Why should every agent learn the same lesson again?

Imagine two agents fixing the same awkward integration.

The first finds a version mismatch, tries another route and gets it working. Tomorrow, the second starts without that history and walks into the same dead end. Both might finish. We have paid for the lesson twice.

That’s the opportunity I want to explore: something like Stack Overflow for agents, with a paid layer for context worth maintaining. The example is hypothetical; savings and demand still need measuring. “The agent can figure it out” doesn’t settle whether buying the lesson is worthwhile.

“Hey agent, act like Calvin”

My Solutions page is a small, free starting point: public skills, tools, selected methods from Calvin Ops and worked examples.

“Act like Calvin” should mean reading the documented approach, choosing what fits and showing how it changes the solution. It should allow disagreement, identify the agent’s own additions and say when nothing fits. It isn’t permission to imitate a personality or access private context.

The site’s llms.txt points to an agent guide and catalogue. The llms.txt proposal is a discovery convention, not a guarantee that agents follow it; you can supply the URL directly.

This is a free reference prototype. It does not run Calvin Ops for you or operate a paid marketplace.

What would someone actually buy?

A useful answer preserves the conditions under which it was right: supported versions, prerequisites, failed attempts, sources and a way to check the result.

The package could contain a specialist procedure, worked example, data or test. It should state what rights the buyer receives, its price, who maintains it and the update and support boundary. “Unique” needs to mean more than a longer prompt.

This can extend beyond coding. Researchers could package bounded methods and source trails; designers could preserve choices, rejected versions and why they failed. The buyer must still test whether those lessons transfer to a different situation.

Stack Overflow is already part of this story

Stack Overflow’s May 2024 partnership with OpenAI established an OverflowAPI licensing precedent with attribution to the community.

The version I imagine makes an inspectable individual package something an agent could buy with its user’s permission. It would need to beat public documentation, free skills, search or the agent’s own work.

A marketplace needs enough relevant supply to attract buyers, enough demand to reward maintenance, and a way to stop convincing wrong answers earning convincing reputations.

What September’s evidence adds

Microsoft’s 2 September 2026 account describes centrally managed skills that agents load when relevant and replace when updated.

It reports internal Foundry IQ evaluations with up to 54% better evidence recall on BrowseComp-Plus and 34% lower retrieval token costs. Those results combine retrieval, reranking, synthesis and token-use changes. They do not isolate purchased skills, whole-job cost or market demand.

Reusable context is becoming infrastructure. That helps the premise, but providers are also building the machinery. A separate market needs valuable material and a reason for creators and buyers to use it; storage alone may not be enough.

What people are trying—and what that proves

The useful lesson may be the work preserved after a real task. Whether the next agent actually receives and uses it is another question.

Calvin Tang’s September X post proposes recording what worked on tickets and turning recurring lessons into guidance. A Reddit discussion read this September argues that precise prompts and code references can matter more than accumulating skills.

Those are competing practitioner views, not a representative survey or evidence of willingness to pay. The challenge is fair: would the buyer do better simply by explaining the task properly?

The price has to beat doing the work again

I expect cheaper and more capable models. A context market has to make sense in that world, without depending on a temporary pause in falling compute prices.

Stanford’s 2025 AI Index reports inference cost at roughly GPT-3.5-level performance falling more than 280-fold between November 2022 and October 2024. That historical capability-level comparison is not a September 2026 price or a forecast.

Some packages should lose value as models improve or equally good free sources appear. Current specialist information, maintained exceptions or hard-won evidence might retain value; none is a permanent refuge.

Cheaper computation also cannot establish an unobserved event. That leaves a possible role for current, legitimately shared context, without telling us its market size or price.

More context can make the result worse

Anthropic’s context-engineering guidance treats context as limited and recommends retrieving relevant material when needed. Inspect the listing before loading the package; don’t hand the agent the whole shop.

A seller should state expiry and applicability. The receiving agent should verify the environment and stop when conditions fail. Paying for instructions grants no permission to upload private files, spend more or change unrelated systems.

Ten agents repeating one unchecked answer are not ten independent confirmations. Reputation needs dated evidence and failed checks. With permission, results could help repair or retire a package; buyers’ private work must not quietly become the next product.

The experiment worth running

Compare the same task, agent and quality requirement with and without the package. Include public documentation and free alternatives. Measure completed work, total cost, time, errors and human correction—not just transcript length.

Models may become good enough that most paid context adds little. Providers may absorb the useful parts. Those are serious alternatives to the business I am imagining.

For now, give your agent a real problem and the free Solutions collection. Ask what earns a place in its approach. The paid market would have to pass the same test: does yesterday’s work help, or only add another document to read?