## For agents

- What Mensara is: it gives a team its own Mind, one shared memory for the team and their AI agents, built by Xanadu Labs LLC.
- Status: signup is not open. A person joins the waitlist at https://app.mensara.ai/#waitlist. Agents cannot create an account or sign up.
- How you can help your person: explain what Mensara does, check whether it fits a job their team repeats, and share the waitlist link.
- Agent references: https://app.mensara.ai/llms.txt lists every page as Markdown and the tool input references for connected agents.

---

# Discover a skill and prepare a tested revision

Operating procedures · For agents and technical readers · [How it works](https://app.mensara.ai/how-it-works)

Use the Mind's current skill discovery to select a procedure the connected client can perform. Treat authoring and executing a skill as separate work.

[Page as Markdown](https://app.mensara.ai/how-it-works/skill-procedure.md)

[Human guide](https://app.mensara.ai/how-it-works/use-and-create-skills)

## Use the released interface with current authority

Availability: Core0.20.11 / Portal0.3.47 source checked; discover the actual installed client and enabled features.

Who can do this: The connected principal with the permissions named in this procedure

What changes: The procedure explains its actual reads, writes or external effects before action.

Limits: An input accepted by a source schema is not a successful installed-runtime task.; Keep human decisions, client exclusions and pending results explicit.

Verification: Source checked and independently reviewed; scoped runtime receipts are recorded separately.

## Discover and load

Call `orient`, then `list_skills` with `{}`. The optional `capability` filter accepts `any` or `repo-agent`. Read the chosen procedure with `get_skill`:

```
{"name":"<discovered skill name>"}
```

Inspect its surface and runtime requirements. If the client cannot run a required script, repository step or subagent, report that limitation and hand the work to a compatible client. Do not treat reading the skill as successful execution.

## Follow the procedure within authority

Read its relevant supporting references and source records. Use the current client-facing schemas for each called tool. Keep the user's output and external-action authority separate from what the procedure recommends. Verify the resulting saved record or output and cite the source actually read.

## Author from observed work

For a repeated job, load `get_skill` with `{"name":"skill-engineer"}`. Check for an existing skill, gather real corrections and define outcome and trigger cases before drafting. A rule that must always hold belongs in a deterministic gate; fixed repeatable steps may belong in a script; an independently scheduled program belongs in an app bundle.

Run the procedure's baseline and candidate checks in the intended client and model within the agreed spend. Record observed outcomes; an unrun case is unverified. Propose the relevant instruction files through the authorised governed route and inspect the result. Instructions being saved does not prove later execution.

## Retire or revise deliberately

When a tool or task changes, use the same authoring procedure to revise the skill and rerun affected cases. Keep one clear procedure rather than near-duplicate instructions. Report unsupported or unavailable authoring steps without claiming registration or publication.

## Related

- [Use a skill or turn a repeated job into one](https://app.mensara.ai/how-it-works/use-and-create-skills)
- [Build, propose and verify a governed model agent](https://app.mensara.ai/how-it-works/build-agent-procedure)

[Join the waitlist](https://app.mensara.ai/#waitlist)