Agent skill

Schedule

by asgeirtj in asgeirtj/system_prompts_leaks

Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule.

CC0-1.0Auto-check passedProductivity & Automation

Install Schedule

skills CLI
$ npx skills add asgeirtj/system_prompts_leaks --skill schedule -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install asgeirtj/system_prompts_leaks schedule --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/asgeirtj/system_prompts_leaks.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Anthropic/claude-code/skills/schedule .claude/skills/schedule && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
schedule
GitHub stars
69k
Token cost
~2.9k tokens
SKILL.md length
1,441 words
Files
1
Skills in repo
128
Repo updated
First seen
Licence
CC0-1.0

At a glance

Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule.

  • Works in 7 steps: Understand the goal — Ask what they want… → Craft the prompt — Help them write an… → Set the schedule — Ask when and how… → …
  • Tasks that involve Scheduled and recurring tasks
  • SKILL.md covers First Step, What You Can Do, Create body shape and Available MCP Connectors, plus 4 more sections
  • Reaches claude.ai and github.com

What it does

Schedule is an agent skill from asgeirtj/system_prompts_leaks. Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Productivity & Automation, covering Scheduled and recurring tasks. The repository describes itself as: Documented system prompts from Anthropic - Claude Fable 5.1, Opus 5.5, Claude Design, Claude Code. OpenAI - ChatGPT GPT-6-Astra, Codex. Google - Gemini 3.8 Flash, 3.1 Pro… The licence is CC0-1.0.

When your agent uses it

  • Tasks that involve Scheduled and recurring tasks

Example prompts

  • “/schedule”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Understand the goal — Ask what they want the cloud agent to do. What repo(s)? What task? Remind them that the agent runs in the cloud — it…
  2. Craft the prompt — Help them write an effective agent prompt. Good prompts are
  3. Set the schedule — Ask when and how often. The user's timezone is Atlantic/Reykjavik. When they say a time (e.g., "every morning at 9am")…
  4. Choose the model — Default to claude-sonnet-5-5. Tell the user which model you're defaulting to and ask if they want a different one.
  5. Validate connections — Infer what services the agent will need from the user's description. For example, if they say "check Datadog and…
  6. Review and confirm — Show the full configuration before creating. Let them adjust.
  7. Create it — Call RemoteTrigger with action: "create" and show the result. The response includes the routine ID. Always output a link at…

What it can do on your machine

Read from SKILL.md and the folder at commit 60d44cc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • claude.ai
    • github.com

    Also links to:

    • api.anthropic.com
    • calendarmcp.googleapis.com
    • drivemcp.googleapis.com
    • econ-index.mcp.claude.com
    • gmailmcp.googleapis.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Schedule loads about 2.9k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 1,441 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgeirtj/system_prompts_leaks at commit 60d44cc, republished under its CC0-1.0 licence (© asgeirtj). 1,441 words, ~2,911 tokens.

Download SKILL.mdSave it as .claude/skills/schedule/SKILL.md (or your agent's skills folder).
name
schedule
description
Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule.
when_to_use
When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines…

Schedule Cloud Agents

You are helping the user schedule, update, list, or run cloud Claude Code agents. These are NOT local cron jobs — each routine spawns a fully isolated cloud session (CCR) in Anthropic's cloud infrastructure, either on a recurring cron schedule or once at a specific time. The agent runs in a sandboxed environment with its own git checkout, tools, and optional MCP connections.

First Step

Your FIRST action must be a single AskUserQuestion tool call (no preamble). Use this EXACT string for the question field — do not paraphrase or shorten it:

"What would you like to do with scheduled cloud agents?"

Set header: "Action" and offer the four actions (create/list/update/run) as options. After the user picks, follow the matching workflow below.

