<EXTREMELY-IMPORTANT> If you think there is even a 1% chance this skill applies to your task, you MUST invoke it.

IF A SKILL APPLIES TO YOUR TASK, YOU DO NOT HAVE A CHOICE. YOU MUST USE IT. </EXTREMELY-IMPORTANT>

Instruction Priority

User's explicit instructions (CLAUDE.md, GEMINI.md, AGENTS.md) — highest priority
MaxFrame coding skills — override default system behavior where they conflict
Default system prompt — lowest priority

Platform Adaptation

This skill uses Claude Code tool names. Non-CC platforms: substitute equivalent tools.

MaxFrame Coding - Create, Test, Debug, Iterate, and Build Custom Runtime

What This Skill Can Do

Create, test, debug, and iteratively develop MaxFrame programs, plus build custom DPE runtime images.

Create MaxFrame jobs from scratch or modify existing ones
Design data processing pipelines using pandas-compatible APIs
Execute MaxFrame code with proper session management
Debug with remote logview URLs or local IDE breakpoints
Generate custom Docker images with specific Python libraries

Mandatory Checklist

Detect Scenario Type — identify which of the 4 scenarios applies
Understand Requirements — ask clarifying questions about data, operations, constraints
Select Appropriate Workflow — match scenario to workflow pattern
Execute Workflow Steps — follow scenario-specific steps below
Validate Execution — ensure execute() called, session cleaned up
Provide Follow-up Guidance — debugging tips, optimization suggestions

Process Flow

dot

digraph maxframe_workflow {
    "User Request Arrives" [shape=box];
    "Detect Scenario Type" [shape=diamond];
    "Scenario 1: Writing Code" [shape=box];
    "Scenario 2: Remote Debug" [shape=box];
    "Scenario 3: Local Debug" [shape=box];
    "Scenario 4: Custom Runtime" [shape=box];
    "Understand Requirements" [shape=box];
    "Operator Selection Needed?" [shape=diamond];
    "Use lookup_operator.py" [shape=box];
    "Confirm with User" [shape=box];
    "Implement Code/Config" [shape=box];
    "Add Error Handling" [shape=box];
    "Validate execute() Called" [shape=box];
    "Validate Session Cleanup" [shape=box];
    "Provide Guidance" [shape=doublecircle];

    "User Request Arrives" -> "Detect Scenario Type";
    "Detect Scenario Type" -> "Scenario 1: Writing Code" [label="new pipeline"];
    "Detect Scenario Type" -> "Scenario 2: Remote Debug" [label="cluster testing"];
    "Detect Scenario Type" -> "Scenario 3: Local Debug" [label="IDE breakpoints"];
    "Detect Scenario Type" -> "Scenario 4: Custom Runtime" [label="custom image"];
    "Scenario 1: Writing Code" -> "Understand Requirements";
    "Scenario 2: Remote Debug" -> "Understand Requirements";
    "Scenario 3: Local Debug" -> "Understand Requirements";
    "Scenario 4: Custom Runtime" -> "Understand Requirements";
    "Understand Requirements" -> "Operator Selection Needed?";
    "Operator Selection Needed?" -> "Use lookup_operator.py" [label="yes"];
    "Operator Selection Needed?" -> "Implement Code/Config" [label="no"];
    "Use lookup_operator.py" -> "Confirm with User";
    "Confirm with User" -> "Implement Code/Config";
    "Implement Code/Config" -> "Add Error Handling";
    "Add Error Handling" -> "Validate execute() Called";
    "Validate execute() Called" -> "Validate Session Cleanup";
    "Validate Session Cleanup" -> "Provide Guidance";
}

Scenario Detection Logic

Scenario 1: Writing MaxFrame Code

User wants to create new data processing pipeline
User mentions reading from/writing to MaxCompute tables
User asks for complete MaxFrame program
Keywords: "create MaxFrame", "write MaxFrame code", "build pipeline", "process data with MaxCompute"

Scenario 2: Remote Debug Mode

User wants to test with actual cluster resources
User mentions job execution errors
User asks for logview URLs
User wants to diagnose execution failures
Keywords: "debug MaxFrame job", "logview", "remote test", "execution error", "cluster testing"

