memory-protocol
Universal protocol for total-agent-memory MCP server. Activate at session start, before any non-trivial task, after every…
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.
Claude Code
$ npx -y skills add vbcherepanov/total-agent-memory --skill onboard --agent claude-code
Codex
$ npx skills add vbcherepanov/total-agent-memory --skill onboard -a codex -y
Cursor
$ npx skills add vbcherepanov/total-agent-memory --skill onboard -a cursor -y
Gemini CLI
$ npx skills add vbcherepanov/total-agent-memory --skill onboard -a gemini-cli -y
GitHub Copilot
$ npx skills add vbcherepanov/total-agent-memory --skill onboard -a github-copilot -y
OpenCode
$ npx skills add vbcherepanov/total-agent-memory --skill onboard -a opencode -y
Windsurf
$ npx skills add vbcherepanov/total-agent-memory --skill onboard -a windsurf -y
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]"
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.
(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.
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.
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.
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.
`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.
explicit request, one lesson or quiz at a time.
Persistent local memory for AI coding agents — Claude Code, Codex CLI, Cursor, any MCP client. Temporal knowledge graph, procedural memory, AST codebase ingest, cross-project analogy. LongMemEval R@5 95.1%, LoCoMo 0.607, BEAM 1M 0.448. 74 MCP tools, 9 IDEs, 100% local.
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Ask which skill or flow fits your situation. A router over the skills in this repo.