meddpicc-gap-audit

meddpicc-gap-audit is a skill for Claude Code, Codex from swan-gtm/gtm-skills. It costs 189 tokens per session (1,980 once invoked), scanned A, original, MIT.

A deal-review process that checks the MEDDPICC sales qualification framework: metrics, buyer, criteria, process, paperwork, pain, champion, and competition.

In plain words
What is it for?
It helps analyse meeting transcripts and email threads, reconcile them with optional CRM fields, and produce an evidence-backed gap report for deal reviews.
Why use it?
It shows which parts of a sales deal are supported by evidence, which are unverified, and where confirmed risks could affect the forecast or next stage.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps analyse meeting transcripts and email threads, reconcile them with optional CRM fields, and produce an evidence-backed gap report for deal reviews.

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Install with agentmods
npx agentmods add skills/swan-gtm/gtm-skills/meddpicc-gap-audit
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add swan-gtm/gtm-skills --skill meddpicc-gap-audit
Clone the repo
git clone --depth 1 https://github.com/swan-gtm/gtm-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for meddpicc-gap-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/meddpicc-gap-audit/github.svg)](https://agentmods.dev/skills/swan-gtm/gtm-skills/meddpicc-gap-audit)
Your own site
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/meddpicc-gap-audit"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/meddpicc-gap-audit/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.

agentmods 80×15 button for meddpicc-gap-audit

Your own site · 80×15
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/meddpicc-gap-audit"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/meddpicc-gap-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 189 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,980 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00189 $0.01980
Opus 5 $0.00095 $0.00990
Sonnet 5 $0.00038 $0.00396
Haiku 4.5 $0.00019 $0.00198

Measured 13d ago against content hash 21698cbc5d01, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

meddpicc-gap-audit 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 13d 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.

skills/amit-rotstein/meddpicc-gap-audit/SKILL.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Use this skill when a deal is coming up for a forecast call, a stage change, when someone asks what's actually missing on it, or after a new call or email that might change qualification. Input: one or more meeting transcripts or email threads for the deal — this skill has nothing to work from without at least one. The deal's current CRM/qualification field values are optional, supplied separately, and used only for reconciliation, not as the primary input. Produces a per-component status — verified, unverified, or confirmed risk — for every MEDDPICC/MEDDIC component, each backed by the exact evidence that produced it.

The play

  1. Start from the meeting transcript(s) or email thread(s) supplied for this deal — that's the material everything else is extracted from. If the deal's current qualification data (CRM fields, a deal doc, notes) was also supplied, note it as the baseline to reconcile against later; if it wasn't, say so explicitly rather than silently skipping reconciliation.
  2. Read the transcripts/emails and map language to each MEDDPICC component (Metrics, Economic Buyer, Decision Criteria, Decision Process, Paper Process, Identify Pain, Champion, Competition) as you go. For every mapping, quote the exact line or lines that justify it. A paraphrase is not a citation.
  3. If more than one transcript or email is supplied for the deal, order them chronologically before mapping anything. Where a component has evidence in more than one source, the most recent statement is the current status — unless it actually reverses an earlier one (a champion's enthusiasm cooling, a budget that was confirmed becoming unconfirmed). A reversal is a finding in its own right — cite both the earlier and later statements rather than quietly keeping only the newer one. Added detail that doesn't conflict (a second call naming specific frameworks after a first call named compliance generally) is enrichment, not a reversal — no flag needed.
  4. Before mapping anything from a transcript, resolve who's speaking. See references/speaker-attribution.md for the fallback chain — domain metadata, then contact-list match, then a content-cue guess that must be confirmed before use. When a CRM connection is available, its own contact and owner records for the deal are usually the strongest source for the contact-list rung — check there before falling back to a guess.
  5. For every mapping, check that the speaker actually matches the side the component expects — a seller describing their own product is not evidence of the buyer's pain, need, or criteria, even when the topic overlaps. Discard or re-flag any mapping that fails this check rather than citing it anyway.
  6. If CRM/qualification values were supplied in step 1, reconcile each mapped value against the existing value for that component: does the source material support it, contradict it, or say nothing? Where the CRM is blank and the source speaks directly to that component, that's a fillable gap with the citation already attached. Where the CRM and the source disagree, surface the disagreement explicitly — never quietly overwrite one with the other — and say what kind of disagreement it is: a hard factual conflict (a number, a name) is a different problem than a qualitative maturity mismatch (a status claim the source doesn't support). Apply the same defensibility check to the CRM's existing value, too — a vague entry doesn't get a pass just because nothing in the source material contradicts it. If no CRM values were supplied, skip this step and say so in the output rather than presenting the extraction as a reconciled audit.
  7. Grade every component on two separate axes: is it defensible (a real citation exists, not a restated field or a paraphrase), and is the actual answer favorable. A well-cited "no budget confirmed" is defensible but still a real risk — flag it as a confirmed risk, not a data gap, and never let a good citation round an unfavorable answer up to verified.
  8. Cross-check gap severity against deal stage. A missing component early on is routine. The same gap at a mature stage — verbal commit, contract-out — is urgent; surface it as a stage/qualification mismatch on its own, not folded quietly into the individual component's score.
  9. If a live CRM connection exists, the default is still to only report — a connection is not permission. Only if the person running this skill explicitly asks to update the CRM, go field by field: show the field name, its current value, the proposed new value, and the citation behind it, and get explicit confirmation on that specific change before writing it. Let them skip any field they don't want touched. Never write a value that has no citation behind it, even on request — say it's unconfirmed instead of writing it.

Read the full file on GitHub · 59 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 13d ago First seen · 59 lines · 189 tokens per session scan A 21698cbc5d01

Subscribe to this mod's changes

meddpicc-gap-audit is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 189 tokens to every session and 1,980 once invoked, about $0.0009 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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