Scenario 3: Local Debug Mode

User wants to debug UDF functions iteratively
User mentions IDE breakpoints (VSCode, PyCharm)
User wants to test with sample data locally
User wants fast iteration without network
Keywords: "local debug", "IDE breakpoints", "debug UDF locally", "VSCode/PyCharm debug"

Scenario 4: Create Custom Runtime Image

User needs Python libraries not in standard runtime
User wants GPU-enabled runtime
User mentions building custom DPE image
Keywords: "custom runtime", "DPE runtime image", "GPU runtime", "install custom packages", "build Docker image"

Core Rules

1. Use Public APIs Only

Use APIs from:

maxframe.dataframe

maxframe.tensor

maxframe.learn

maxframe.session

maxframe.udf

maxframe.config

2. DO NOT Read Private .env Files

Use

dotenv.load_dotenv()

programmatically. Never read

.env

files directly with Read tool.

3. Lazy Execution

MaxFrame uses lazy execution. Operations build computation graph, execute only when

.execute()

called. Always call .execute().

4. Session Management

Always create session before operations, destroy in

finally

block for cleanup.

5. Operator Selection with User Confirmation

Before implementing processing logic, confirm operator selection with user using

scripts/lookup_operator.py

Red Flags

Thought	Reality
"This is just a simple MaxFrame question"	Questions are tasks. Invoke the skill.
"I already know the MaxFrame API"	Skills have latest patterns. Use them.
"Let me just write the code directly"	Operator selection is MANDATORY.
"I can skip operator confirmation"	User confirmation is REQUIRED.

Scenario 1: Writing MaxFrame Code

Workflow Steps

Understand Requirements — source/target tables, schema, partition filters, write mode, processing logic
Operator Selection (MANDATORY) — use
```
python scripts/lookup_operator.py search "<operation>"
```
, present options, get confirmation
Implement Code — session setup, read data, process with confirmed operators, write results, add execute(), cleanup in finally
Add Error Handling — wrap execute() in try/except, print logview URL on error
Validate — ensure execute() called, session.destroy() in finally, no hardcoded credentials

Example Code Structure

python

import maxframe.dataframe as md
from maxframe.session import new_session
import dotenv

dotenv.load_dotenv()
session = new_session()

try:
    df = md.read_odps_table("source_table")
    result = df.groupby('column').agg({'value': 'sum'})
    md.to_odps_table(result, "target_table", overwrite=True).execute()
finally:
    session.destroy()

See:

references/common-workflow.md

for complete patterns.

Scenario 2: Remote Debug Mode

Workflow Steps

Understand Requirements — current code state, error messages, table names
Add Logview Support — session before operations, try/except around execute(), logview URL in except
Provide Debugging Guidance — explain logview usage, common error patterns

Example Code Structure

python

import maxframe.dataframe as md
from maxframe.session import new_session

session = new_session()

try:
    df = md.read_odps_table("table_name")
    result = df.groupby('region').agg({'sales': 'sum'})
    result.execute()
except Exception as e:
    print(f"Error: {e}")
    print(f"Logview URL: {session.get_logview_address()}")
finally:
    session.destroy()

Common Error Patterns

Authentication Errors — verify environment variables
Table Not Found — check table name and permissions
Timeout Errors — check logview, optimize query
Type Mismatch — check DataFrame dtypes
SQL Errors — review generated SQL in logview

See:

references/remote-debug-guide.md

for detailed solutions.

Scenario 3: Local Debug Mode

Workflow Steps

Understand Requirements — UDF logic, sample data schema, IDE preference
Create Local Debug Setup — session with
```
debug=True
```
, sample data with
```
md.DataFrame(pd.DataFrame(...))
```
Provide IDE Setup Guidance — breakpoint setup, execution flow

Example Code Structure

python

import maxframe.dataframe as md
from maxframe.session import new_session
import pandas as pd

session = new_session(debug=True)

sample_data = pd.DataFrame({
    'user_id': ['u1', 'u2', 'u3'],
    'level': ['gold', 'silver', 'bronze'],
    'amount': [1000, 500, 100]
})
df = md.DataFrame(sample_data)

def calculate_discount(row):
    # Set breakpoint here in IDE
    if row['level'] == 'gold':
        return row['amount'] * 0.1
    return row['amount'] * 0.02

result = df.apply(calculate_discount, axis=1)
result.execute()
session.destroy()

See:

references/local-debug-guide.md

for complete guide.

