# Competition Style Ml Engineering

> Skill: competition-style-ml-engineering

- Skill: `dingxingdi/competition-style-ml-engineering-2` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add dingxingdi/competition-style-ml-engineering-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dingxingdi/competition-style-ml-engineering-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: dingxingdi (https://skillmd.com/u/dingxingdi)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/dingxingdi/competition-style-ml-engineering-2

---


# Skill: competition-style-ml-engineering
## 1. Capability Definition & Real Case
* **Professional Definition**: The ability to solve benchmarked ML engineering tasks in competition-like settings that require working from datasets, descriptions, grading code, and submission formats to produce valid, high-scoring outputs under resource and process constraints.
* **Dimension Hierarchy**: Data and ML Workflow Engineering->Machine Learning Engineering->competition-style-ml-engineering
### Real Case
**[Case 1]**
* **Initial Environment**: An offline competition workspace contains a description, train.csv, test.csv, sample_submission.csv, grading code, and baseline utilities. The task has no complete working solution yet.
* **Real Question**: Produce a valid submission that performs competitively on the competition metric using only the provided resources.
* **Real Trajectory**: Read the competition overview and data schema, inspect the submission format, implement preprocessing and a baseline model, validate the submission locally, iterate on data handling and model choices, and generate the final CSV file.
* **Real Answer**: A valid submission file is produced in the expected format and scores better than naive baselines under the local grader.
* **Why this demonstrates the capability**: This tests competition-style ML engineering because the agent must combine dataset understanding, code writing, submission validity, and iterative optimization inside a leaderboard-like workflow rather than merely patching an existing training script.
---
**[Case 2]**
* **Initial Environment**: A more complex competition workspace includes multimodal data, longer training cycles, and stricter formatting rules. Local validation can confirm file validity but not reveal the full score directly.
* **Real Question**: Engineer a robust submission pipeline that can train, evaluate, and export predictions in the required competition format.
* **Real Trajectory**: Map data modalities and labels, build a training script, generate validation estimates internally, check submission validity with the local tool, debug formatting or shape errors, and refine the strongest model within runtime constraints.
* **Real Answer**: The pipeline yields a valid competition submission and demonstrates sound engineering choices for improving performance under limited feedback.
* **Why this demonstrates the capability**: The capability lies in handling the entire competition workflow: datasets, local validation, formatting, experiment planning, and robustness to hidden-score uncertainty. This goes beyond ordinary model improvement because correctness of the submission process itself is part of the task.

## Pipeline Execution Instructions
To synthesize data for this capability, you must strictly follow a 3-phase pipeline. **Do not hallucinate steps.** Read the corresponding reference file for each phase sequentially:

1. **Phase 1: Environment Exploration**
   Read the exploration guidelines to discover raw knowledge seeds:
   `references/EXPLORATION.md`

2. **Phase 2: Trajectory Selection**
   Once Phase 1 is complete, read the selection criteria to evaluate the trajectory:
   `references/SELECTION.md`

3. **Phase 3: Data Synthesis**
   Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data:
   `references/SYNTHESIS.md`