Coding Skills#

AI-powered programming assistant for distributed data development.

Overview#

MaxFrame Coding Skill is an AI coding assistant released by Alibaba Cloud MaxFrame. It integrates with mainstream AI coding assistants as an intelligent plugin and injects MaxFrame’s distributed data processing knowledge into AI agents, enabling them to generate runnable MaxFrame code from natural language requirements.

MaxFrame Coding Skill covers the full MaxFrame development workflow, from session management, data reading and writing, and operator selection to result writing. It lowers the entry barrier for distributed data processing and improves coding efficiency.

Architecture#

MaxFrame Coding Skill uses a multi-layer knowledge injection architecture to systematically inject the complete development knowledge base into AI agents:

+---------------------------------------------------+
|               AI Coding Assistants                |
|    (Claude Code / Cursor / Codex / Gemini CLI /   |
|        Tongyi Lingma / OpenCode / ...)            |
+---------------------------------------------------+
|            MaxFrame Coding Skill                  |
|  +----------+  +----------+  +----------+         |
|  | Coding   |  | Context  |  | Operator |         |
|  | Skill    |  | Guide    |  | Selector |         |
|  +----------+  +----------+  +----------+         |
|  +----------+  +----------+  +----------+         |
|  | Selection|  | API Docs |  | Operator |         |
|  | Rules    |  | 900+ pp. |  | Validator|         |
|  +----------+  +----------+  +----------+         |
|  +----------------------------------------+       |
|  |      Production-grade code examples    |       |
|  +----------------------------------------+       |
+---------------------------------------------------+
|               MaxFrame SDK                        |
|    DataFrame | Tensor | Learn | UDF | Session     |
+---------------------------------------------------+
|            MaxCompute distributed engine          |
+---------------------------------------------------+

Component	Capability
Coding skill definition	Defines the Skill’s core responsibilities, capability boundaries, and workflow.
Context guide	A comprehensive 1700+ line reference covering all features from basics to advanced usage.
Operator selector agent	An intelligent agent responsible for operator discovery, validation, and recommendation.
Selection rule engine	Selection strategies based on performance-first, batch-first, and compatibility-first principles.
API documentation library	900+ pages of complete MaxFrame API documentation with real-time lookup support.
Operator validation scripts	Executable scripts that verify whether operators exist and retrieve detailed documentation.
Production examples	10 complete production-grade code templates covering typical scenarios.

Supported Platforms#

MaxFrame Coding Skill supports mainstream AI coding assistants with a unified installation pattern:

AI coding platform	Installation directory
Claude Code	`.claude/skills/`
Cursor	`.cursor/rules/`
Codex	`.codex/skills/`
OpenCode	`.opencode/skills/`
Gemini CLI	`.gemini/skills/`
Tongyi Lingma / Qoder	`.aone_copilot/skills/` or `.qoder/skills/`

Installation#

Download the package

Skill package: maxframe-coding-skill.zip
Extract it to the skills directory of your AI coding assistant. For Claude Code:
```
unzip maxframe-coding-skill.zip -d your-project/.claude/skills/
```
Verify the installation
```
ls your-project/.claude/skills/maxframe-job-coding/
```
The directory should contain SKILL.md, examples/, references/, and scripts/.
After installation, enter the following prompt in your AI coding assistant
```
Create a MaxFrame job that reads data from the user_behavior table, groups by city to calculate GMV, and writes the result to the city_gmv_report table.
```
The AI assistant will automatically:
- Confirm the data source and output target.
- Recommend the best operator combination, such as groupby().agg().
- Generate runnable code with complete Session management and error handling.

Core Capabilities#

Intelligent Operator Recommendation#

MaxFrame provides a multi-layer operator system, including standard pandas-compatible operators, MaxFrame-specific .mf extension operators such as apply_chunk, map_reduce, flatmap, and rebalance, and UDF / UDTF capabilities. For a specific data processing requirement, the Operator Selector agent built into Coding Skill automatically completes operator selection and validation:

Task-driven recommendation: recommends the best operator combination based on the task description and explains the reason.
API authenticity validation: validates operators against 900+ pages of API documentation to prevent hallucinated APIs.
Fallback alternatives: provides alternatives, including UDF fallback options, when the preferred operator has constraints.

Example:

User: "I need a rolling average for time-series data."
AI:   "Use DataFrame.rolling().
       If you need custom window logic, use .mf.apply_chunk() as an alternative."

