Frappe Impl Scheduler logo

Frappe Impl Scheduler

Organization
Impertio-Studio
frappe-impl-scheduler

Use when implementing scheduled tasks and background jobs in Frappe v14/v15/v16. Covers hooks.py scheduler_events, frappe.enqueue, queue selection, job deduplication, testing with bench execute/scheduler, monitoring via Scheduled Job Log and RQ Dashboard, error handling, long-running job patterns, email digest, data cleanup, and report generation. Keywords: schedule task, background job, cron job, async processing, queue selection, job deduplication, scheduler implementation, run task automatically, background process, scheduled task not running, async task.

Overview

PublisherImpertio-Studio
RepositoryFrappe_Claude_Skill_Package
Skill namefrappe-impl-scheduler
Stars
180
Forks
53
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Impertio-Studio on GitHub. Read the source before you install it.

Installation

Install the Frappe Impl Scheduler AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Impertio-Studio/Frappe_Claude_Skill_Package.git /tmp/Frappe_Claude_Skill_Package
mkdir -p .claude/skills
cp -r /tmp/Frappe_Claude_Skill_Package/skills/source/impl/frappe-impl-scheduler .claude/skills/frappe-impl-scheduler
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Frappe Impl Scheduler in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Frappe Impl Scheduler on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Frappe Impl Scheduler is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Frappe Scheduler & Background Jobs - Implementation

Workflow for implementing scheduled tasks and background jobs. For exact syntax, see frappe-syntax-scheduler.

Version: v14/v15/v16 compatible


Main Decision: scheduler_events vs frappe.enqueue

WHAT ARE YOU BUILDING?
|
+-- Runs at fixed intervals/times?
|   +-- YES --> scheduler_events (hooks.py)
|   |           Task receives NO arguments
|   |           See: Workflow 1-2
|   |
|   +-- NO --> Triggered by user action or code?
|              +-- YES --> frappe.enqueue()
|              |           Pass any serializable data
|              |           See: Workflow 3-4
|              |
|              +-- NO --> Reconsider requirements
Aspectscheduler_eventsfrappe.enqueue
Triggered byTime/intervalCode execution
Defined inhooks.pyPython code
ArgumentsNONE (must be parameterless)Any serializable data
Use caseDaily cleanup, hourly syncUser-triggered long task
Queue controlEvent suffix (_long)queue= parameter
Restart behaviorRuns on scheduleLost if worker restarts

Which Scheduler Event Type?

NeedEvent KeyQueue
Every scheduler tickallshort (NEVER >60s)
Hourly (<5 min)hourlyshort
Hourly (5-25 min)hourly_longlong
Daily (<5 min)dailyshort
Daily (5-25 min)daily_longlong
Weekly (<5 min)weeklyshort
Weekly (5-25 min)weekly_longlong
Monthly (<5 min)monthlyshort
Monthly (5-25 min)monthly_longlong
Custom schedulecron["expr"]short

Rule: ALWAYS use *_long suffix for tasks exceeding 5 minutes.


Which Queue for frappe.enqueue?

QueueDefault TimeoutUse For
short300s (5 min)Quick operations (<1 min)
default300s (5 min)Standard tasks (1-5 min)
long1500s (25 min)Heavy processing (>5 min)

Rule: ALWAYS specify queue= explicitly. NEVER rely on the default.


Implementation Step 1: Scheduler Event

python
# myapp/tasks.py
import frappe

def daily_cleanup():
    """Daily cleanup - NO parameters allowed."""
    cutoff = frappe.utils.add_days(frappe.utils.nowdate(), -30)
    frappe.db.delete("Error Log", {"creation": ("<", cutoff)})
    frappe.db.commit()
python
# hooks.py
scheduler_events = {
    "daily": ["myapp.tasks.daily_cleanup"]
}

After editing hooks.py: ALWAYS run bench migrate.


Implementation Step 2: Background Job (frappe.enqueue)

python
# myapp/api.py
import frappe
from frappe.utils.background_jobs import is_job_enqueued

@frappe.whitelist()
def process_documents(doctype, filters):
    job_id = f"process_{doctype}_{frappe.session.user}"

    if is_job_enqueued(job_id):
        return {"message": "Already in progress"}

    frappe.enqueue(
        "myapp.tasks.process_batch",
        queue="long",
        timeout=1800,
        job_id=job_id,
        enqueue_after_commit=True,
        doctype=doctype,
        filters=filters
    )
    return {"status": "queued"}

Testing Scheduled Tasks

Method 1: bench execute (direct)

bash
# Run the function directly (no queue involved)
bench --site mysite execute myapp.tasks.daily_cleanup

Method 2: bench scheduler (full scheduler test)

bash
# Check scheduler status
bench --site mysite scheduler status

# Enable scheduler
bench --site mysite scheduler enable

# Trigger all pending scheduler events NOW
bench --site mysite scheduler trigger

# Run specific event type
bench --site mysite execute frappe.utils.scheduler.trigger --args "['daily']"

Method 3: bench console (interactive)

python
bench --site mysite console
>>> frappe.enqueue("myapp.tasks.my_task", queue="short", now=True)
# now=True executes synchronously for testing

Method 4: Check Scheduled Job Type

1. Go to: Setup > Scheduled Job Type
2. Find: myapp.tasks.daily_cleanup
3. Verify: Frequency correct, Stopped = No
4. Click "Run Now" to trigger manually