What You Can Do

Use the RemoteTrigger tool (load it first with ToolSearch select:RemoteTrigger; auth is handled in-process — do not use curl):

  • {action: "list"} — list all routines
  • {action: "get", trigger_id: "..."} — fetch one routine
  • {action: "create", body: {...}} — create a routine
  • {action: "update", trigger_id: "...", body: {...}} — partial update
  • {action: "run", trigger_id: "..."} — run a routine now
  • {action: "list_runs", trigger_id: "..."} — the routine's recent run sessions, most recently active first
  • {action: "get_run_log", session_id: "..."} — condensed log of one run (provisioning, tool calls and errors, permission denials, API retries, final result)

To debug a routine that misbehaved, call list_runs and then get_run_log on the run in question. A fire that was skipped or refused before a session existed (routine paused, a fire cap, a kill switch) or that failed its pre-creation checks (repository access, environment) leaves no run in list_runs, and a routine that posts into an existing session adds to that session rather than a new run; when the list is empty or short, check the routine itself with get rather than concluding it never fired.

(Note: the API uses trigger_id as the parameter name, but the user-facing term is "routine".)

You CANNOT delete routines. If the user asks to delete, direct them to: https://claude.ai/code/routines

Create body shape

For a recurring schedule:

json
{
  "name": "AGENT_NAME",
  "cron_expression": "CRON_EXPR",
  "enabled": true,
  "job_config": {
    "ccr": {
      "environment_id": "ENVIRONMENT_ID",
      "session_context": {
        "model": "claude-sonnet-5-5",
        "sources": [
          {"git_repository": {"url": "https://github.com/acme-corp/acme-app"}}
        ],
        "allowed_tools": ["Bash", "Read", "Write", "Edit", "Glob", "Grep"]
      },
      "events": [
        {"data": {
          "uuid": "<lowercase v4 uuid>",
          "session_id": "",
          "type": "user",
          "parent_tool_use_id": null,
          "message": {"content": "PROMPT_HERE", "role": "user"}
        }}
      ]
    }
  }
}

For a one-time run, replace "cron_expression": "CRON_EXPR" with "run_once_at": "YYYY-MM-DDTHH:MM:SSZ" (RFC3339 UTC, must be in the future). Everything else is identical.

Generate a fresh lowercase UUID for events[].data.uuid yourself.

Every events[].data.message must be the API message shape {"role": "user", "content": "..."} — the role field is required, never omit it. If you instead write the body in the session_request form that list and get return, the same rule applies to session_request.events[].payload.message.

Available MCP Connectors

These are the user's currently connected claude.ai MCP connectors:

Available connectors (usable by routines):

When attaching connectors to a routine, use the connector_uuid and name shown above (the name is already sanitized to only contain letters, numbers, hyphens, and underscores), and the connector's URL. The name field in mcp_connections must only contain [a-zA-Z0-9_-] — dots and spaces are NOT allowed.

Important: Infer what services the agent needs from the user's description. For example, if they say "check Datadog and Slack me errors," the agent needs both Datadog and Slack connectors. Cross-reference against the list above and warn if any required service isn't connected. If a needed connector is missing, direct the user to https://claude.ai/customize/connectors to connect it first.

Environments

Every routine requires an environment_id in the job config. This determines where the cloud agent runs. Ask the user which environment to use.

Available environments:

  • Default (id: env_011T6SpReYrGRXyHMALmund8, kind: anthropic_cloud)

Use the id value as the environment_id in job_config.ccr.environment_id.

API Field Reference

Create Routine — Required Fields
  • name (string) — A descriptive name
  • Exactly ONE of:
    • cron_expression (string) — 5-field cron in UTC. Minimum interval is 1 hour.
    • run_once_at (string) — RFC3339 UTC timestamp. Must be in the future. Fires once, then auto-disables.
  • job_config (object) — Session configuration (see structure above)
Create Routine — Optional Fields
  • enabled (boolean, default: true)
  • mcp_connections (array) — MCP servers to attach:
    json
    [{"connector_uuid": "uuid", "name": "server-name", "url": "https://..."}]
Update Routine — Optional Fields

All fields optional (partial update):

  • name, cron_expression, run_once_at, enabled, job_config
  • mcp_connections — Replace MCP connections
  • clear_mcp_connections (boolean) — Remove all MCP connections
Cron Expression Examples

The user's local timezone is Atlantic/Reykjavik. Cron expressions and run_once_at timestamps are always in UTC. When the user says a local time, convert it to UTC but confirm with them: "9am Atlantic/Reykjavik = Xam UTC, so the cron would be 0 X * * 1-5." For one-time runs, the same conversion applies — "run this at 3pm" → "run_once_at": "YYYY-MM-DDTHH:00:00Z" with their 3pm converted to UTC.

