u/Akhil_vallala

Back in October 2025, Simon Willison (who built Datasette and is one of the most reliable technical voices in AI) called Claude Skills "maybe a bigger deal than MCP." That's a bold claim. In this video I break down why I agree with him.

What we cover:
→ What a Claude Skill actually is (it's just a markdown file)
→ The token math that makes Skills dramatically more efficient than AGENTS.md
→ Skills vs MCP - they're not competing, they're different categories
→ How to build your first skill in 4 steps (takes ~30 minutes)
→ How skills self-improve over sessions - unlike prompts
→ Chaining skills into full automated workflows
→ Where skills fit in your full agent stack

Full article with code examples and diagrams:
https://medium.com/p/3d706ba52044

Also check out my article on OKF - the missing piece for AI agents:
https://medium.com/@AkhilAIWorld/google-just-quietly-released-the-missing-piece-for-ai-agents-its-called-okf-7e96a33898ce

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Been diving deep into agent memory architecture lately and stumbled on OKF - Open Knowledge Format - published by Google Cloud on June 12th. It's gotten way less attention than it deserves.

The core idea is simple: instead of explaining your codebase/systems to an AI agent every single session, you build a .okf/ directory of markdown files with YAML frontmatter that any agent can read. One required field (type). No SDK, no schema registry, no vendor lock-in. Just files.

What makes it interesting vs. just using CLAUDE.md or AGENTS.md:

• It's a knowledge graph, not a flat list - concepts link to each other via plain markdown links
• Versioned in git next to your code
• Works across any agent (Claude Code, Cursor, Codex, 20+)
• Karpathy's LLM wiki gist basically predicted this pattern; Google just formalized it

I wrote two pieces on it if anyone wants to go deeper:

Part 1 - What OKF is and how it works: Google Just Quietly Released the Missing Piece for AI Agents. It's Called OKF.

Part 2 - OKF + RAG together (when to use each, hybrid architecture): Your AI Agent Has Two Memory Problems. OKF Solves One. RAG Solves the Other.

The OKF vs RAG breakdown is the part I found most useful - they're not competing, they solve different memory problems. OKF handles your "known-knowns." RAG handles the large unstructured corpus. Most production stacks need both.

Curious if anyone here is already using something like this pattern.

Been diving deep into agent memory architecture lately and stumbled on OKF - Open Knowledge Format - published by Google Cloud on June 12th. It's gotten way less attention than it deserves.

The core idea is simple: instead of explaining your codebase/systems to an AI agent every single session, you build a .okf/ directory of markdown files with YAML frontmatter that any agent can read. One required field (type). No SDK, no schema registry, no vendor lock-in. Just files.

What makes it interesting vs. just using CLAUDE.md or AGENTS.md:

• It's a knowledge graph, not a flat list - concepts link to each other via plain markdown links
• Versioned in git next to your code
• Works across any agent (Claude Code, Cursor, Codex, 20+)
• Karpathy's LLM wiki gist basically predicted this pattern; Google just formalized it

I wrote two pieces on it if anyone wants to go deeper:

Part 1 - What OKF is and how it works: Google Just Quietly Released the Missing Piece for AI Agents. It's Called OKF.

Part 2 - OKF + RAG together (when to use each, hybrid architecture): Your AI Agent Has Two Memory Problems. OKF Solves One. RAG Solves the Other.

The OKF vs RAG breakdown is the part I found most useful - they're not competing, they solve different memory problems. OKF handles your "known-knowns." RAG handles the large unstructured corpus. Most production stacks need both.

Curious if anyone here is already using something like this pattern.

Been diving deep into agent memory architecture lately and stumbled on OKF - Open Knowledge Format - published by Google Cloud on June 12th. It's gotten way less attention than it deserves.

The core idea is simple: instead of explaining your codebase/systems to an AI agent every single session, you build a .okf/ directory of markdown files with YAML frontmatter that any agent can read. One required field (type). No SDK, no schema registry, no vendor lock-in. Just files.

What makes it interesting vs. just using CLAUDE.md or AGENTS.md:

• It's a knowledge graph, not a flat list - concepts link to each other via plain markdown links
• Versioned in git next to your code
• Works across any agent (Claude Code, Cursor, Codex, 20+)
• Karpathy's LLM wiki gist basically predicted this pattern; Google just formalized it

I wrote two pieces on it if anyone wants to go deeper:

Part 1 - What OKF is and how it works: Google Just Quietly Released the Missing Piece for AI Agents. It's Called OKF.

Part 2 - OKF + RAG together (when to use each, hybrid architecture): Your AI Agent Has Two Memory Problems. OKF Solves One. RAG Solves the Other.

The OKF vs RAG breakdown is the part I found most useful - they're not competing, they solve different memory problems. OKF handles your "known-knowns." RAG handles the large unstructured corpus. Most production stacks need both.

Curious if anyone here is already using something like this pattern.

Watched about 6+ hours of Greg Isenberg, Ras Mic, Matthew Berman, and Austin Marchese covering Claude agents, MCP, skills, and the Karpathy method. Tried to synthesize the most useful stuff into two writeups.

The biggest thing I took away: the models are good enough now. The gap between Opus 4.6 and GPT 5.4 is nearly irrelevant. What actually separates people getting 10x results is the architecture around the model - context files, memory.md, MCP connections, and reusable skills.

A few things that surprised me:

• Skills cost ~53 tokens per turn vs 944+ for equivalent agents.md entries. That gap destroys performance on long sessions.
• Ras Mic argues agents.md files are mostly counterproductive for most users (hot take but he makes a good case)
• Karpathy's method is dead simple: write a spec before you start, maintain a scratchpad, and feed every failure back into the system permanently

Wrote it up in full if anyone wants to go deeper:

Article 1 (agents, memory, MCP, skills): https://medium.com/p/d1d59321bc95

Article 2 (Karpathy's 3-layer method): https://medium.com/p/292a716bc840

Happy to answer questions - been deep in this stuff all week.