Product messaging
SkillWeb & browsingBuilds a 10-component messaging library from website and product research. Produces value propositions, key differentiators,
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Product messaging skill
What this skill tells your AI
The instructions your AI receives, as published by matteotitta/genesys-skills in skills/primitives/product-marketing/strategy/messaging/SKILL.md and read by ahel’s review.
Builds a 10-component messaging library from website and product research. Output ships as the source of truth for all downstream marketing assets — landing pages, sales enablement, LinkedIn content, outreach. Knowledge type: messaging (per .claude/rules/ontology.md); maturity: emergent → validated after team review → canonical when locked. Visual phase map → the premium reference.
When to run
Invoke when the user asks for: product messaging for [URL/company], messaging library for [product], extract messaging from [website], product messaging framework, capabilities and benefits for [company], what are [product]'s differentiators?, pain points and capabilities for [URL]. Do NOT invoke for: competitor analysis only (use /competitor-research), landing page copy directly (use /landing-page-copy — run this first), ICP research only (use /icp-behavioural), or single-feature questions (answer directly without full framework).
The Iron Law: no messaging output without source verification. Every claim cites URL + access date or is marked [Not available]. Every quote is verbatim. Every consequence chain traces 1st→2nd→3rd order. Full guardrails + red flags + anti-hallucination rules → the premium reference.
Inputs
Required:
website URL— primary product website (verify it loads).product name— exact product/company name (confirm if ambiguous, e.g., "Bolt" could be ride-share, fintech, orbolt.new).
Recommended (improve quality):
target ICP context— focuses messaging on relevant segments.competitor context— sharpens differentiators (use/competitor-researchoutput if available).internal docs— provides claims not on website.customer quotes— fills gaps in testimonial coverage.
Upstream skill outputs (if available, read first):
positioning(primary) — frames Description and core messaging blocks.icp-behavioural— enriches pain points and benefits with VoC data.competitor-research— sharpens status quo and differentiators.tov-guidelines— applies tone to messaging.
If website URL is missing, ask. If product name is ambiguous, confirm before starting.
Steps
- Validate inputs → verify URL accessible, product name confirmed, ICP context confirmed if not obvious. Pull upstream skill outputs (positioning, icp-behavioural, competitor-research) into context if available.
- Phase 1 — Discovery research → the premium reference. Fetch core pages (homepage, features, pricing, customers, about — 5+ pages with URLs + access dates). Search external data (G2, testimonials, vs-pages) per Exa protocol (
.claude/rules/exa-protocol.md). Extract branded feature names verbatim before structured extraction begins. Detailed search/scraping patterns → the premium reference. - Phase 2 — Structured extraction → the premium reference. Extract all 10 components in order: Description → Status quo & alternatives → Pain points (with consequence chains) → Capabilities → Functional benefits → Emotional & social benefits → Features (branded names) → Cost of inaction → Common objections → Core messaging blocks. Frameworks + descriptor counts + link graph → the premium reference.
- Phase 3 — Verification & gaps → the premium reference. Source-verify every claim (URL + access date), confirm verbatim quotes, assign confidence levels (High/Medium/Low →
[VERIFIED]/[INFERRED]/[ESTIMATED]), document data gaps + recommendations as Component 10. - Apply attribution standards → per
.claude/rules/ontology.md:[VERIFIED: source_type, reference],[INFERRED: from X + Y],[ESTIMATED: reasoning],[UNAVAILABLE]. Quality threshold for client-deliverable strategy outputs: ≥60% verified, ≤10% estimated. - Self-evaluate against quality gates → the premium reference. Run completeness, evidence-quality, and guardrail checks. Answer self-roast questions honestly.
- Write to client folder per output template → the premium reference. File path:
messaging/MMYY-messaging.md(or per client CLAUDE.md folder map). Header includes skill name, generated date, font (Inter), version. - Push to Notion (Product Messaging Database) and Google Docs (
client_folder/strategy/) per push targets in frontmatter. For refresh runs, UPDATE existing pages rather than duplicating. - Offer iteration prompts post-delivery → the premium reference. If user signals approval ("great messaging" / quick approval), offer to save as a reference example under the premium reference.
What good looks like
Evaluations (binary pass/fail before declaring "done")
- All 10 components present in correct order (or explicit
[Not available]per component with reason). - Status quo includes Manual/DIY + at least one named competitive alternative.
- Every pain point has a complete 1st→2nd→3rd order consequence chain (not cut short).
- Every pain point links forward to a capability; every capability links to a branded feature name.
- Every functional benefit includes a verbatim customer quote or explicit "Not available" with reason.
- Emotional + social benefits section complete: 2 emotional + 2 social.
- Cost of inaction section quantified (daily/weekly/monthly cost stated, not abstract).
- Common objections section complete (3-5 objections with root cause + Acknowledge/Reframe/Evidence response).
- Core messaging blocks complete: tagline ≤7 words, elevator pitch (1 sentence), 3-bullet value prop, proof point, audience-segmented messages.
- Every claim has source URL + access date; every quote verbatim; confidence level assigned (High/Medium/Low →
[VERIFIED]/[INFERRED]/[ESTIMATED]). - ≥60%
[VERIFIED]confidence; ≤10%[ESTIMATED](per ontology threshold for client deliverables). - Data gaps section non-empty if any component is incomplete or low-confidence; recommendations provided for filling each gap.
- Source appendix lists ALL referenced URLs with access dates.
- Output title is
# Product messaging library: [Product Name]exactly — no aliases.
Pre-slim original
Pre-slim SKILL.md (1,048 lines, v2.1) archived at .claude/skills/_archive/messaging/SKILL-pre-slim-20260429.md. See the premium reference ("Changelog") for the v2.2 entry documenting the slim.
Final ship gate
Run /premortem --output before ship. See /premortem skill for the 5 execution domains (will-it-resonate / will-it-convert / will-it-stay-on-brand / will-stakeholder-push-back / will-it-degrade-over-time) and output template.
Trivial-case escape: ## Premortem\nNo failure modes — trivial change satisfies the contract for genuinely trivial outputs.
Signals
- GitHub stars
- 36
- Forks
- 14
- Last commit
- Jul 2026
Advanced
- Catalog kind
- skill
- Gateway key
product-messaging-matteotitta- Source
- github.com/matteotitta/genesys-skills