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Command

/status

Print John workspace status — active template, current phase, inventory of inputs/parsed/chunks/knowledge/events/checkpoints/produced-skills. Use whenever the user asks "where are we?", "what's done?", "what's next?", or you need to verify state before advancing a phase. Cheap;

From plugin
joharnessburg
85 skills5 agents5 commands
Install
$ npx -y skills add kitchen-engineer42/joharnessburg --agent claude-code

How it fires

How this command gets triggered: by you, by Claude, or both.

  • Fires itselfClaude auto-loads it when your prompt matches the work.
  • You can call itInvoke it directly when you want it.
  • Slash command/status

Context preview

What this command does when you run it.

Print John workspace status — active template, current phase, inventory of inputs/parsed/chunks/knowledge/events/checkpoints/produced-skills. Use whenever the user asks "where are we?", "what's done?", "what's next?", or you need to verify state before advancing a phase. Cheap;

Command definition

status.md
description: Print John workspace status — active template, current phase, inventory of inputs/parsed/chunks/knowledge/events/checkpoints/produced-skills. Use whenever the user asks "where are we?", "what's done?", "what's next?", or you need to verify state before advancing a phase. Cheap; run it generously.

When this command fires:

1. Invoke the status script via Bash:

   python3 "${CLAUDE_PLUGIN_ROOT}/scripts/workspace_status.py"

2. The script emits JSON to stdout and a human-readable summary to stderr. Show the user the human-readable summary directly. Use the JSON to decide your own next action (e.g., advance phase, dispatch subagents).

3. On `success: false` with "No .john/ directory found", tell the user to run `/john:init` first to scaffold the workspace.

After showing the status, if the user is asking "what's next?", consult `PLAN.md` (read the file) to identify the next incomplete phase and propose advancing it per the `ralph-loop` skill.

Ships withjoharnessburg

中文版: README_ZH.md John turns unstructured source material into a working knowledge-dense app. It keeps knowledge engineering and app building in one durable run, coordinates large per-entry fan-outs, and leaves auditable events and checkpoints on disk.

Get the whole plugin, auto-invoked
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Maintenance
Python
Language
MIT
License
29d ago
Last commit
2mo ago
Created

Repo: kitchen-engineer42/joharnessburg