Make your bots smarter.

Why is intelligence not enough?

Many people have tried asking ChatGPT to help with their work, only to get an answer that sounds capable but does not move the task forward. It is a little like asking a very smart intern to take over an assignment without teaching them your process or showing them the tools. They may be intelligent enough, but without clear instructions, useful context, and access to the systems the job depends on, the exercise quickly becomes frustrating.

Redeep closes those gaps in three practical ways. Skills teach an agent how you want a repeatable job done. Specialists give one focused part of an assignment to the right agent. Tools let the agent work in familiar places such as Gmail, Calendar, a browser, your Mac apps, and Kanban boards instead of merely describing what you should do next.

You can use any one on its own. Together, they turn a clever response into a dependable workflow and a finished result.

What is a skill in Redeep?

A skill is a reusable playbook: a checklist, instructions, and supporting templates that an agent loads when a task needs them. It is like giving a new colleague the team’s proven procedure instead of asking them to invent a process from scratch.

A data-analysis skill can require source checks, reproducible calculations, and a clear distinction between evidence and interpretation. A writing skill can carry your brand voice and the structure of a finished deliverable. A meeting-preparation skill might collect the agenda, recent correspondence, and open decisions before drafting a useful brief.

The best part is that skills are written in natural language. You can tell Redeep to create a specific skill for you, describe the method you want it to follow, and refine the playbook in ordinary words. You do not need to write code.

Redeep includes 26 starters across engineering, product, design, marketing, analysis, documents, automation, and conversation — including data analysis, accessibility review, landing-page copy, presentation decks, and spreadsheet reports. You can edit those starters or make your own.

Browse the categories in the skills documentation.

How do specialists divide a large task?

A specialist is a focused agent that takes one part of a larger assignment and reports back to the main agent. Several read-only specialists can explore different questions at the same time, while a specialist making changes works in an isolated copy of the project.

Suppose I ask for a plan to improve customer onboarding. One specialist inspects product analytics, another reviews interview notes, and a third maps the current experience. The main agent combines those findings into a recommendation. Later, a reviewer can challenge the evidence before anything is shared.

Redeep includes focused specialists for exploration, research, planning, implementation, review, and website design. You do not need to coordinate their messages yourself — the main agent delegates a bounded assignment, receives the result, and stays responsible for the final outcome.

This pattern shines when work benefits from independent perspectives or can be divided cleanly. It is less useful when every step depends on the last one. Good delegation is about clear boundaries, not simply adding more bots.

How can an agent use the tools you already know?

Redeep can use the tools you are already familiar with instead of asking you to rebuild your work around the bot. An agent can open Gmail in your signed-in browser to help draft a reply, check Calendar before proposing a meeting, move work across a Kanban board, read files on your Mac, or write and run a small program when that is the fastest route to the result.

Sometimes that means using the browser or operating a desktop app with your approval. Sometimes it means calling a built-in tool for documents, spreadsheets, research, or data analysis. Redeep can also connect to specialized services through MCP, a newer standard that lets AI products discover the actions and information another service offers.

You stay in charge of access. Secrets live in the macOS Keychain, and actions can run automatically, ask before acting, or be blocked. The agent gets the tools the task needs; you keep control over what it can do.

See the built-in tools and service connections available in Redeep.

How can a Kanban board coordinate specialist agents?

With Redeep’s built-in Kanban board, each stage of a workflow can call on an agent suited to that job. Picture a simple board with Ideation → Implementation → Review. The Ideation column can ask a research specialist to explore options. Moving a card into Implementation can start an independent agent in its own isolated worktree. The Review column can bring in a fresh reviewer to check the result before you approve it.

Each card remains a durable session with its own brief, conversation, files, and progress. Column actions provide the handoff instructions, while independent agents do the work. You can see what is running, what is waiting, and what needs your decision without coordinating a pile of separate chats.

You can also put subscriptions you already pay for to work. Redeep can delegate a bounded assignment to Codex or Claude Code through the account configured on your Mac. The delegate works in an isolated project copy and returns an explanation plus reviewable changes; Redeep does not inject your API keys into that process.

Use the board to decide which specialist owns each stage and where a human should approve the outcome. Start with one repeatable workflow, turn your standards into skills, and add delegates where a second opinion or deeper specialization will improve the result.

What makes an AI worker “smarter”?

A smarter AI worker is not merely a different model. It has better instructions, better context, and the right tools for the job. An AI worker is a persistent work session that uses files, code, data, websites, and desktop apps to complete an assignment — and a brilliant generalist can still miss a required review step, use the wrong report format, or work from stale information. Good operating structure closes those gaps.

Redeep gives me three practical ways to add that structure. Skills teach a repeatable method. Specialists divide a broad assignment among focused helpers. Connections let the worker act in the services and data sources your organization already uses.

You can use any one on its own. Together, they turn a broad request into a dependable workflow.

What is a skill in Redeep?

A skill is a reusable playbook: a checklist, instructions, and supporting templates that an AI worker loads when a task needs them. It's like giving a new colleague the team's proven procedure instead of asking them to invent a process from scratch.

A code-review skill might define what to inspect, how to rank findings, and how to cite each problem. A data-analysis skill can require source checks, reproducible calculations, and a clear distinction between evidence and interpretation. A writing skill can carry the brand voice and the structure of a finished deliverable.

Redeep includes 26 starters across engineering, product, design, marketing, analysis, documents, automation, and conversation — examples include code review, release notes, data analysis, accessibility review, landing-page copy, presentation decks, and spreadsheet reports.

You can edit the starters or make your own. A skill can be a short written playbook or a fuller bundle with templates and scripts. The point isn't complexity — it's to teach something once and reuse it across projects.

Browse the categories in the skills documentation.

How do specialists divide a large task?

A specialist is a focused helper that takes one part of a larger assignment and reports back to the main worker. Several read-only specialists can explore different questions at the same time, while a specialist making changes works in an isolated copy of the project.

Suppose I ask for a plan to improve customer onboarding. One helper inspects product analytics, another reviews interview notes, a third maps the current experience. The main worker combines those findings into a recommendation. Later, a reviewer can challenge the evidence before anything is shared.

Redeep ships with focused helpers for exploration, research spikes, architecture, implementation, review, and website design. You don't need to coordinate their messages yourself — the main worker delegates a bounded assignment, receives the result, and stays responsible for the final outcome.

This pattern shines when work benefits from independent perspectives or can be divided cleanly. It's less useful when every step depends on the last one. Good delegation is about clear boundaries, not simply adding more workers.

How can an AI worker use your existing tools?

Redeep can connect AI workers to databases, services, and internal tools through MCP, a common way for software to offer actions and information to AI. You don't need to understand the protocol to use it — think of each connection as a secure adapter to part of your stack.

A connection might let a worker look up a customer record, query an analytics service, read an issue tracker, or call an internal business process. Add local connections running on your Mac or hosted ones that use a normal sign-in flow.

You stay in charge of access. Secrets live in the macOS Keychain. Each connection can be trusted, set to ask before acting, or blocked, and individual actions can have their own rules. When a service offers hundreds of actions, Redeep lets the worker search for the relevant one instead of loading the whole catalog into every task.

MCP stands for Model Context Protocol. The name is technical; the outcome is familiar: your AI worker can use the tools where your real work already happens. Setup details are in the connections documentation.