  • 0 9 * * 1-5 — Every weekday at 9am UTC
  • 0 */2 * * * — Every 2 hours
  • 0 0 * * * — Daily at midnight UTC
  • 30 14 * * 1 — Every Monday at 2:30pm UTC
  • 0 8 1 * * — First of every month at 8am UTC

Minimum interval is 1 hour. */30 * * * * will be rejected.

Current Time (for one-off runs)

When /schedule was invoked it was Thu, Oct 8, 2026 at 06:52 AM (Atlantic/Reykjavik) / 2026-10-08T06:52:46.000Z UTC. Treat this as an approximate anchor only — the conversation may have been running for a while since then.

Before computing any run_once_at value, you MUST re-check the current time by running date -u +%Y-%m-%dT%H:%M:%SZ via the Bash tool. Do not guess or infer today's date from conversation context. Resolve relative requests ("tomorrow at 9am", "in 3 hours", "next Monday") against the freshly fetched time, then echo the resolved local time AND the UTC timestamp back to the user for confirmation before creating the routine. If the resolved time is already in the past, ask the user to clarify rather than silently rolling forward.

Show full SKILL.md (530 more words)Show less

Workflow

CREATE a new routine:
  1. Understand the goal — Ask what they want the cloud agent to do. What repo(s)? What task? Remind them that the agent runs in the cloud — it won't have access to their local machine, local files, or local environment variables.
  2. Craft the prompt — Help them write an effective agent prompt. Good prompts are:
    • Specific about what to do and what success looks like
    • Clear about which files/areas to focus on
    • Explicit about what actions to take (open PRs, commit, just analyze, etc.)
  3. Set the schedule — Ask when and how often. The user's timezone is Atlantic/Reykjavik. When they say a time (e.g., "every morning at 9am"), assume they mean their local time and convert to UTC for the cron expression. Always confirm the conversion: "9am Atlantic/Reykjavik = Xam UTC." If they want a one-time run (e.g., "once at 3pm", "tomorrow morning", "remind me to check X later"), use run_once_at instead of cron_expression — same timezone conversion applies. First re-check the current time with date -u via Bash (the reference time above may be stale in a long conversation), resolve the relative phrase against that fresh value, and confirm the resulting absolute timestamp with the user.
  4. Choose the model — Default to claude-sonnet-5-5. Tell the user which model you're defaulting to and ask if they want a different one.
  5. Validate connections — Infer what services the agent will need from the user's description. For example, if they say "check Datadog and Slack me errors," the agent needs both Datadog and Slack MCP connectors. Cross-reference with the connectors list above. If any are missing, warn the user and link them to https://claude.ai/customize/connectors to connect first. The default git repo is already set to https://github.com/acme-corp/acme-app. Ask the user if this is the right repo or if they need a different one.
  6. Review and confirm — Show the full configuration before creating. Let them adjust.
  7. Create it — Call RemoteTrigger with action: "create" and show the result. The response includes the routine ID. Always output a link at the end: https://claude.ai/code/routines/{ROUTINE_ID}
UPDATE a routine:
  1. List routines first so they can pick one
  2. Ask what they want to change
  3. Show current vs proposed value
  4. Confirm and update
LIST routines:
  1. Fetch and display in a readable format
  2. Show: name, schedule (human-readable), enabled/disabled, next run, repo(s)
RUN NOW:
  1. List routines if they haven't specified which one
  2. Confirm which routine
  3. Execute and confirm

