routines
Use for a recurring unattended task (cron; pull data, open a PR, post a digest), or to test/dry-run a routine first. Explains authoring and testing via the routine tool.
- License 许可
- Apache-2.0
- 最近更新
- 2026-11-11
- Stars
- ★ 1.1w
- Forks
- 3.0k
01 前言 Front Matter
- name
- routines
- description
- Use for a recurring unattended task (cron; pull data, open a PR, post a digest), or to test/dry-run a routine first. Explains authoring and testing via the routine tool.
03 SKILL.md 正文
Scheduling routines
A routine is a recurring task that runs unattended on a cron schedule, in a fresh sandbox isolated from the user's workspace, holding only the secrets the routine declares. Use it for "every morning, summarize X and report" / "weekly, bump deps and open a PR" — work that should happen without the user present. There is no one-off reminder tool: it cannot ask the user anything mid-run, so it is the wrong fit for a plain "remind me at 9pm" ping.
The isolation model (why this is safe)
- The routine runs in its OWN sandbox (
lhart-<id>), never the user's — so it cannot see their workspace, other projects, or undeclared secrets. - It receives ONLY the secret env vars listed in
secrets:— that list IS the blast-radius boundary. Declare the minimum the task needs (check the "Available secret env vars" line for the names). - It is headless (nobody to approve mid-run): shell commands run normally in
the isolated sandbox —
git clone,pip install, build,commit/pushall work, since the sandbox is the blast radius. Secret-exfiltration and known-destructive commands are still refused, and any non-shell action that would need approval is auto-denied — design the task to avoid those, or do an irreversible external step as a draft for the user to confirm later. - Outputs come back as artifacts in the routine's session — it cannot write into the user's workspace.
How to author one
Call routine(action="create", name=..., schedule=..., task=..., secrets=[...]):
schedule: a 5-field cron expression ("0 8 * * *"= daily 08:00 UTC).task: a self-contained instruction — the routine has no memory of this chat and no access to the user's files, so state everything it needs.secrets: the exact secret env names the task requires (e.g.["GITHUB_PAT"]), or omit for none.
Test it before scheduling
ALWAYS dry-run a routine before you create it:
routine(action="test", name=..., task=..., secrets=[...])
This runs the task ONCE right now under the routine's real isolation — its own
lhart- sandbox, headless (shell runs; non-shell approvals denied), only the declared secrets —
and blocks until it finishes, returning {success, status, output, ...}. Read the
output: if the task didn't work (missing secret, needed an approval, wrong
assumption), fix task/secrets and test again. A test schedules nothing (no
schedule row, no chat session) — but it does provision the routine's real
lhart- sandbox, which the scheduled routine then reuses, so pass the same name
you'll create with. Then create.
Scheduling it
The tool asks the user to confirm before scheduling. On the first call it
returns status: "awaiting_user_response" — stop, let the user decide, and do
NOT claim the routine is scheduled until you get success: true with an id.
Use routine(action="list") to show scheduled routines and
routine(action="cancel", id=...) to remove one (the user can also type
/routines / /routines remove <id>).
04 同仓库更多 Skills
retail-virtual-tryon
Creates virtual try-on agents supporting image and video (catwalk animation) try-ons on Google Cloud (Gemini image models and Veo on Gemini Enterprise Agent Platform). Handles resource setup, user photo uploading, image/video generation pipelines, local testing, and evaluation.
retail-product-search
Creates product search agents with semantic search and RAG on Google Cloud (Vector Search on Gemini Enterprise Agent Platform, BigQuery, embeddings). Use when the user wants to "build a product search agent", "create an e-commerce search", "make a shopping assistant", "set up semantic catalog discovery", "ingest products into Vector Search", or "deploy a retail RAG agent". Handles the full pipeline: catalog data ingestion to BigQuery, Gemini Enterprise Agent Platform Vector Search collection setup, ADK agent scaffolding, evaluation, and Cloud Run deployment.
policy
Inspect the per-workspace tool-policy overlay (.lha/policies.jsonl) that gates destructive or sensitive tool calls, and tell the user what to change if they want a rule added or removed.
google-workspace
Read/write Google Drive, Docs, Sheets, Gmail, Calendar, Chat, Tasks, Slides via the `gws` CLI. Needs an OAuth client or service account configured first. Use for any Google Workspace task.
find-skills
Discover/install NEW agent skills from external sources (skills.sh). Use for capabilities you lack, e.g. "find a skill for X". Do NOT use to list skills you already have; see `<available_skills>`.
bootstrap-google-tools
Install/auth CLIs the sandbox lacks - `gws` (Drive, Gmail, Sheets, Calendar), `gcloud`, `agents-cli`, `mcp-cli` (MCP servers). Use on "command not found" or before GCP/Workspace/MCP work.