[2026-01-07T18:05:02] OpenAI Codex v0.0.0 (research preview)
[2026-01-07T18:05:02] binary: /home/volker/.cache/just-every/code/0.6.40/code-aarch64-unknown-linux-musl
--------
workdir: /home/volker/coding/fsspeckit
model: gpt-5.2-codex
provider: openai
approval: never
sandbox: read-only
reasoning effort: medium
reasoning summaries: auto
--------
[2026-01-07T18:05:02] User instructions:
Context: Repo: /home/volker/coding/fsspeckit. We updated getting_started examples with dependency checks and README install guidance. Ran examples via uv and they succeeded. There may be API drift in examples (e.g., 04_pyarrow_merges mentions fs.insert_dataset etc which are removed). Identify worthwhile follow-ups that improve reliability and user experience.

Agent: Propose meaningful and useful follow-ups after getting_started examples now run successfully. Consider docs/README improvements, example accuracy vs current APIs, testing, CI checks, and optional refactors. Provide a concise plan with steps and rationale.

Files to consider: /home/volker/coding/fsspeckit/examples/datasets/getting_started/README.md, /home/volker/coding/fsspeckit/examples/datasets/getting_started/04_pyarrow_merges.py, /home/volker/coding/fsspeckit/src/fsspeckit/datasets/pyarrow/io.py

[Running in read-only mode - no modifications allowed]

[2026-01-07T18:05:05] thinking

**Reviewing plan and skills files**
[2026-01-07T18:05:06] exec cat /home/volker/.code/skills/.system/plan/SKILL.md in /home/volker/coding/fsspeckit
[2026-01-07T18:05:06] cat /home/volker/.code/skills/.system/plan/SKILL.md succeeded in 2ms:
---
name: plan
description: Generate a plan for how an agent should accomplish a complex coding task. Use when a user asks for a plan, and optionally when they want to save, find, read, update, or delete plan files in $CODEX_HOME/plans (default ~/.codex/plans).
metadata:
  short-description: Generate a plan for a complex task
---

# Plan

## Overview

Draft structured plans that clarify intent, scope, requirements, action items, testing/validation, and risks.

Optionally, save plans to disk as markdown files with YAML frontmatter and free-form content. When drafting in chat, output only the plan body without frontmatter; add frontmatter only when saving to disk. Only write to the plans folder; do not modify the repository codebase.

This skill can also be used to draft codebase or system overviews.

## Core rules

- Resolve the plans directory as `$CODEX_HOME/plans` or `~/.codex/plans` when `CODEX_HOME` is not set.
[2026-01-07T18:05:06] tokens used: 8,840

[2026-01-07T18:05:09] thinking

**Searching for openspec AGENTS file**
[2026-01-07T18:05:09] exec cat /home/volker/coding/fsspeckit/openspec/AGENTS.md in /home/volker/coding/fsspeckit
[2026-01-07T18:05:09] cat /home/volker/coding/fsspeckit/openspec/AGENTS.md succeeded in 4ms:
# OpenSpec Instructions

Instructions for AI coding assistants using OpenSpec for spec-driven development.

## TL;DR Quick Checklist

- Search existing work: `openspec spec list --long`, `openspec list` (use `rg` only for full-text search)
- Decide scope: new capability vs modify existing capability
- Pick a unique `change-id`: kebab-case, verb-led (`add-`, `update-`, `remove-`, `refactor-`)
- Scaffold: `proposal.md`, `tasks.md`, `design.md` (only if needed), and delta specs per affected capability
- Write deltas: use `## ADDED|MODIFIED|REMOVED|RENAMED Requirements`; include at least one `#### Scenario:` per requirement
- Validate: `openspec validate [change-id] --strict` and fix issues
- Request approval: Do not start implementation until proposal is approved

## Three-Stage Workflow

### Stage 1: Creating Changes
Create proposal when you need to:
- Add features or functionality
- Make breaking changes (API, schema)
[2026-01-07T18:05:09] tokens used: 10,695

[2026-01-07T18:05:13] thinking

**Planning file review**
[2026-01-07T18:05:13] exec cat /home/volker/coding/fsspeckit/examples/datasets/getting_started/README.md in /home/volker/coding/fsspeckit
[2026-01-07T18:05:14] cat /home/volker/coding/fsspeckit/examples/datasets/getting_started/README.md succeeded in 2ms:
# Getting Started with Datasets

This directory contains beginner-friendly examples that introduce the fundamental concepts of working with datasets using fsspeckit.

## Overview

The examples in this directory are designed to help you:
- Understand basic dataset operations
- Learn when to use DuckDB vs PyArrow approaches
- Master essential data merging techniques
- Build a foundation for more advanced workflows

## Learning Path

We recommend following the examples in numerical order:

### 1. `01_duckdb_basics.py` - DuckDB Fundamentals
**Prerequisites**: Basic Python knowledge
**Time**: 15-20 minutes

[2026-01-07T18:05:14] tokens used: 14,849
[2026-01-07T18:05:15] exec cat /home/volker/coding/fsspeckit/examples/datasets/getting_started/04_pyarrow_merges.py in /home/volker/coding/fsspeckit
[2026-01-07T18:05:15] cat /home/volker/coding/fsspeckit/examples/datasets/getting_started/04_pyarrow_merges.py succeeded in 3ms:
"""
PyArrow Merge-Aware Writes - Getting Started

This example introduces PyArrow's merge-aware write functionality for efficient dataset operations.

