Context-First Marketing Automation System
Encode your marketing intelligence before automating repeatable work
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 6
- Confidence
- 97%
This system separates durable marketing intelligence from the automated skills that use it. A team first creates a simple project structure, then records reusable context such as brand voice, visual identity, ideal customers, trusted sources, channel strategy, and performance knowledge. Project instructions explain how the AI should navigate those files and route work to the correct tools. Only after that foundation works does the team create reusable skills for specific outputs. Each skill loads the relevant context before acting, so automation scales the team's judgment rather than replacing it with generic model defaults. The structure begins small and evolves as real workflows reveal missing context, routing rules, or quality controls.
Origin
Extracted from Marketing Against the Grain, where Grace demonstrated a context-driven Claude Cowork system for automating multiple marketing disciplines.
Core principles
- 01Context precedes automation
- 02Reusable intelligence produces differentiated outputs
- 03Simple systems should evolve with actual use
- 04Skills inherit standards from shared context
- 05Explicit routing reduces inconsistent decisions
How to run it
- 1
Map the recurring disciplines
List the marketing areas the system must support, such as ads, SEO, social media, and websites. Create only the folders needed for current work.
Pro tip Start with a minimal structure and let usage reveal necessary additions.
Watch out Building ten or twenty speculative folders creates complexity before the system has proven useful.
- 2
Build the context layer
Record reusable intelligence about the brand, audience, strategy, voice, design, trusted sources, and successful patterns. Keep each file focused enough for an AI assistant to retrieve selectively.
Pro tip Capture both what works and what should be avoided.
Watch out Do not automate before foundational brand and customer decisions are clear.
- 3
Define project routing
Create project instructions explaining the folder structure, context-file routing, output locations, and expected behavior. Tell the assistant which context to load for each kind of task.
Pro tip Generate a first draft with the AI, then shorten and correct it manually.
Watch out Vague routing causes the assistant to ignore useful context or load irrelevant material.
- 4
Route specialist tools
Specify which connected tool or data source should answer each class of question, such as search performance, advertising data, or competitor intelligence.
Pro tip Name a preferred source for every data category with multiple possible tools.
Watch out Overlapping connectors can produce conflicting or unpredictable results.
- 5
Add reusable skills
Turn proven tasks and templates into skills that inherit the appropriate context. Keep each skill responsible for a bounded operation.
Pro tip Build skills from outputs that have already been reviewed and approved.
Watch out Treating skills as the starting point merely automates an immature process.
- 6
Evaluate and evolve
Compare generated work with the team's standards, diagnose whether failures come from context, routing, templates, or execution, and update the relevant layer.
Pro tip Correct the shared layer when the same problem appears across several skills.
Watch out Adding prompt complexity to every individual task hides systemic defects.
In the wild
A marketer stores brand visuals, voice, audience strategy, and project-routing instructions in a shared system. When asked to propose carousel topics, the assistant loads the strategy and ideal-customer context, selects the approved voice and design skills, and saves the resulting asset in the social folder.
→ The carousel reflects the marketer's established taste and arrives in the correct project location without a long one-off prompt.
A paid-media team creates reusable context for its customer, visual standards, winning ad patterns, and reporting definitions. Project instructions route performance questions to connected ad platforms and competitive questions to approved research tools before an ad-production skill runs.
→ Campaign work uses company-specific evidence instead of generic advertising best practices.
Common mistakes
Automating before defining context
A skill cannot reliably reproduce brand judgment that has never been made explicit. The model fills the gap with average patterns learned from general material.
Overbuilding the initial structure
A sophisticated folder hierarchy increases maintenance and makes navigation harder. Begin with the smallest system that supports real work.
Leaving tool selection implicit
When several connectors can answer the same question, the assistant may choose an unsuitable or inconsistent source. Route each data class explicitly.
Is it for you?
Best for
Marketing teams that repeatedly create branded assets, campaigns, analyses, or channel-specific content with AI.
Not ideal for
One-off tasks where reusable context, consistent quality, and future automation have little value.
From the transcript
“So I will prepare all those context files first before like doing anything.”
“Yes absolutely because yeah most marketers just directly jump okay I have to create skills but to me skills is always like the last mile…”
“So we want to brief claw how to route the system, how to navigate different folders, and also like the tool routing.”
From the episode
How to Automate ALL of your Marketing w/ Claude Cowork