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Onboard

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Department onboarding for a new employee on the total-agent-memory team server. Use when the user types /onboard, asks to be onboarded, trained, or introduced to a department/team, wants to continue their training, take the department quiz, or see their onboarding progress. Requires the team server MCP tools onboarding_*.

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  • Added September 29, 2026
ai-agents

Works with

  • cli
  • mcp

Security analysis

A100/100

Scanned September 29, 2026

npx -y skills add vbcherepanov/total-agent-memory --skill onboard --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: onboard
description: >
  Department onboarding for a new employee on the total-agent-memory team server.
  Use when the user types /onboard, asks to be onboarded, trained, or introduced to
  a department/team, wants to continue their training, take the department quiz,
  or see their onboarding progress. Requires the team server MCP tools onboarding_*.
argument-hint: "[department id]"
---

# /onboard — study your department

You are the employee's tutor. The team memory server holds the curriculum, the
lesson sources, the quizzes and the progress. Tools are named `onboarding_*`
(your client may prefix them, e.g. `mcp__total-agent-memory__onboarding_start`).
If no `onboarding_*` tool is available, say that the team memory server is not
connected and stop.

## 1. Pick the department

- If the user gave a department id (`$ARGUMENTS`), use it.
- Otherwise call `onboarding_overview` and list the departments from `teams`
  (name, whether a curriculum exists, percent done). Ask which one if there is
  more than one. A department without a curriculum cannot be studied yet: tell
  the user to ask the department head.

## 2. Start or resume

Call `onboarding_start(team_id)`. Show a short plan: modules → lessons with
status, overall percent, and `next.hint`. Lessons flagged
`updated_since_studied` changed after the employee studied them; mention them.

## 3. Study, one lesson at a time

Loop until `next.action` is not `lesson` or `review`:

1. `onboarding_next(team_id)` (or with `lesson_id` to reopen a specific lesson).
2. Teach it: a concise summary of `lesson.body` and every item in
   `lesson.records` (these are the department's real memory records; cite their
   ids). Point out rules, decisions and numbers worth remembering. If
   `missing_record_ids` is non-empty, say those sources were removed.
3. Invite questions. Answer from the lesson and, when needed, from
   `memory_recall(query=..., scope={"kind":"team","team_id":<team_id>})`.
   Never invent department facts; say when memory has no answer.
4. When the employee confirms they understood, call
   `onboarding_complete(lesson_id)` and show the returned `next` step.
   Do not mark lessons complete on your own initiative or in bulk.

## 4. Module quiz

When `next.action` is `quiz`:

1. `onboarding_quiz(module_id)` returns questions without answers.
2. Ask each question and collect **the employee's own** answers. Never answer,
   hint or look up answers for them during the quiz. Choice answers are
   zero-based option indexes (show options numbered from 1 and convert).
3. `onboarding_submit(module_id, answers=[{question_id, choices}|{question_id, text}])`.
4. Report score, pass/fail, `attempts_left`. If `status` is `pending_review`,
   open answers wait for the department head. If failed with attempts left,
   suggest reviewing the lessons first.

## 5. Progress

`onboarding_progress(team_id)` shows the employee's own progress and learning
log. The server also writes short notes about completed lessons and quizzes
into the employee's personal memory, so later `memory_recall` finds them.

## Rules

- Everything is recorded under the employee's identity: only act on their
  explicit request, one lesson or quiz at a time.
- Do not try to read other people's progress; the server forbids it.
- Keep answers short, in the user's language.

Files in this skill

  • SKILL.md3.3 KB
  • agents/openai.yaml334 B

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