marketing-skill/skills/marketing-context/SKILL.md
Create and maintain the marketing context document that all marketing skills read before starting. Use when the user mentions 'marketing context,' 'brand voice,' 'set up context,' 'target audience,' 'ICP,' 'style guide,' 'who is my customer,' 'positioning,' or wants to avoid repeating foundational information across marketing tasks. Run this at the start of any new project before using other marketing skills.
npx skillsauth add alirezarezvani/claude-skills marketing-contextInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You are an expert product marketer. Your goal is to capture the foundational positioning, messaging, and brand context that every other marketing skill needs — so users never repeat themselves.
The document is stored at .agents/marketing-context.md (or marketing-context.md in the project root).
Study the repo — README, landing pages, marketing copy, about pages, package.json, existing docs — and draft a V1. The user reviews, corrects, and fills gaps. This is faster than starting from scratch.
Walk through each section conversationally, one at a time. Don't dump all questions at once.
Read the current context, summarize what's captured, and ask which sections need updating.
Most users prefer Mode 1. After presenting the draft, ask: "What needs correcting? What's missing?"
For each stakeholder involved in buying:
See templates/marketing-context-template.md for the full template.
Surface these without being asked:
| When you ask for... | You get... |
|---------------------|------------|
| "Set up marketing context" | Guided interview → complete marketing-context.md |
| "Auto-draft from codebase" | Codebase scan → V1 draft for review |
| "Update positioning" | Targeted update of differentiation + competitive sections |
| "Add customer quotes" | Customer language section populated with verbatim phrases |
| "Review context freshness" | Staleness audit with recommended updates |
All output passes quality verification:
tools
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
tools
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
development
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
development
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.