# Processed Data Export

> Export plotting-ready processed research data from raw files, especially raw HDF5, into standardized CSV files with paired YAML metadata. Use when Codex needs to extract datasets such as time, temperature, power, voltage, or current from raw HDF5, compute derived plotting columns, create tidy long-form processed data, add traceability to source files/datasets/scripts, or convert existing processi…

- **Type:** Skill
- **Install:** `agentstack add skill-narroog-research-skills-processed-data-export`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Narroog](https://agentstack.voostack.com/s/narroog)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Narroog](https://github.com/Narroog)
- **Source:** https://github.com/Narroog/research-skills/tree/main/skills/processed-data-export

## Install

```sh
agentstack add skill-narroog-research-skills-processed-data-export
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Processed Data Export Skill

## Purpose

This skill creates plotting-ready processed data from raw research data.

The standard output is a matched pair:

```text
processed/
├── zth_curve.csv
└── zth_curve.yaml
```

The CSV is for Python, MATLAB, Origin, or other plotting programs. The YAML records provenance: source files, source datasets, processing script, creation time, column meanings, units, intended use, and processing history.

This skill does not draw figures and does not modify raw data.

## When to Use

Use this skill when the user asks to:

- extract selected datasets from raw HDF5 into CSV
- generate processed plotting data from raw data
- export curves such as time-temperature, Zth, IV, power, voltage, or current traces
- create YAML metadata for a processed CSV
- make plotting data traceable to a raw HDF5 file
- convert an existing processing script to write standardized CSV + YAML
- prepare tidy data for multi-curve plotting

Typical requests:

- "从 raw HDF5 里导出画图用 CSV"
- "把 /data/time_s 和 /data/temperature_K 提取出来"
- "生成 zth_curve.csv 和对应 metadata"
- "把这个处理脚本改成输出 processed CSV + YAML"
- "保证 processed data 可追溯到原始 HDF5"

## Scope Boundary

This skill is responsible for:

- reading raw source files, especially HDF5
- selecting and optionally transforming datasets
- writing processed CSV files
- writing paired YAML metadata files
- validating CSV/YAML consistency
- preserving traceability to raw data

This skill is not responsible for:

- plotting figures
- fitting physical models unless needed to produce an exported column
- changing numerical simulation algorithms
- modifying raw HDF5 files
- deleting or overwriting raw data

Never modify original raw files. Avoid overwriting processed files unless the user asks or the script has an explicit `overwrite=True` path.

## Recommended Project Structure

Prefer:

```text
project/
├── raw/
│   └── hdf5/
│       └── 2026-05-30_90nmMOSFET_pulse_10K_1mW_run001.h5
├── processed/
│   ├── zth_curve.csv
│   └── zth_curve.yaml
├── scripts/
│   └── process_zth.py
└── figures/
```

## Input HDF5 Convention

Default input is an HDF5 file from `raw/` or `raw/hdf5/`.

Common structure:

```text
/
├── data/
│   ├── time_s
│   ├── temperature_K
│   ├── power_W
│   ├── voltage_V
│   └── current_A
└── metadata/
```

Always allow the user or project code to specify actual dataset paths, for example:

```text
/data/time_s
/data/temperature_K
/data/power_W
```

## CSV Rules

CSV files must:

- use the first row as column names
- use clear physical quantity names
- include units in column names when practical
- use `snake_case`
- avoid spaces
- contain only plotting-ready values, labels, and identifiers

Good column names:

```text
time_s
time_us
temperature_K
delta_temperature_K
power_W
zth_K_per_W
voltage_V
current_A
device
condition
run_id
```

Avoid vague names:

```text
T
x
data
result
temp
new
```

## Tidy Data Preference

For multi-curve plotting, default to tidy long-form data unless the user explicitly requests wide-form output.

Recommended:

```csv
time_us,zth_K_per_W,device,condition,run_id
0.1,0.02,90nmMOSFET,10K_1mW,run001
1.0,0.05,90nmMOSFET,10K_1mW,run001
0.1,0.01,FinFET,10K_1mW,run002
1.0,0.03,FinFET,10K_1mW,run002
```

Avoid unless requested:

```csv
time_us,mosfet_zth,finfet_zth
0.1,0.02,0.01
1.0,0.05,0.03
```

## YAML Metadata Rules

Every processed CSV must have a same-stem YAML file:

```text
zth_curve.csv
zth_curve.yaml
```

The YAML must include at least:

```yaml
name:
description:
created_at:
created_by:
source_files:
source_datasets:
processing_script:
processing_steps:
columns:
intended_use:
notes:
```

Each entry in `columns` should record:

- description
- unit
- source

Use `source: computed` for derived columns and an HDF5 dataset path for copied or transformed raw datasets.

## Naming Rules

Name processed CSV files by figure or analysis purpose:

```text
zth_curve.csv
peak_temperature.csv
structure_comparison.csv
duty_cycle_sweep.csv
foster_fit_parameters.csv
cauer_network_parameters.csv
temperature_profile.csv
```

If a date or condition is needed:

```text
YYYY-MM-DD___.csv
```

Example:

```text
2026-05-30_zth_curve_90nmMOSFET_10K_1mW.csv
```

Avoid:

```text
data.csv
result.csv
new.csv
final.csv
```

## Workflow

1. Identify the raw source file and required dataset paths.
2. Inspect HDF5 keys and metadata before assuming names.
3. Decide processed columns and units.
4. Prefer tidy long-form layout for multi-condition or multi-device data.
5. Implement or adapt a processing script using `templates/export_processed_csv.py` when useful.
6. Write CSV and YAML together.
7. Validate that CSV columns match YAML `columns`.
8. Confirm raw files were not modified.

## Reusable Template

Use `templates/export_processed_csv.py` for deterministic CSV + YAML export utilities. It provides:

- `read_hdf5_datasets`
- `export_processed_csv_with_metadata`
- `validate_processed_pair`
- `make_processed_name`

For a minimal end-to-end HDF5 example, use:

```text
examples/minimal_hdf5_to_csv_example.py
```

For a synthetic raw HDF5 generator, use:

```text
examples/example_raw_data_generator.py
```

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Narroog](https://github.com/Narroog)
- **Source:** [Narroog/research-skills](https://github.com/Narroog/research-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-narroog-research-skills-processed-data-export
- Seller: https://agentstack.voostack.com/s/narroog
- Browse the marketplace: https://agentstack.voostack.com/browse

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