Context Engineering Timeline
Three years of research into how AI agents should understand codebases, maintain memory, and learn team-specific patterns. From .context.md files to the Agent Skills standard.
This is the story of moving from prompt gambling to engineering discipline—one standard at a time.
The Evolution
Early Experiments: .context Files
Before standards existed, developers manually created .context.md files in their repos. Simple markdown files explaining architecture, conventions, and patterns. The AI would read them... if you remembered to tell it to look.
The problem: Static, manual, no discovery, no standards.
Codebase Context Specification (CCS)
I formalized the .context pattern into a proper specification at codebasecontext.org. One of the first standards for AI codebase context. Introduced hierarchical context (project, directory, file levels), multiple formats (Markdown, YAML, JSON), and conventions for AI consumption.
The breakthrough: Standardized format, early tool adoption.
The limitation: Still static. No dynamic loading.
mem8: Workspace Memory Research
Built mem8 as a Claude Code plugin to research dynamic context and workspace memory. Questions explored: How should agents maintain context across sessions? How do you surface the right information at the right time? What's the difference between project context and team knowledge?
The insight: Context isn't just documentation—it's about when to load information, what the agent needs for each task, and how to keep knowledge updated.
Agent Skills: The Open Standard
Anthropic publishes Agent Skills as an open standard. Solves everything we'd identified: dynamic discovery, task-based loading, cross-platform portability (Claude, Cursor, VS Code), git control, and team ownership. Microsoft and OpenAI immediately adopt compatible formats.
The realization: This is the standard. The evolution is complete.
Production Implementation
Now specializing in Agent Skills implementation for startups. Building foundry as a curated Skills registry plus consulting services for custom implementations. The research phase is over—this is production work.
View Solutions →Key Concepts
Static vs Dynamic Context
Static (CCS era): .context.md files sit in repo. AI reads them if told to.
Dynamic (Skills era): Agent discovers and loads Skills based on current task. No manual prompting required.
Documentation vs Procedural Knowledge
Documentation: "We use ECS for deployments"
Procedural: "Run this exact command with these flags, check these logs, rollback if X happens"
Prompts vs Skills
Prompts: "Remember we use Fargate with..." (context evaporates)
Skills: Git-controlled knowledge that agents load automatically (ground truth)
Individual vs Team Context
Individual: Each dev builds their own context through conversation
Team: Shared Skills library ensures everyone's AI follows same patterns
Research & Writing
Agent Skills: The End of Prompt Gambling
Deep dive into Anthropic's Agent Skills standard. Why it matters, how it works, and how to implement for your startup.
Dec 2025Death to Vibe Coding: Long Live Context Engineering
From prompt engineering to context systems. Why temporal context awareness and multi-modal integration matter more than clever prompts.
Jul 2025AI Coding - Beyond the Prompt
Mastering context engineering in Claude Code. Understanding thinking budgets, sub-agents, and keeping your context window clean.
Aug 2025Codebase Context Specification
The original CCS announcement. Video tutorial, AI journalist coverage, and the npm package for linting context files.
Sep 2024Stop Gambling. Start Engineering.
Three years of context research, now available as production-ready Skills implementation for your startup. Let's build your Skills library.