Scenario 4: Create Custom Runtime Image

Build custom Docker images through conversational guidance using best practices from reference guides.

When to Create Custom Runtime

Create when: need Python libraries not in standard DPE runtime, GPU-enabled processing, specific Python version, custom system dependencies NOT needed when: standard packages suffice, no GPU requirements

Conversational Workflow

Read Best Practices Guide —

references/runtime-image-guides/README.md

Base Image Selection — Ubuntu 22.04 (GPU/ML workloads) or Ubuntu 24.04 (modern development)
Python Version Selection — Python 3.11 (production), 3.10-3.12 (development), or all versions
GPU Configuration — CUDA 12.4 + PyTorch 2.6.0+cu124 (if ML workloads)
Iterative Package Collection — collect required packages, note version constraints
Output Directory — confirm where to create files
Build Dockerfile Section-by-Section — header, base setup, conda setup, GPU setup, packages, env config, verification
Create Support Files — README.md, .dockerignore, requirements.txt
Provide Build and Test Instructions
MaxFrame Usage Example

Step-by-Step Guidance

Step 1: Base Image Selection (AskUserQuestion)

Present Ubuntu options with trade-offs:

Which Ubuntu version for your custom runtime?

A. Ubuntu 22.04 (Recommended for most cases)
   - Stable, production-ready
   - Excellent CUDA support (12.4, 12.1, 11.8)
   - Widely tested ML libraries (PyTorch, TensorFlow)
   - LTS until 2027

B. Ubuntu 24.04 (Modern/latest)
   - Newer system packages
   - Latest LTS (until 2029)
   - Better for non-GPU workloads
   - Python 3.12 integration

Recommendation:
- GPU/ML workloads → Ubuntu 22.04
- Modern development → Ubuntu 24.04

Step 2: Python Version Selection (AskUserQuestion)

Which Python versions?

A. Python 3.11 only (Recommended for production)
   - Best performance
   - Smallest image (~1 GB)
   - Excellent package support

B. Python 3.10, 3.11, 3.12 (Development)
   - Good compatibility
   - Medium size (~2 GB)
   - Recent versions

C. All versions 3.7-3.12 (Maximum flexibility)
   - Largest image (~3-5 GB)
   - Maximum compatibility
   - Testing across versions

Recommendation:
- Production → Single version (3.11)
- Development → Recent versions (3.10-3.12)

Step 3: GPU Configuration (AskUserQuestion)

If user mentions GPU or ML packages:

Need GPU support?

A. Yes - GPU-enabled with CUDA 12.4 (Recommended)
   - Install PyTorch 2.6.0+cu124
   - CUDA toolkit 12.4
   - Note: Requires Ubuntu 22.04 for best compatibility

B. No - CPU only
   - Standard package installation
   - Smaller image size

Recommendation: For ML/AI workloads, GPU support significantly improves performance.

Compatibility Handling: If user selected Ubuntu 24.04 earlier and now requests GPU support:

Explain: "Ubuntu 24.04 has limited CUDA support. Ubuntu 22.04 is recommended for GPU workloads."
AskUserQuestion: "Should I use Ubuntu 22.04 instead for better GPU compatibility?" (Yes recommended)

Step 4: Build Dockerfile Section-by-Section

For each section:

Read pattern from best practices guide
Explain purpose and trade-offs
Write section with inline comments
Accumulate into complete Dockerfile

Sections:

