> ## Documentation Index > Fetch the complete documentation index at: https://developers.within.ai/llms.txt > Use this file to discover all available pages before exploring further. # The non-technical track > Business users building skills and simple agents in Advisor or from a chat app. This track is for business users building in Advisor, or reaching the Company Brain from [ChatGPT](/install/chatgpt) or [Claude](/install/claude) via the MCP. The people who do the work know the exceptions, judgment calls, and source material that a useful skill must handle. Giving them a direct build path reduces the distance between the process evidence and the instructions the skill follows. This track builds skills and simple agents. Compound agents are [the technical track](/transformation/technical-track). ## Run recurring build sessions Bring people together on a regular cadence, in person or virtual, to build. In each session, they use Advisor or an MCP-connected client to turn repeated work into skills and reusable prompts. Repeated practice gives the team direct experience reviewing instructions, finding failure cases, and deciding what is safe to share. ```text theme={null} Find the thing my team repeats most in monthly close that a skill could take off our plate. ``` ```text theme={null} Turn the best version of how we handle a customer refund into a reusable skill, and show me the before-and-after workflow. ``` ## Choose your surface Either works, with a clear tradeoff. ### Advisor Advisor guides the build inside Within and produces a before-and-after workflow diagram. Use it when you want the analysis and resulting artifact to remain in Within. ### MCP-connected client The MCP brings Company Brain evidence into the chat app a person already uses. Use it when the build also needs files or connectors available in that client. ```text theme={null} From Claude, using the Within MCP plus my email and Slack, draft a skill that handles an inbound vendor request end to end. ``` [Chain with other connectors](/guides/chain-with-connectors) covers that pattern in more detail. ## Build one skill A skill fits a repeated task with a bounded outcome: prepare a brief, check an input against a policy, classify a request, or draft from known source material. The assistant should use the Agent Builder instructions served by the MCP rather than inventing its own build method. ### Sample prompt This example uses a fictional shipment-exception process. ```text theme={null} Use Within Agent Builder to create a reusable skill for the synthetic "Resolve shipment exceptions" process. The skill should prepare a review brief from the exception record and the documented process, then stop for human approval. It must not approve a disposition, update the case system, or invent a missing policy. Ground the instructions in the current process and representative observations. Separate documented rules from behavior inferred from captured runs. Show me the proposed scope, inputs, output, and refusal conditions before writing the package. ``` The assistant begins with the MCP-served method. A client-neutral representation of the first call is: ```javascript theme={null} get_agent_builder_instructions({ "step": "skill_track" }) ``` The returned instructions identify the resources needed for that build. The assistant first gathers current process evidence and drafts the proposed scope. You approve that outline before it writes the package. After the package is built, you review the result and its smoke-test evidence before installation. ### Sample output ```text theme={null} Proposed skill: shipment-exception-brief Scope Prepare one review brief for a shipment exception. A person decides the disposition. Inputs - Exception record - Current documented process - Available shipment and contact context Output - Facts found in the source records - Missing information - Applicable process rules - Recommended questions for the reviewer Refuse when - The current process cannot be retrieved - The exception record is missing - The request asks the skill to approve or update the case Grounding - Current "Resolve shipment exceptions" process - Five synthetic representative observations Package - skills/shipment-exception-brief/SKILL.md - skills/shipment-exception-brief/decision-rules.md - skills/shipment-exception-brief/caveats.md - skills/shipment-exception-brief/examples.md - skills/shipment-exception-brief/README.md - skills/shipment-exception-brief/evals/positive.md - skills/shipment-exception-brief/evals/negative.md - skills/shipment-exception-brief/evals/neighbor.md - INSTALL.md ``` The package keeps its evidence references in the working audit trail rather than placing internal citation tokens in the files people install. The generated `SKILL.md` should be short enough to inspect and flexible enough to apply the documented judgment to a new case. For example: ```md theme={null} --- name: shipment-exception-brief description: Load when preparing a shipment-exception review brief. Do not load for approving a disposition or updating the case system. --- # Shipment exception brief Prepare a review brief from the supplied exception record and the current documented process. ## Required content - Separate source-backed facts from missing information. - Apply only rules stated in the current process. - Draft questions and a non-binding recommendation for the reviewer. ## Decision boundaries - Stop if the process or exception record cannot be retrieved. - Treat conflicting evidence as an issue for the reviewer. - Do not approve a disposition or change the case system. ``` Test the package with ordinary cases, missing inputs, conflicting evidence, and requests that should be refused. A useful skill is defined as much by where it stops as by what it produces. ## Create a review group A small cross-functional committee can review the use cases and skills coming out of sessions, decide which ones are safe to distribute, and assign an owner for maintenance. ## Make it recurring Run the build session and committee review on a regular cadence. Repetition gives people time to improve the skills, add evaluations, and retire packages that no longer match the process. ## Keep a skills repository Store the good skills in one place. A simple GitHub repository works well. ## Ship the skills Ship approved skills through your AI platform's organization controls or publish them in a repository your team manages. Record an owner, version, and evaluation set for each distributed skill. ## Within support Within's team can facilitate the build session, help review the resulting skills, and work with you on production readiness. ## Support adoption This track works when leaders use the same tools they ask teams to adopt and when people have a recurring place to build, test, and improve skills from their own work. Hands-on building gives teams direct experience with what the tools can do, where they fail, and which controls they need. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.