---
name: vera-ai-nlp-analyzing
description: >-
  Server-side extension that completes the full analysis pipeline for NLP
  text classification after vera-ai-nlp-testing has run. Adds SVM, Random
  Forest, LightGBM classifiers with TF-IDF and optional extra features,
  subgroup analysis by metadata or text properties, deep learning models
  (GRU, TextCNN, ALBERT with optional tabular fusion and hyperparameter
  search), cross-method comparison with unified feature importance on a 0-100
  scale, and manuscript-ready methods.md and results.md. Applies output
  variation and code style diversity for natural, non-repetitive output.
  Open-source skill. Triggered after vera-ai-nlp-testing completes,
  or direct API call with an NLP text classification task.
user-invocable: true
allowed-tools: Read, Bash, Write, Edit, Grep, Glob
---

# NLP Text Classification --- Full Analysis & Manuscript Generation

Open-source skill. Read `reference/specs/output-variation-protocol.md`
before every generation --- apply all variation layers for natural, diverse output.

## Workflow

Continues from where vera-ai-nlp-testing stopped (PART 0-2 done).

| Step | File | Executor | Output |
|---|---|---|---|
| Additional ML models | `workflow/04-run-additional-models.md` | Main Agent | PART 3 code + prose |
| Subgroup | `workflow/05-analyze-subgroups.md` | Main Agent | PART 4 code + prose |
| Deep learning | `workflow/06-fit-advanced-models.md` | Main Agent | PART 5 code + prose |
| Comparison | `workflow/07-compare-models.md` | Main Agent | PART 6 code + prose |
| Manuscript | `workflow/08-generate-manuscript.md` | Main Agent | methods.md + results.md |

## Additional Inputs

Collect if not already provided:
- Target discipline (for reporting conventions)
- Target journal or style (ACL, EMNLP, NeurIPS, etc.)
- Research question / hypothesis
- Subgroup variable or text property for stratification

## Output Structure

```
output/
├── methods.md
├── results.md
├── tables/             ← Markdown + CSV per table
├── figures/            ← PNGs, 300 DPI
├── references.bib
└── code.py             ← Style-varied
```

## Key References (read before generation)

| File | Purpose |
|---|---|
| `reference/specs/output-variation-protocol.md` | Output quality variation layers |
| `reference/specs/code-style-variation.md` | Seven-dimension code style diversity |
| `reference/patterns/sentence-bank.md` | 4-6 phrasings per result type |
| `reference/rules/reporting-standards.md` | Hard rules for ML/DL reporting |

## Reporting Standards

Same as vera-ai-nlp-testing, plus:
- All models: report F1 (weighted) and AUC (macro) with bootstrapped 95% CIs
- Deep learning: report training epochs, best epoch, learning rate, batch size
- ALBERT: report pre-trained model name, whether base was frozen
- Feature importance: unified 0-100 scale across ML and DL models
- Model comparison: frame as convergent findings, not horse race
- Tree-based with small N: frame as "exploratory"; never claim generalizability

## Cross-Skill Interface

```
Method Unit Contract:
├── code_python      → .py script (style-varied)
├── methods_md       → methods.md (varied structure)
├── results_md       → results.md (varied phrasing)
├── tables/          → Markdown + CSV
├── figures/         → PNGs 300 DPI (varied layout)
├── references_bib   → .bib with cited references
└── comparison       → cross-method narrative (in results.md)
```