Skill: internal-api-aware-code-completion-and-refinement

[Case 2]

• Initial Environment: A deep learning framework repository used for model training and deployment. The developer is editing the DefaultTrainer class in trainers/base.py and needs to initialize the logger writers using project-specific utilities.
• Real Question: Initialize the self.log_writers variable within the __init__ function of the DefaultTrainer class using the appropriate project-defined utility.
• Real Trajectory: The agent creates a draft which incorrectly uses a generic getattr pattern to find common writers. Realizing this pattern is likely a placeholder, the agent summarizes the intent as 'obtaining log writers from project settings' and searches the functional semantic index. It identifies the internal API get_log_writers in the project's utility module, which perfectly matches the required behavior. The agent then implements the call while passing the existing self.opts and self.save_location variables.
• Real Answer: self.log_writers = get_log_writers(self.opts, save_location=self.save_location)
• Why this demonstrates the capability: This illustrates the capability to move from a vague functional intent to a concrete internal API call by bypassing superficial code similarity and grounding the solution in the project's actual functional repertoire.

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