agent-device-lab
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Manage one tmux agent lane from its matching ops pane, inspect live pane state and Codex logs on cold start, and produce concise manager summaries for OpenClaw and adjacent project work.
$ npx -y skills add vincentkoc/dotskills --skill tmux-lane-orchestrator --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/tmux-lane-orchestratorContext preview
The summary Claude sees to decide when to auto-load this skill.
Manage one tmux agent lane from its matching ops pane, inspect live pane state and Codex logs on cold start, and produce concise manager summaries for OpenClaw and adjacent project work.
name: tmux-lane-orchestrator description: Manage one tmux agent lane from its matching ops pane, inspect live pane state and Codex logs on cold start, and produce concise manager summaries for OpenClaw and adjacent project work. license: MIT compatibility: Requires tmux. Codex log inspection expects local session logs under ~/.codex/sessions. metadata: source: "https://github.com/vincentkoc/dotskills" version: "0.1.8" spec: agentskills-v1
Act as the manager for one tmux worker lane. Keep visibility over the lane's panes, infer what each worker is doing from tmux and Codex logs, surface blockers, and give the operator short status summaries with concrete next actions.
The manager owns only its lane by default. Current cockpit topology uses odd `ops` panes as lane managers:
Treat even-numbered `ops` panes as spare shells unless the operator explicitly assigns them. Do not inspect or steer another lane unless the operator expands scope.
Read `references/factory-model.md` when setting up or revising lane responsibilities. Read `references/openclaw-lane-matrix.md` for OpenClaw-specific worker interpretation.
1. Set the manager pane title immediately:
2. Infer manager scope:
3. Run the cold-start snapshot:
4. Read the operator-provided lane map and treat it as the mission source of truth. If IDs are duplicated or missing, call that out as a warning instead of silently rewriting it. 5. For each worker pane, classify state:
6. Cross-check live panes against Codex logs before summarizing. Pane titles alone are weak evidence; logs often contain the real task, branch, Testbox id, run id, and latest failure. 7. If the snapshot helper is slow or stale, do not wait on it forever. Fall back to direct `tmux capture-pane`, focused `jq` reads of recent `~/.codex/sessions/YYYY/MM/DD/*.jsonl`, and live GitHub/Testbox checks for the panes that matter. 8. Summarize in manager style:
9. When asked to intervene, prefer low-risk coordination first:
10. When launching workers into panes:
11. When assigning OpenClaw work to a fresh worker, create or verify the requested `gwt` worktree first. The worker prompt must name the exact worktree path, forbid touching the main checkout, forbid `pnpm install` inside Codex worktrees, require `node_modules` symlink verification, and route broad validation through Testbox. 12. When launching a review-first worker:
We are moving from "prompt as text" to skill as runtime module. Each skill acts like a lightweight, containerized application for AI work: a stable interface, opinionated workflow, and bundled resources that can be installed, versioned, tested, and reused
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