Agent skill

Prefect

by Kilo-Org in Kilo-Org/kilo-marketplace

Prefect is a modern workflow orchestration framework for Python data pipelines.

Apache-2.0Auto-check passedData & Analytics

Install Prefect

skills CLI
$ npx skills add Kilo-Org/kilo-marketplace --skill prefect -a claude-code

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

GitHub CLI
$ gh skill install Kilo-Org/kilo-marketplace prefect --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/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/prefect .claude/skills/prefect && 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
prefect
GitHub stars
189
Used in
1 other repo
Token cost
~1.4k tokens
SKILL.md length
42 words
Files
3
Skills in repo
87
Repo updated
First seen
Licence
Apache-2.0

At a glance

Prefect is a modern workflow orchestration framework for Python data pipelines.

  • Tasks that involve Deployment
  • SKILL.md covers Installation, Basic Flow, Scheduling and Deployments and Error Handling and Concurrency, plus 3 more sections
  • Calls prefect and pip
  • Tasks that involve Data pipelines and ETL

What it does

Prefect is an agent skill from Kilo-Org/kilo-marketplace. Prefect is a modern workflow orchestration framework for Python data pipelines. Learn to define flows and tasks with decorators, handle retries and scheduling, create deployments, and monitor via the Prefect UI.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `_scores.json`). Compatibility notes: macos, linux, windows

It sits in Data & Analytics, covering Deployment and Data pipelines and ETL. It works with Python. The repository describes itself as: Kilo Marketplace - A curated collection of Skills, MCP Servers, and Modes for enhancing AI agent capabilities across the Kilo ecosystem—including Kilo Code (VS Code extension)… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Deployment
  • Tasks that involve Data pipelines and ETL

Example prompts

  • “/prefect”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): macos, linux, windows

What it can do on your machine

Read from SKILL.md and the folder at commit ff51758. 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

    Shell commands in SKILL.md call:

    • prefect
    • pip

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

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    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.

  • Compatibility

    macos, linux, windows

    From compatibility in the SKILL.md frontmatter.

Context cost

Prefect loads about 1.4k tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 42 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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 Kilo-Org/kilo-marketplace at commit ff51758, republished under its Apache-2.0 licence (© Kilo-Org). 42 words, ~1,369 tokens.

Download SKILL.mdSave it as .claude/skills/prefect/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
prefect
description
Prefect is a modern workflow orchestration framework for Python data pipelines. Learn to define flows and tasks with decorators, handle retries and scheduling, create deployments, and monitor via the Prefect UI.
compatibility
macos, linux, windows
metadata.author
terminal-skills
metadata.version
1.0.0
metadata.category
data
metadata.tags
prefect, workflow-orchestration, python, data-pipeline, scheduling

Prefect

Prefect turns Python functions into observable, schedulable workflows with minimal boilerplate. Add @flow and @task decorators to get retries, logging, caching, and a monitoring UI.

Installation

bash
# Install Prefect
pip install prefect

# Start the local Prefect server (UI + API)
prefect server start
# UI at http://localhost:4200

# Or use Prefect Cloud (managed)
prefect cloud login

Basic Flow

python
# flows/hello.py: Simple flow with tasks
from prefect import flow, task, get_run_logger
from datetime import timedelta

@task(retries=3, retry_delay_seconds=10)
def fetch_data(url: str) -> dict:
    import httpx
    logger = get_run_logger()
    logger.info(f"Fetching {url}")
    response = httpx.get(url)
    response.raise_for_status()
    return response.json()

@task(cache_expiration=timedelta(hours=1))
def transform(data: dict) -> list:
    return [
        {"id": item["id"], "value": item["amount"] * 100}
        for item in data["results"]
    ]

@task
def load(records: list) -> int:
    logger = get_run_logger()
    logger.info(f"Loading {len(records)} records")
    # Insert into database...
    return len(records)

@flow(name="etl-pipeline", log_prints=True)
def etl_pipeline(api_url: str = "https://api.example.com/data"):
    raw = fetch_data(api_url)
    cleaned = transform(raw)
    count = load(cleaned)
    print(f"Processed {count} records")
    return count

if __name__ == "__main__":
    etl_pipeline()

Scheduling and Deployments

python
# flows/deploy.py: Create a deployment with schedule
from prefect import flow
from prefect.deployments import Deployment
from prefect.server.schemas.schedules import CronSchedule

@flow
def daily_report():
    print("Generating daily report...")

if __name__ == "__main__":
    # Deploy via Python
    daily_report.serve(
        name="daily-report-deployment",
        cron="0 8 * * *",  # Every day at 8 AM
        tags=["reporting"],
        parameters={"param1": "value1"},
    )
bash
# deploy.sh: Deploy and manage via CLI
# Create deployment from flow file
prefect deploy flows/hello.py:etl_pipeline \
  --name etl-prod \
  --pool default-agent-pool \
  --cron "*/30 * * * *"

