scores whatever letters that specific call could plausibly have surfaced.
When to use this
- Prepping for a forecast or deal-review call and want a structured read on qualification gaps before a manager asks.
- A rep wants to know which MEDDICC letters this call actually covered vs. which are still open.
- RevOps wants to sweep a pipeline export for deals missing MEDDICC fields before a QBR.
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 letters count, or whether an indirect reference to a decision-maker "counts" — apply the rubric as written, decide, and note the assumption once.
- If the call is earlier-stage than typical for a full MEDDICC pass (e.g. a first discovery call), say so in one line and still score whichever letters the conversation happens to touch. Mark letters genuinely unreachable at this stage as Not applicable rather than Missed — a MEDDICC audit that penalizes every early call for lacking late-stage letter coverage is not useful.
- If the file is truncated or a section is inaudible/unclear, score what's there and mark the affected letters Unclear rather than guessing at what was probably said.
Modes
Transcript mode (.txt, .vtt, .json, .md)
claude "run meddicc on ./calls/acme-call.txt"
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 · 83 tokens per session scan A a97536027252
meddicc is a skill published in the GitHub repository zime-ai/zime-gtm-skills (14 stars, last pushed 17d ago), licensed MIT. It adds 83 tokens to every session and 1,340 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.
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