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Context Overflow

Like Stack Overflow, but for context engineering — thinking techniques for humans and AI agents, from one corpus, through two doors.

Most guides teach you to write better prompts. Context Overflow teaches something different: how to work with AI so that it extends your thinking instead of replacing it — and gives your agent the same techniques through a protocol it can actually run.

The two doors

For humans — contextoverflow.org. A curated, categorized corpus of thinking techniques. No login, no feed. You arrive with a problem — the site opens with "What are you trying to get done?" — find the technique that fits, and learn to build it into a prompt yourself: worked, annotated examples, never copy-paste snippets.

For agents — MCP at contextoverflow.org/mcp. The same corpus served over Streamable HTTP; free, keyless, stateless. Five tools:

Tool What it does
list_categories The eight problems, as a human would say them
classify_intent Symptom description → matching techniques; genuinely ambiguous → one clarifying question, never a guess
find_technique Direct lookup by name
get_technique Mechanism, agent instructions, verification, failure modes
apply_technique The runnable scaffold + narration line + the check that proves it worked

Every response carries a narration line, so the agent's technique use surfaces in-conversation in the same vocabulary its human learned on the site. When your agent says "one real unknown before I act," you know exactly which technique is running. Both sides of the pair get smarter; neither goes opaque. Setup for every client: contextoverflow.org/connect.

The eight problems

lost-the-thread · doing-my-thinking · confidently-wrong · agrees-with-everything · stalls-instead-of-acting · bloated-answers · starting-blind · problem-too-big

Each named for the problem as you experience it — "My AI forgets everything between sessions," "It tells me I'm right even when I'm not." Every technique lives in exactly one, and answers it.

What makes an entry

Entries are grounded in real production use (generalized field notes) or published research (verified citations only — an unverifiable attribution doesn't ship). Every entry states its mechanism, its verification check, and its failure modes — if we can't tell you how to know it worked, it doesn't ship. The full contract: corpus/SCHEMA.md.

Repository layout

  • corpus/ — the techniques. The repo is the database; site and MCP are two views of it.
  • site/ — the human door: Jekyll, generated from the corpus at build time.
  • mcp/ — the agent door: a stateless TypeScript Cloudflare Worker, corpus compiled in, no LLM inside.
  • validator/ — the gate both doors build behind: schema, section order, edge integrity, and a build-failing privacy blocklist.

Contributing

See CONTRIBUTING.md. Short version: issues and discussion are open; corpus and code contributions require a CLA (activating shortly after launch), and every entry must pass the validator and carry real evidence.

License

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Like Stack Overflow, but for context engineering — thinking techniques for humans and AI agents, one corpus, two doors.

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