Decide

The AI build pattern library

38 everyday work tasks, from meeting notes to invoice matching, each with a build verdict, the reasoning, what would change it, and where to start.

Every pattern below went through the same eight questions as the decision tool, and each verdict is exactly what the tool returns for those answers. A test in the site's code checks that on every build, so the library and the tool can't drift apart. Open any card for the starting point, what would push it up or down the ladder, and a link that loads its answers into the tool so you can change one and watch the verdict move.

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How to read a verdict

A verdict is the cheapest rung that works for the typical version of the task. Your version may differ, and the "climbs if" and "drops if" lines say how. Meeting notes are a skill for a team that wants identical action-item formats, and a saved prompt for one person. Lead triage is an agent because leads arrive all day and nobody should have to press go, but if one person reviews them every morning it's a skill plus a CRM connector.

Three patterns land on "don't build it", and they're the ones people most often build anyway. Expense categorisation, inbox reply drafts and meeting scheduling are all solved by tools most teams already pay for. The work is switching the feature on and configuring it, which is less fun than building something and much cheaper.

What the 38 patterns show

The split is lopsided. Fifteen tasks are skills, seven need a connector or MCP server, four are agents, three each are plain prompts, saved prompts and apps, and three are "don't build". Two things drive most of the climbs. Live data is the big one: as soon as the answer depends on what's in the CRM, the warehouse or the help desk, copying it into a chat stops being reliable and the floor rises to a connector. The other is autonomy. Only the tasks that must run with nobody pressing go (lead triage, churn alerts, bug triage, competitor pricing) become agents, and even then two of them would do fine as a simpler scheduled workflow.

Apps are rare. The three that need one are invoice matching, a customer-facing support assistant and cold outreach that sends without review, and each has the same profile: high volume, a high cost of error, and either customers or money on the other side. If a task doesn't have that profile and someone is proposing an app, ask which answer forced it there.

Where a card links to "rated skills", it goes to a category on Skillspace, a directory of public agent skills that we run. Skillspace indexes skills from GitHub and grades them with a published, static scoring method; its own methodology page says the grade can't tell you whether a skill works, only how well it's built and whether its files trip a safety scan. So treat a linked skill as a starting point to read and adapt, never as something to install blind. Anthropic's own guidance says the same for any skill you didn't write. Other places to look, and how they compare, are on the registry comparison.

Questions people ask

Which AI use cases need a custom app?

In our library, only three of 38 do, and they share a trait. Customers use them directly, they run hundreds of times a day, or a wrong output moves money with nobody reviewing it. Invoice matching, a customer-facing support assistant and automated cold outreach all land there.

What is the most common verdict?

A skill. Fifteen of the 38 tasks are a stable procedure with reference files that a team repeats, which is exactly what a SKILL.md folder captures. Most of them take an afternoon to write.

Are the Skillspace links sponsored?

No, they're ours. We run Skillspace, a directory that rates public agent skills, and we link to its category pages so you can find rated skills for a pattern. We don't link to it where it has nothing relevant.