TL;DR: The MCP tools and agent skills behind Credible's agents are now open source, contributed straight into Malloy Publisher. They run wherever you already work, no Credible account required. The skills are excellent -- but excellence is no longer a moat. Anyone with a few good textbooks, a couple of experts, and Claude can now build a great set of skills, and the premium on that layer is gone whether vendors admit it or not. We traded it for the three things a premium can't buy: trust you can verify, architecture that hardens in the open, and doors into the product for every kind of user. We'll make up the premium in volume and scale. This post is that trade in full.
What We Just Open Sourced
The agent skills and MCP tools behind Credible's agents now live in Malloy Publisher, the open source server for Malloy models. Retrieval tools that let an agent look up what your model actually defines, and 24 skills encoding the discipline to use them well: the query patterns, the gotchas that trip up frontier models, and the analysis rigor that separates a real answer from a plausible one. Everything runs wherever you already work. No Credible account required.
This is the opening post of a series. The deep dives -- what we shipped, the principles behind it, and a tutorial that gets an agent answering real data questions in minutes -- land over the coming weeks.
The reaction to the news is predictable, because I'd have the same one: those skills are the product. That's the expertise. Why would you give that away?
Because of an uncomfortable conclusion we reached about our own work, and about this entire market.
Excellence Is No Longer a Moat
In 1884, the Washington Monument was capped with a small pyramid of aluminum -- then a precious metal, priced alongside silver. Napoleon III served his most honored guests on aluminum; gold was for everybody else. Two years after the capstone went on, a new smelting process made aluminum cheap. Within a decade it was a commodity.
The metal did not get worse. It's as light and strong and useful as it ever was. What died wasn't the value. It was the scarcity. Value and defensibility came apart.
Data analysis tools and skills are having their aluminum moment. Here's the recipe for a high-quality set of agent skills in any specialized domain: take a few of the best textbooks on the discipline you're trying to encode, and a couple of experts who've lived it. Hand Claude a tool and skill design philosophy and a style guide. Iterate.
That's exactly how ours were built, and I'd put them against anything on the market. That's the point: the skills are aluminum -- valuable, and now anyone can make them. Excellence stopped being a moat the moment it became reproducible.
The Premium Is Gone
Once everyone knows a competent team with Claude, a few textbooks, and a couple of experts can rebuild this layer, nobody pays a premium subscription for it. Not for anyone else's version, and not for ours. The premium that specialized SaaS trained this industry to expect is gone from this layer, permanently. Open sourcing doesn't cause that. It stops pretending otherwise.
Hard truths don't get easier in installments. The installment plan is obvious -- keep the skills proprietary. Wrap them in “trusted AI” copy. Spend the next two years convincing our customers, our investors -- and ourselves -- that the premium still has legs. Plenty of companies are running that play right now, and some will win the fight for a few years.
We committed to a different direction while it was still a choice. This layer is not the business. Give it away. Build on what's actually hard.
Don't Bet Against the Standard
A lot of people can't accept this truth. Their identity is tied to their skills. You can see it in the wave of “data analysis agents” on the market: at best, a set of skills and MCP tools under a proprietary wrapper; at worst, a bespoke agent harness, a pile of prompts, and an accumulation of things that felt innovative nine months ago.
Both are bets against the standard, and the standard is winning: MCP and agent skills are where the ecosystem is pouring its engineering, and every model release rewards the systems that conform and strands the ones that don't. The craft still matters -- but the experts who thrive in this era will compound it in the open, not guard the forge.
Trust but Verify
Coding agents handed the industry the thing it spent a decade dreaming about -- and now anyone can build the dream, chat your data & build a dashboard demo. Natural language in, answer out, chart on top. Everyone can build it, so everyone claims it, and every product page in the AI data space reads like every other one. When the demos converge, the claims converge, and marketing stops being a differentiator.
Which explains the one word you'll find everywhere: trust. Trusted analytics. Trusted data for AI agents. Trustworthy AI architecture. When you can't differentiate on what the product does, you reach for the one claim nobody can falsify. Underneath the word sits the same positioning every time: a black box you can't see inside and can't take with you -- trust us and lock in. It's the same tired SaaS model -- proprietary product, premium subscription, walled garden -- but based on “trust”.