Important Notes

  • These are CLOUD agents — they run in Anthropic's cloud, not on the user's machine. They cannot access local files, local services, or local environment variables.
  • Always convert cron to human-readable when displaying
  • When listing routines, ended_reason: "run_once_fired" means a one-shot already ran (shows as "Ran" in the web UI). The user can re-arm it by updating with a new run_once_at.
  • Default to enabled: true unless user says otherwise
  • Accept GitHub URLs in any format (https://github.com/org/repo, org/repo, etc.) and normalize to the full HTTPS URL (without .git suffix)
  • The prompt is the most important part — spend time getting it right. The cloud agent starts with zero context, so the prompt must be self-contained.
  • To delete a routine, direct users to https://claude.ai/code/routines

© asgeirtj, CC0-1.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in Anthropic/claude-code/skills/schedule of asgeirtj/system_prompts_leaks.

Open the folder on GitHubat commit 60d44cc

Compare with similar skills

Schedule next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Schedule compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Schedule this skillasgeirtj/system_prompts_leaks69k—~2.9kAutomated safety check: PassCC0-1.0
ScheduleTinyAGI/tinyagi3.6k—~1.4kAutomated safety check: PassMIT
Send User MessageTinyAGI/tinyagi3.6k—~829Automated safety check: PassMIT
Cron Opsczl9707/build-your-own-openclaw1.9k—~593Automated safety check: PassMIT
X Bookmarkssharbelxyz/x-bookmarks289—~2kAutomated safety check: NotesNone
Wp Wpcli And OpsAutomattic/agent-skills2112 repos~988Automated safety check: PassNone

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Questions about Schedule

What does Schedule do?

Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. Schedule is an agent skill from asgeirtj/system_prompts_leaks. Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule.

When should I use Schedule?

Schedule fits situations like: tasks that involve Scheduled and recurring tasks.

How do I install Schedule in Claude Code?

Run `npx skills add asgeirtj/system_prompts_leaks --skill schedule -a claude-code`. Or copy the skill folder (Anthropic/claude-code/skills/schedule in asgeirtj/system_prompts_leaks) into .claude/skills/schedule in your project. Claude Code loads it when a task matches its description.

How do I install Schedule in Codex?

Run `npx skills add asgeirtj/system_prompts_leaks --skill schedule -a codex`. Or copy the skill folder (Anthropic/claude-code/skills/schedule in asgeirtj/system_prompts_leaks) into .agents/skills/schedule in your project. Codex loads it when a task matches its description.

Can I use Schedule in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgeirtj/system_prompts_leaks --skill schedule -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/schedule, .gemini/skills/schedule, .github/skills/schedule and .opencode/skills/schedule in your project.

What does Schedule need to run?

SKILL.md names no scripts, command-line tools or credentials: Schedule is instructions for the agent only.

Does Schedule access the network?

SKILL.md names 7 domains. In commands or code: claude.ai and github.com; the agent is likely to contact these when it follows the instructions. As links in the text: api.anthropic.com, calendarmcp.googleapis.com, drivemcp.googleapis.com, econ-index.mcp.claude.com and gmailmcp.googleapis.com. This is read from the text; nothing was executed.

Is Schedule safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Schedule use?

Schedule is published under the CC0-1.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Schedule use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Schedule?

Skills that share tags, products or a category with Schedule: Schedule (TinyAGI/tinyagi, 3.6k stars), Send User Message (TinyAGI/tinyagi, 3.6k stars), Cron Ops (czl9707/build-your-own-openclaw, 1.9k stars) and X Bookmarks (sharbelxyz/x-bookmarks, 289 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Schedule?

asgeirtj (a GitHub user) maintains it in asgeirtj/system_prompts_leaks, which has 69,280 GitHub stars. The repository holds 128 skills in this directory. The repository was last updated on October 10, 2026.

Source: asgeirtj/system_prompts_leaks on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.