format-mcp-skills

Stop re-explaining your company in every chat. One run distills what customers actually say — who you serve, what they struggle with, the words they use — into a context doc your team's AI works from, every claim quote-backed. It won't invent your strategy or pricing; it flags those for you to fill.

Ground a ticket in customer reality before you build it. Paste it (or point at the tracker issue) and get back what customers have actually said about the problem behind it: each distinct ask in their own words, who raised it and when, every quote linked to its source — and the requirements the evidence implies but the ticket doesn't spell out.

Hold your roadmap up against what customers have actually said. Paste a roadmap (or point at a tracker project) and get an evidence board: what customers have said about each item with links to every supporting quote, and the things they keep raising that map to nothing you're building.

Scan your book of business for what's actually been said across customer conversations — risks, blockers, adoption, relationships, growth and commercial signals — grouped per account with verbatim evidence. Built for weekly briefs and QBR prep.

Find your strongest case-study candidates from customer conversation data and walk into the interview with a near-finished draft — built from what customers have already said, instead of starting from a blank page.

Build an evidence-backed Ideal Customer Profile from real customer conversations: an ICP snapshot, buyer personas, in-market language cues, target-account criteria, and the competitive landscape — one document the whole go-to-market team can work from.

Ship ad copy that sounds like your customers because it is built from them: 3–5 angles each anchored to a verbatim quote, spec-compliant copy for LinkedIn, Google RSA, and Lead Gen Forms, with character counts validated and CSV blocks ready to upload.

Sales collateral reps actually trust because it's built from your own deals: pick the asset — deck, one-pager, objection doc, demo script, battlecard, persona card, or ROI framing — and get it grounded in verbatim customer language and the objections and proof points your conversations actually contain.

Blog posts that AI answer engines cite because they contain something no model can fabricate: your customers' real words and honestly counted data. One checkpoint to agree the angle, then a publish-ready draft — answer-first structure, extractable sections, evidence appendix.

A rep scorecard built from every real call they ran in the window — not a sampled highlight reel: each conversation classified, prospecting calls scored dimension by dimension with verbatim evidence, and exactly one coaching priority, framed Situation–Behaviour–Impact.