# Start a worker to execute deployments
prefect worker start --pool default-agent-pool

# Trigger a deployment run
prefect deployment run "etl-pipeline/etl-prod" --param api_url=https://api.example.com

Error Handling and Concurrency

python
# flows/advanced.py: Concurrent tasks, error handling, and sub-flows
from prefect import flow, task
from prefect.tasks import task_input_hash
import asyncio

@task(
    retries=2,
    retry_delay_seconds=[10, 60],  # Exponential backoff
    cache_key_fn=task_input_hash,
    timeout_seconds=300,
)
def process_item(item_id: int) -> dict:
    # Process a single item
    return {"id": item_id, "status": "done"}

@flow
def batch_process(item_ids: list[int]):
    # Submit tasks concurrently
    futures = [process_item.submit(id) for id in item_ids]
    results = [f.result() for f in futures]

    succeeded = [r for r in results if r["status"] == "done"]
    print(f"Processed {len(succeeded)}/{len(item_ids)} items")

@flow
async def async_pipeline():
    # Async flow for I/O-bound work
    results = await asyncio.gather(
        fetch_from_api("source_a"),
        fetch_from_api("source_b"),
    )
    return results

Blocks and Infrastructure

python
# flows/blocks.py: Use blocks for reusable configuration
from prefect.blocks.system import Secret, JSON
from prefect_sqlalchemy import SqlAlchemyConnector

# Store secrets (set via UI or CLI)
# prefect block register -m prefect_sqlalchemy
# Then configure in UI at http://localhost:4200/blocks

# Use in flows
@flow
def db_flow():
    api_key = Secret.load("my-api-key").get()
    config = JSON.load("pipeline-config").value

    with SqlAlchemyConnector.load("prod-db") as conn:
        result = conn.fetch_all("SELECT count(*) FROM users")
        print(result)

Notifications

python
# flows/notifications.py: Send alerts on failure
from prefect import flow
from prefect.blocks.notifications import SlackWebhook

@flow
def monitored_flow():
    try:
        # ... do work
        pass
    except Exception as e:
        slack = SlackWebhook.load("alerts-channel")
        slack.notify(f"❌ Pipeline failed: {e}")
        raise

# Or use automations in Prefect UI:
# Automations → Create → Trigger: Flow run failed → Action: Send Slack notification

CLI Reference

bash
# cli.sh: Common Prefect CLI commands
# Check connection
prefect version
prefect config view

# List flows and deployments
prefect flow-run ls
prefect deployment ls

# View logs
prefect flow-run logs <flow-run-id>

# Manage work pools
prefect work-pool create my-pool --type process
prefect work-pool ls

© Kilo-Org, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in skills/prefect of Kilo-Org/kilo-marketplace.

  • SKILL.md
  • LICENSE
  • _scores.json

Open the folder on GitHubat commit ff51758

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in Kilo-Org/kilo-marketplace, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Prefect 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.

Prefect compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Prefect this skillKilo-Org/kilo-marketplace1891 repos~1.4kAutomated safety check: PassApache-2.0
Upgrading Mwaa Environmentsaws/agent-toolkit-for-aws2.8k—~7.3kAutomated safety check: PassApache-2.0
Monitor With HaolemeHaolemeApp/Haoleme157—~1.3kAutomated safety check: PassAGPL-3.0
Raypproenca/dot-skills214—~2.2kAutomated safety check: PassMIT
Mle Workflowaffaan-m/ECC274k1 repos~5.6kAutomated safety check: PassMIT
Senior Data Scientistborghei/Claude-Skills874—~1.7kAutomated safety check: PassMIT

Similar skills

  • Upgrading Mwaa Environments

    aws/agent-toolkit-for-aws

    Official

    Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary.

    2.8k GitHub stars~7.3k tokensUpdated today
    Data & AnalyticsAuto-check passed
  • Monitor With Haoleme

    HaolemeApp/Haoleme

    Selectively monitor important long-running or resource-intensive commands with Haoleme by prefixing them with hao, so status, output, and completion notifications sync to the mobile app.

    157 GitHub stars~1.3k tokensUpdated 1 mo ago
    Data & AnalyticsAuto-check passed
  • Ray

    pproenca/dot-skills

    Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay.

    214 GitHub stars~2.2k tokensUpdated 1 mo ago
    AI & LLM EngineeringAuto-check passed
  • Mle Workflow

    affaan-m/ECC

    Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback.

