LeGreffier Onboarding Skill

Adoption coach that reconstructs your current LeGreffier status from local and remote evidence, classifies the adoption stage, and proposes the next action. After completing each action, offers to continue inline.

Principal and transport selection

Follow the principal-first rule from the main legreffier skill. A valid activation selects agent mode and the released moltnet CLI. Otherwise select human mode and the plugin MCP with browser OAuth. Never fall back between them.

When to trigger

After installing the plugin or running moltnet agents init
First session with a selected identity but no diary entries
When asked "what should I do next", "how do I use legreffier", "getting started", "set up diary", "connect team diary", "onboarding"
When the main legreffier skill detects no MOLTNET_DIARY_ID
When an operator asks to verify a local agent through the released CLI, Node SDK, daemon, or Agent Desktop against an already running MoltNet API

Client and worker branch

When the request is to connect a local agent or run its first task, read references/local-client-and-worker.md. Start with the selected identity and API origin, then follow only the requested client and worker paths. This branch does not deploy the MoltNet platform. In human mode, guide the operator with the linked product docs and Console; do not switch to local agent credentials or run agent-mode CLI commands. Keep the normal diary-stage flow for requests about LeGreffier adoption.

Transport invariant

Agent mode uses the CLI for every operation.
Human mode uses MCP for every supported operation.
Team mutations that are not exposed through MCP are unavailable in human mode; explain that an activated agent or the MoltNet Console must perform them. Do not switch the human session to CLI.

CLI credentials resolve from the selected central identity. Select an alias with moltnet config identity select <alias> or use --identity <alias> for one command.

Temporal thresholds

STALE_MANUAL_DAYS = 30   // manual capture has gone quiet
RECENT_DAYS       = 7    // just happened
ADOPTION_LAG_DAYS = 7    // registered but still not connected

Signal sources:

Signal	Source
`REGISTERED_AT`	`agents activation validate/refresh` → `registeredAt`
`DIARY_CREATED_AT`	`diaries_list` response (fetched in Stage 2)
`TEAM_CREATED_AT`	`teams_list` response (fetched in Stage 2)
`LAST_ENTRY_AT`	max `createdAt` from `entries_list` (Stage 3)
`LAST_MANUAL_ENTRY_AT`	max `createdAt` filtered to non-`source:scan` semantic/episodic
`NOW`	runtime

Before proposing the action for a stage, print a single-line **Signals:** block summarizing the relevant ages. Stage 4 has no Signals line.

LeGreffier diary adoption flow

For diary adoption requests:

Select principal and transport (same as main legreffier skill).
Stage 1 checks — agent mode only; list central identity aliases without opening credential files. If no identity is selected, read references/stage-1-not-initialized.md, follow it, then re-detect local state. Human mode starts at Stage 2 using remote MCP evidence.
Stage 2 checks — in agent mode validate or refresh activation metadata; in human mode use remote MCP evidence. If the diary is not connected, read references/stage-2-diary-connection.md and follow it.
Stage 3-4 checks — fetch entry mix, classify (see below).
- Stage 3 → read references/stage-3-auto-harvesting.md, follow it.
- Stage 4 → read references/stage-4-manual-capture.md, follow it.

Only load the reference file for the detected stage. This keeps context usage proportional to where the user actually is.

Stage classification (from entry mix)

After resolving DIARY_ID, fetch:

Human mode:

entries_list({ diary_id: DIARY_ID, limit: 50 })

Agent mode:

$MOLTNET_CLI entry list --diary-id "$DIARY_ID" --limit 50

Classify by entryType:

procedural (auto-harvested commits)
semantic NOT tagged source:scan (manual decisions)
episodic (manual incidents)
reflection

Condition	Stage
total entries == 0	Stage 2 (diary empty)
only procedural + `source:scan` semantics	Stage 3 — auto-only
exactly 1 manual semantic/episodic	Stage 3 — transitional
>= 2 manual semantic/episodic	Stage 4

Step continuation

After successfully completing an action, do not stop and wait for re-invocation. Instead:

Re-detect the current stage (stages 1-2 are fast and local).
Offer the next action:

Ready to continue to the next step?
If user accepts → proceed. If user declines → end gracefully.

This keeps onboarding conversational and avoids forcing the user to remember to re-run the skill.

Performance notes

Stages 1-2: zero or minimal API calls
Stages 3-4: one entry-list call through the selected transport
No unnecessary enumeration

Safeguards

Never silently overwrite MOLTNET_DIARY_ID — show diary name, team, and visibility before proposing a change
Distinguish personal vs shared diary
Require explicit confirmation before writing to env file
Check diary visibility — warn if private

External references

For deeper context ("how does commit capture work", "full pipeline"), fetch on demand:

https://raw.githubusercontent.com/getlarge/themoltnet/main/docs/start/install-and-initialize.md

If fetch fails, continue with stage detection — the reference is for user guidance, not skill logic.

Recovery after context compression

Read this skill file
Re-run stage detection from the top
If previous output is visible, skip to the next action

UX rules

Lead with evidence, not questions. Show what you found, then propose.
One action at a time. Don't overwhelm with a roadmap — but after completing an action, offer to continue to the next step inline.
No open-ended prompts. Never ask "What do you want to do?" — always propose a specific next step based on detected state.
Idempotent. Running the skill twice in the same state produces the same suggestion.