condition-cataloging
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/condition-catalogingExtract the complete set of experimental conditions under which a method was evaluated. This metadata is essential for determining whether two scores are directly comparable or require normalization.
SKILL.md
.github/skills/condition-catalogingView on GitHub ↗
---
name: condition-cataloging
description: Record evaluation conditions (data splits, hyperparams, hardware, seeds) from a paper
execution: subagent
prompt: ./prompt.md
input: paper_content, method_name
used-by: baseline-establishment
---
# Condition Cataloging
## Purpose
Extract the complete set of experimental conditions under which a method was evaluated. This metadata is essential for determining whether two scores are directly comparable or require normalization.
## Input Schema
| Field | Type | Description |
|-------|------|-------------|
| paper_content | string | Full paper text (markdown format) |
| method_name | string | The specific method to catalog conditions for |
## Output Schema
```json
{
"method": "string",
"conditions": {
"data": {
"dataset_version": "string",
"split": "string",
"preprocessing": "string",
"augmentation": "string",
"training_size": "string",
"external_data_used": false
},
"compute": {
"hardware": "string",
"gpu_count": null,
"training_time": "string",
"flops_estimate": "string"
},
"hyperparameters": {
"learning_rate": "string",
"batch_size": null,
"epochs": null,
"optimizer": "string",
"scheduler": "string",
"key_hyperparams": {}
},
"evaluation": {
"num_seeds": null,
"seed_values": [],
"ensemble": false,
"post_processing": "string",
"evaluation_protocol": "string"
},
"reproducibility": {
"code_available": false,
"code_url": "string",
"pretrained_model_available": false,
"full_config_provided": false
}
},
"missing_information": ["string"],
"completeness_score": 0.0
}
```