    274k GitHub starsUsed in 1 repo~5.6k tokens
    Data & AnalyticsAuto-check passed
  • Senior Data Scientist

    borghei/Claude-Skills

    A skill your agent uses when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model…

    874 GitHub stars~1.7k tokensUpdated today
    Data & AnalyticsAuto-check passed
  • Crawl4AI Web Scraping

    smallnest/goclaw

    Scrapes sites, handles JavaScript-heavy pages and extracts structured data with Crawl4AI, through its crwl CLI or Python SDK, including schema-based extraction without an LLM.

    598 GitHub starsUsed in 1 repo~2.5k tokens
    Data & AnalyticsAuto-check passed

More from Kilo-Org/kilo-marketplace

All 87 skills in this repo
  • AzureML Project Scaffolding

    Kilo-Org/kilo-marketplace

    Sets up and maintains AzureML-ready Python projects as uv workspaces with devcontainers, a Makefile and job YAML, so local runs match cloud jobs and experiments stay reproducible.

    189 GitHub stars~3.1k tokensUpdated 8 days ago
    Auto-check: notes
  • Jupyter Notebook Builder

    Kilo-Org/kilo-marketplace

    Creates, inspects, edits and runs Jupyter notebooks, scaffolding experiment or tutorial notebooks from templates and preferring a Jupyter MCP server over raw JSON edits.

    189 GitHub stars~1.3k tokensUpdated 8 days ago
    Auto-check passed
  • Tableau Dashboard Creator

    Kilo-Org/kilo-marketplace

    Takes a plain-language dashboard request through brand setup, data exploration, planning, an interactive HTML mock and a Tableau implementation spec.

    189 GitHub stars~3.8k tokensUpdated 8 days ago
    Auto-check: notes
  • Elasticsearch File Ingest

    Kilo-Org/kilo-marketplace

    Ingest and transform data files (CSV/JSON/Parquet/Arrow IPC) into Elasticsearch with stream processing and custom transforms.

    189 GitHub stars~2.8k tokensUpdated 8 days ago
    Auto-check passed
  • Nifi Flow Layout

    Kilo-Org/kilo-marketplace

    A skill your agent uses when arranging Apache NiFi processors, process groups, ports, comments, numbering, crossing connections, dense fan-in/fan-out, or reusable readable canvas layouts.

    189 GitHub stars~1.5k tokensUpdated 8 days ago
    Auto-check passed
  • Splunk Ingest Processor Setup

    Kilo-Org/kilo-marketplace

    Render Cisco Data Fabric ingest-time routing workflows and Splunk Cloud Platform Ingest Processor setup plans with SPL2 pipelines, source types, destinations, lifecycle handoffs, queue and…

    189 GitHub stars~1.2k tokensUpdated 8 days ago
    Auto-check passed

Works with

Questions about Prefect

What does Prefect do?

Prefect is a modern workflow orchestration framework for Python data pipelines. Prefect is an agent skill from Kilo-Org/kilo-marketplace. Prefect is a modern workflow orchestration framework for Python data pipelines.

When should I use Prefect?

Prefect fits situations like: tasks that involve Deployment; tasks that involve Data pipelines and ETL.

How do I install Prefect in Claude Code?

Run `npx skills add Kilo-Org/kilo-marketplace --skill prefect -a claude-code`. Or copy the skill folder (skills/prefect in Kilo-Org/kilo-marketplace) into .claude/skills/prefect in your project. Claude Code loads it when a task matches its description.

How do I install Prefect in Codex?

Run `npx skills add Kilo-Org/kilo-marketplace --skill prefect -a codex`. Or copy the skill folder (skills/prefect in Kilo-Org/kilo-marketplace) into .agents/skills/prefect in your project. Codex loads it when a task matches its description.

Can I use Prefect 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 Kilo-Org/kilo-marketplace --skill prefect -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prefect, .gemini/skills/prefect, .github/skills/prefect and .opencode/skills/prefect in your project.

What does Prefect need to run?

Going by SKILL.md and its folder, Prefect needs the command-line tools its instructions call (prefect and pip). Our summary lists: Python 3. Compatibility (from SKILL.md): macos, linux, windows.

Does Prefect access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Prefect 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 Prefect use?

Prefect is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prefect use?

About 1.4k tokens (SKILL.md is roughly 5.5k 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 Prefect?

Skills that share tags, products or a category with Prefect: Upgrading Mwaa Environments (aws/agent-toolkit-for-aws, 2.8k stars), Monitor With Haoleme (HaolemeApp/Haoleme, 157 stars), Ray (pproenca/dot-skills, 214 stars) and Mle Workflow (affaan-m/ECC, 274k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prefect?

Kilo-Org (a GitHub organization) maintains it in Kilo-Org/kilo-marketplace, which has 189 GitHub stars. The repository holds 87 skills in this directory. The repository was last updated on September 28, 2026.

Source: Kilo-Org/kilo-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.