We're taking the opposite approach, all the way down. There is no magic. Here are our skills -- every one of them, readable, in the open -- shaped by a design philosophy we also published and distilled from a community whose expertise runs deeper than any proprietary bench. Read them, fork them, extend them with your own institutional knowledge, or tell us where they're wrong. Publicly.
Trust is earned with candor; it can't be demanded in marketing copy. Every vendor asking you to trust their black box is asking you to skip the only step that creates trust: looking inside.
Open Architecture Compounds
If marketing can't break the tie, product quality will. Most of the market is looking for quality in the wrong place -- the prompts, the wrapper, the surface. Quality lives in the architecture underneath, and over time, architecture will dictate everything above it.
AI has disrupted software architecture as much as the software itself. Coding agents have flooded the market with systems that demo well and fall apart in production -- AI slop, shipped as architecture. Most of it will crumble under the next wave of AI adoption. The architectures that scale and extend will survive. And each wave thins the field: the survivors get scarcer, the problems get harder, the wins get bigger and more valuable, and somewhere along the way they become durable businesses.
So the question that matters over a decade isn't whose demo is slickest. It's whose architecture survives. An architecture built and stressed by a community -- extended in directions we didn't plan, broken by workloads we didn't imagine, fixed by people whose expertise runs deeper than any one company's bench -- ends up more flexible and more robust than anything we could harden alone. A closed architecture ages at the speed of one team's payroll. An open one compounds.
Meet Every User Where They Work
No company has one kind of data user. But every SaaS product is built as if it does: one surface, one login, one way of working, and everyone gets marched through the same door.
We built the opposite. Because the stack is open and conforms to the standards, the same tools and skills reach you through whichever door fits how you work:
Open source. Download the server, the tools, and the skills, and start building. No account, no sales call. The software you get is the core of the architecture we run.
Plugins. Install our skills and MCP tools into the agent you already use -- Claude, ChatGPT, Gemini, your IDE. No new surface to learn. The meaning comes to where you work.
The bundled app. The familiar turnkey package -- hosted, governed, with a world-class in-app experience for people who want to open one thing and start building and asking questions. The difference from every other SaaS app you've bought: what's underneath isn't a secret.
The hybrid. Start from our tools and skills, then extend them to capture your organization's unique insights -- the edge cases, the business rules, the way your team works data. Your experts encode institutional knowledge as skills on the same architecture we build ours on. Bundle them in your plugin or app.
And the doors all open into the same room. Your data engineer works the open source stack in her IDE. Your analyst lives in the app. Your ops lead asks questions through a plugin in the agent he already had open. Different personas, different packaging, one governed model underneath -- all collaborating on the same meaning. Same components, same tools, same skills, remixed to meet each user where they are.
That's the promise to customers. The promise to the community is the flywheel: every team that extends the skills and contributes back grows the commons -- expertise, encoded as skills, compounding into the package everyone builds from. No closed vendor can match it. They have to sell you their door; we meet you at whichever one you pick -- change doors anytime, or use all four at once, and nothing underneath changes.
The Premium for Trust, Durability, and Distribution
Here's the whole strategy in one breath. We made one decision -- stop defending a layer that can't be defended -- and it bought us the three things no premium could: trust, durability, and distribution.
What we gave up was the premium. It was already gone. We will make it up in volume and scale: when the doors are open, far more people walk through them, and the architecture is built to carry them all. That's the “actually hard” thing we committed to building -- not the layer we gave away, but the platform underneath that has to carry everyone who shows up.
And this is the part that has us excited. AI has dramatically reshaped the playing field, and open source came out of it with more leverage than it's ever had -- every model release amplifies shared code, shared tools, and shared expertise faster than any proprietary shop can match. That's the future we're betting on, and our mission is to realize it.
The piece this post hasn't covered is our business model itself: what we charge for, and why the same open strategy aligns our incentives with our customers' instead of against them. That's a later post.
In the meantime, we're proving the candor is real. The series starts with the engineering: what we shipped and the principles behind it, then a hands-on tutorial that gets an agent answering questions against real data in minutes. From there we publish the expertise itself, one theme at a time, following the life of a governed model in the agent era: how agents analyze without hallucinating, how they build the model, how they ship data apps, how the model scales, how you prove it works with evals, and how you govern what agents see. The open-sourcing continues on the same arc -- skills and the contributor template today, more of the stack landing in Publisher as the series runs.
The tools and skills are free now. The expensive thing was admitting why they had to be.