Monitoring

Scheduled Job Log (UI)

Setup > Scheduled Job Log
- Shows every scheduler run with status
- Filter by: status (Success/Failed), creation date
- Check execution time to detect slow tasks

RQ Dashboard

bash
# Start RQ monitor (development)
bench --site mysite rq-dashboard
# Opens at http://localhost:9181

# Show background job status
bench --site mysite show-pending-jobs
bench --site mysite show-failed-jobs

Programmatic Health Check

python
def scheduler_health_check():
    failed = frappe.db.count("Scheduled Job Log", {
        "status": "Failed",
        "creation": [">=", frappe.utils.add_to_date(None, hours=-1)]
    })
    if failed > 5:
        frappe.sendmail(
            recipients=["admin@example.com"],
            subject="Scheduler Alert: Many failures",
            message=f"{failed} scheduler jobs failed in last hour"
        )

Error Handling in Scheduled Tasks

Per-Record Error Isolation

python
def sync_all_orders():
    orders = get_pending_orders()
    success, errors = 0, 0

    for order in orders:
        try:
            sync_to_external(order)
            success += 1
        except Exception as e:
            errors += 1
            frappe.db.rollback()
            frappe.log_error(
                f"Sync failed for {order}: {e}",
                "Order Sync Error"
            )
    frappe.db.commit()
    frappe.logger("sync").info(f"{success} ok, {errors} errors")

Rule: ALWAYS wrap per-record processing in try-except. NEVER let one failure stop the entire batch.


Long-Running Job Patterns

Self-Chaining Pattern (>25 min tasks)

python
def process_batch(offset=0, batch_size=500, total=None):
    if total is None:
        total = frappe.db.count("Sales Invoice", {"custom_processed": 0})

    records = frappe.get_all("Sales Invoice",
        filters={"custom_processed": 0},
        pluck="name", limit=batch_size)

    if not records:
        return  # Done

    for name in records:
        process_single(name)
    frappe.db.commit()

    remaining = frappe.db.count("Sales Invoice", {"custom_processed": 0})
    if remaining > 0:
        frappe.enqueue(
            "myapp.tasks.process_batch",
            queue="long",
            offset=offset + batch_size,
            batch_size=batch_size,
            total=total
        )

Rule: ALWAYS split tasks >25 min into self-chaining batches.


Common Implementation Patterns

Email Digest (weekly summary)

python
# hooks.py
scheduler_events = {
    "cron": {
        "0 8 * * 1": ["myapp.newsletter.send_weekly_digest"]
    }
}

See references/examples.md Example 4 for complete implementation.

Data Cleanup (daily maintenance)

python
scheduler_events = {
    "daily_long": ["myapp.maintenance.daily_database_maintenance"]
}

See references/examples.md Example 1 for batch deletion pattern.

Report Generation (user-triggered)

python
frappe.enqueue(
    "myapp.tasks.generate_report",
    queue="long",
    timeout=3600,
    job_id=f"report::{frappe.session.user}",
    user=frappe.session.user
)

See references/workflows.md Workflow 6 for progress reporting.


Critical Rules

  1. Scheduler tasks receive NO arguments - Use settings or hardcoded values
  2. ALWAYS bench migrate after hooks.py changes - Required to register events
  3. Jobs run as Administrator - ALWAYS commit explicitly
  4. Commit in batches - NEVER per-record (every 100-500 records)
  5. ALWAYS use job_id for user-triggered jobs - Prevents duplicates
  6. Use enqueue_after_commit=True from document events - Ensures data exists
  7. Scheduler events should be thin - Enqueue heavy work to background

Version Differences

Aspectv14v15v16
Tick interval240s60s60s
Job dedup paramjob_namejob_idjob_id
enqueue_doc()YesYesYes
Custom queuesNoYesYes

Reference Files

FileContents
workflows.md8 step-by-step implementation patterns
decision-tree.mdDetailed decision flowcharts
examples.md5 complete working examples
anti-patterns.md14 common mistakes to avoid

See Also

  • frappe-syntax-scheduler - Exact syntax reference for hooks and enqueue
  • frappe-errors-serverscripts - Error handling patterns
  • frappe-impl-hooks - Hook configuration patterns
  • frappe-ops-bench - Bench commands for scheduler management
  • frappe-ops-performance - Performance tuning for background jobs
  • frappe-testing-unit - Testing scheduled task logic

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Frappe Impl Scheduler AI skill do?

Use when implementing scheduled tasks and background jobs in Frappe v14/v15/v16. Covers hooks.py scheduler_events, frappe.enqueue, queue selection, job deduplication, testing with bench execute/scheduler, monitoring via Scheduled Job Log and RQ Dashboard, error handling, long-running job patterns, email digest, data cleanup, and report generation. Keywords: schedule task, background job, cron job, async processing, queue selection, job deduplication, scheduler implementation, run task automatically, background process, scheduled task not running, async task.

Why use Frappe Impl Scheduler on TypingMind?

Because you install it once and use it with any model. Frappe Impl Scheduler is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Frappe Impl Scheduler in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Impertio-Studio/Frappe_Claude_Skill_Package/tree/main/skills/source/impl/frappe-impl-scheduler. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Frappe Impl Scheduler?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Frappe Impl Scheduler?

As many as you like. As long as a model supports skills, you can use Frappe Impl Scheduler with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Frappe Impl Scheduler AI skill free?

Yes. It is published on GitHub by Impertio-Studio under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