The example covers:
1. Basic merge-aware write concepts
2. Strategy selection (insert, upsert, update, etc.)
3. Key column configuration
4. Convenience helper functions
5. Performance benefits over traditional approaches
"""

import argparse
import tempfile
from pathlib import Path
from typing import Dict, Any

try:
    import pyarrow as pa
[2026-01-07T18:05:15] tokens used: 16,717
[2026-01-07T18:05:17] exec cat /home/volker/coding/fsspeckit/src/fsspeckit/datasets/pyarrow/io.py in /home/volker/coding/fsspeckit
[2026-01-07T18:05:17] cat /home/volker/coding/fsspeckit/src/fsspeckit/datasets/pyarrow/io.py succeeded in 3ms:
"""PyArrow dataset I/O and maintenance operations.

This module contains the PyarrowDatasetIO class for reading, writing, and
maintaining parquet datasets using PyArrow's high-performance engine.
"""

from __future__ import annotations

from typing import TYPE_CHECKING, Any, Literal, Callable, Iterable

if TYPE_CHECKING:
    import pyarrow as pa
    import pyarrow.dataset as ds
    from fsspec import AbstractFileSystem

    from fsspeckit.core.incremental import MergeResult
    from fsspeckit.core.merge import MergeStats
    from fsspeckit.datasets.pyarrow.memory import MemoryMonitor
    from fsspeckit.datasets.pyarrow.adaptive_tracker import AdaptiveKeyTracker
    from fsspeckit.datasets.write_result import WriteDatasetResult
[2026-01-07T18:05:17] tokens used: 21,522

[2026-01-07T18:05:23] thinking

**Outlining response plan**
[2026-01-07T18:05:23] codex

Using skill: plan (user asked for a concise plan).

# Plan

Focus on follow-ups that improve reliability and UX now that examples run, with an emphasis on API accuracy and documentation clarity.

## Requirements
- Ensure getting_started examples reflect current public APIs (no stale `fs.*` helpers).
- Keep README and example list accurate and runnable with documented install steps.
- Add lightweight validation so drift is caught early (CI or local script).

## Scope
- In: `examples/datasets/getting_started/README.md`, `examples/datasets/getting_started/04_pyarrow_merges.py`, `src/fsspeckit/datasets/pyarrow/io.py`, docs references in examples.
- Out: Major API redesign, new merge features, performance tuning.

## Files and entry points
- `examples/datasets/getting_started/README.md`
- `examples/datasets/getting_started/04_pyarrow_merges.py`
- `src/fsspeckit/datasets/pyarrow/io.py`

## Data model / API changes
- None planned; align docs/examples to current API or introduce minimal wrappers only if they already exist as intended public API.

## Action items
[ ] Audit `04_pyarrow_merges.py` for API drift and replace `fs.*_dataset` calls with current supported APIs (likely `PyarrowDatasetHandler.merge(...)` or `write_dataset(...)`), or remove/mark sections as “conceptual” if no API exists.  
Rationale: avoid teaching non-existent calls.

[ ] Update `examples/datasets/getting_started/README.md` to include `04_pyarrow_merges.py` in the learning path and explicitly note which merge operations are supported today.  
Rationale: align docs with the expanded example set and reduce confusion.

[ ] Add a small “API compatibility” note in the example (or README) pointing to the real merge entry points (`PyarrowDatasetIO.merge` and `MergeStrategy`), with references to `src/fsspeckit/datasets/pyarrow/io.py`.  
Rationale: direct readers to authoritative APIs.

[ ] Add a lightweight example check in CI (or a local `scripts/verify_examples.py`) that runs the getting_started scripts under the documented env (uv/pip) with a short timeout.  
Rationale: catch drift and dependency issues early.

[ ] Consider adding a “public API” section to the dataset docs that lists supported operations and helpers so examples can link to a single stable source.  
Rationale: reduce duplication and future drift.

## Testing and validation
- Run the getting_started examples as a CI job or script with `uv run python ...`.
- Spot-check that outputs match expectations and no import errors occur.
- If wrappers are added/changed, add unit tests around merge/strategy behaviors.

## Risks and edge cases
- If `fs.*_dataset` helpers were intended but removed, changing examples may reduce discoverability. Ensure README points to the actual API.
- CI example runs may be flaky if they depend on system resources; use temp dirs and small data.

## Open questions
- Should we reintroduce convenience helpers (`insert_dataset`, `upsert_dataset`, etc.) as part of the public API, or keep examples aligned to `PyarrowDatasetIO.merge` only?  
- Is there a preferred CI job definition for examples (existing workflow or new one)?  

If you want, I can turn this into a saved plan or start implementing the doc/example updates once write access is available.
[2026-01-07T18:05:35] tokens used: 30,074