Header — Image metadata, configuration summary
Base setup — FROM, apt packages, locales, timezone
Conda setup — Miniforge installation, environment creation
GPU setup — CUDA installation, PyTorch with CUDA (if applicable)
Package installation — User packages in multi-environment loops
Environment config — MF_PYTHON_EXECUTABLE, CONDA_DEFAULT_ENV, PATH
Verification — Health checks, Python version verification

Step 5: Provide Build and Test Instructions

bash

# Build
docker build -t <image-tag> <output-dir>

# Test Python
docker run --rm <image-tag> conda run -n py311 python --version

# Test GPU (if applicable)
docker run --rm --gpus all <image-tag> python -c "import torch; print(torch.cuda.is_available())"

# Test packages
docker run --rm <image-tag> conda run -n py311 python -c "import transformers; print(transformers.__version__)"

# Push to registry
docker push <image-tag>

Step 6: MaxFrame Usage Example

python

from maxframe.session import new_session

session = new_session(
    odps=odps_connection,
    image="your-registry/your-image:v1"
)

# Your MaxFrame operations here

Default Recommendations

Component	Recommendation
Base Image	Ubuntu 22.04 (production, GPU, ML)
Python	3.11 (production), 3.10-3.12 (development)
GPU	Ubuntu 22.04 + CUDA 12.4 + PyTorch 2.6.0+cu124

Critical Notes

MaxFrame SDK NOT in Runtime Image: SDK and pyodps are client-side only. Custom runtime needs user-specific packages (transformers, pandas, etc.).

MF_PYTHON_EXECUTABLE (CRITICAL): Always set:

ENV MF_PYTHON_EXECUTABLE=/py-runtime/envs/<env_name>/bin/python

Best Practices Reference

See:

references/runtime-image-guides/

for detailed guides on base image selection, Python environment strategy, package management, GPU/CUDA configuration, Dockerfile templates, and testing/validation.

Operator Selection Workflow

MANDATORY before implementing processing logic when user mentions specific operations, asks about efficiency/performance, or you need to find appropriate MaxFrame operator.

Workflow

Identify Operations — list required transformations

Find Operators —

python scripts/lookup_operator.py search "<operation>"

Present Options — show operator name, description, trade-offs
Get User Confirmation — confirm operator and parameters
Implement — use confirmed operator

See:

references/operator-selector.md

for detailed guidance.

Key Validation Points

Before finishing, validate:

```
.execute()
```
called on result DataFrame
Session created before operations
Session destroyed in
```
finally
```
block
No hardcoded credentials
Operator selection confirmed with user
Error handling with logview URL (remote)
```
debug=True
```
used (local debug)
```
MF_PYTHON_EXECUTABLE
```
set (custom runtime)

Resources

References

Operator Selector:
```
references/operator-selector.md
```
Local Debug:
```
references/local-debug-guide.md
```
Remote Debug:
```
references/remote-debug-guide.md
```
Complete Workflow:
```
references/common-workflow.md
```
Runtime Guides:
```
references/runtime_image_*.md
```

Examples

Working Examples:
```
assets/examples/*.py
```

Scripts

Operator Lookup:
```
scripts/lookup_operator.py
```

alibabacloud-odps-maxframe-coding

NPX Install

Tags

SKILL.md Content

Instruction Priority

Platform Adaptation

MaxFrame Coding - Create, Test, Debug, Iterate, and Build Custom Runtime

What This Skill Can Do

Mandatory Checklist

Process Flow

Scenario Detection Logic

Core Rules

1. Use Public APIs Only

2. DO NOT Read Private .env Files

3. Lazy Execution

4. Session Management

5. Operator Selection with User Confirmation

Red Flags

Scenario 1: Writing MaxFrame Code

Workflow Steps

Example Code Structure

Scenario 2: Remote Debug Mode

Workflow Steps

Example Code Structure

Common Error Patterns

Scenario 3: Local Debug Mode

Workflow Steps

Example Code Structure

Scenario 4: Create Custom Runtime Image

When to Create Custom Runtime

Conversational Workflow

Step-by-Step Guidance

Default Recommendations

Critical Notes

Best Practices Reference

Operator Selection Workflow

Workflow

Key Validation Points

Resources

References

Examples

Scripts