End-to-End Code Generation#

Coding Skill covers the complete MaxFrame development lifecycle:

Session creation -> data reading -> operator selection -> data processing -> result writing -> Session cleanup

It uses a three-phase confirm-before-execute interaction model to ensure the generated code precisely matches the requirement:

Phase	Content	Description
Phase 1	Requirement and data confirmation	Confirms data sources, target tables, selected columns, and related inputs.
Phase 2	Operator selection confirmation	Shows recommended operators and alternatives, then waits for confirmation.
Phase 3	Code generation and validation	Generates complete runnable code based on the confirmed plan.

All generated code follows production-grade standards:

Uses try/finally to ensure Session resource cleanup.
Automatically calls .execute() to trigger lazy execution.
Correctly declares UDF return types with dtypes.
Includes complete error handling logic.

Common Pitfall Prevention#

Generic AI-generated MaxFrame code often runs into the following issues. Coding Skill solves them with its built-in knowledge base:

Common issue	How Coding Skill solves it
Calling nonexistent APIs	Validates against 900+ pages of documentation to prevent hallucinated APIs.
Missing `.execute()` calls	Enforces lazy execution patterns and includes execution triggers in code templates.
Session not destroyed correctly	Uses `try/finally` in all generated code to release resources.
UDF return type mismatch	Shows the correct `dtypes` declaration pattern through examples.
Poor execution engine choice	Recommends engines by SQL Engine > DPE > SPE priority.
Inefficient operators	Recommends `DataFrame.mf.apply_chunk` instead of `Series.apply` where appropriate.

Built-In Scenario Templates#

Coding Skill includes 10 production-grade code templates for typical business scenarios. AI agents can use these templates to generate high-quality code:

Scenario	Example file	Core capability
LLM batch inference	`ai_function_basic.py`	Distributed batch inference with ManagedTextLLM, ready to use out of the box.
GPU-accelerated computing	`gpu_unit_dpe_processing.py`	GPU resource allocation with `@with_running_options(gu=1)`.
OSS file processing	`fs_mount_example.py`	Distributed OSS file reading with `@with_fs_mount`.
Multiple OSS mounts	`oss_multi_mount.py`	Mounts one or more OSS buckets at the same time.
Grouped batch processing	`groupby_batch_processing.py`	Efficient grouped batch processing with `groupby` + `apply_chunk`.
Complex data structures	`complex_struct.py`	Nested structures with custom grouped processing.
Arrow type handling	`complex_struct_arrow.py`	PyArrow complex types with JSON conversion.
DLF external table writes	`dlf_table_write_basic.py`	DLF external table configuration and data writing.
DLF primary-key table writes	`dlf_table_write_with_pk.py`	Primary-key tables with binary data type handling.
Large-scale document deduplication	`minhash_lsh_document_similarity.py`	MinHash + LSH algorithm with 4000+ parallelism support.

Typical Scenarios#

Scenario 1: Distributed Batch LLM Inference#

Requirement: run batch inference on massive text data with a large language model.

No model deployment, GPU resource management, or inference service development is required. ManagedTextLLM provides built-in qwen2.5 series models, DeepSeek-R1, and more.

Generated code example:

import os

import maxframe.dataframe as md
from maxframe.learn.contrib.llm.models.managed import ManagedTextLLM
from maxframe.session import new_session
from odps import ODPS

o = ODPS(
    os.getenv("ALIBABA_CLOUD_ACCESS_KEY_ID"),
    os.getenv("ALIBABA_CLOUD_ACCESS_KEY_SECRET"),
    project="your-default-project",
    endpoint="your-end-point",
)

session = new_session(o)

try:
    df = md.DataFrame(
        {
            "query": [
                "What is the average distance from Earth to the Sun?",
                "What is the boiling point of water?",
            ]
        }
    )
    df.execute()

    llm = ManagedTextLLM(name="qwen2.5-1.5b-instruct")
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "{query}"},
    ]
    result = llm.generate(df, prompt_template=messages)
    result.execute()
finally:
    session.destroy()

Scenario 2: Distributed OSS File Processing#

Requirement: mount files from OSS to every distributed Worker node for parallel reading and processing.

OSS paths are mounted as local file system paths. Distributed Workers read data in parallel, and throughput scales with the number of nodes.