analyze-data

name	analyze-data
description	Dispatch data-scientist to analyze your data or reproduce analysis. The agent runs Python (pandas, numpy, scipy, matplotlib, scikit-learn), produces figures/tables with reproducible scripts, and registers results as evidence cards. Gate: PROJECT.md complete.
disable-model-invocation	true
argument-hint	<path|task>

Analyze Your Data

This skill runs analysis on your data: exploratory statistics, model training/evaluation, figure production. All outputs are reproducible scripts + publication-ready figures/tables + evidence cards.

Language: every user-facing string below (gates, aborts, summaries) is written in Turkish, the kit's conversational default (CLAUDE.md). If the active project's PROJECT.md sets Conversation language: en, present the equivalent content in natural English — translate meaning and structure (headings, bullets, ✅/❌ markers), don't reproduce the Turkish text verbatim.

GATE: PROJECT.md Complete

Check: Read PROJECT.md. Verify NO TBD values in these fields:

• topic ✓
• rqs ✓
• type ✓
• output_language ✓

If any field is TBD:

❌ PROJECT.md tamamlanmamış. Eksik alan(lar):
  - [field1]
  - [field2]

Lütfen /new-project çalıştırarak PROJECT.md'yi doldurun.

ABORT and stop.

---

PROCEDURE

1. Parse Argument

User provides either:

• Path: /analyze-data data/vibration.csv

  • Trigger: exploratory data analysis (EDA)

• Task description: /analyze-data "train CNN model for fault detection"

  • Trigger: specific analysis task

2. Prepare Input for data-scientist Agent

Compose the subagent input contract:

Task: [from argument or ask user]
Data path(s): <paths under analysis/> or to be copied there
RQs from PROJECT.md: [list]
Target venue: [from PROJECT.md if known]
Requested outputs:
  - EDA: yes (if path given) / no
  - Model training: yes/no [ask if task is vague]
  - Figures: yes [always] — prefer PDF + PNG at 300 dpi
  - Tables: yes [always] — CSV + LaTeX/Excel format
  - Reproducibility: yes [always] — scripts + environment + seeds

Data dictionary (if available): [description of columns, units, missing-value policy]

Ask user for clarification:

Analiz türü:
1) Keşfedici veri analizi (EDA)
2) İstatistiksel testler (t-test, ANOVA, vb.)
3) Model eğitimi ve değerlendirmesi
4) Belirli görevler (hangi?)

Lütfen seçin veya görevin açıklamasını yazın:

Also ask:

Veri sözlüğü? (sütun adları, birimler, eksik değer politikası)
[input veya: hayır, ayrıntı yoktur]

Hedef dergi stil kuralları? (figür boyutu, renk şeması, vb.)
[input veya: varsayılan kullan]

3. Dispatch data-scientist Agent

Call with:

• Task (full description or data path + EDA request)
• RQs from PROJECT.md
• Data locations
• Style requirements

Expected return:

• File paths produced (scripts, figures, tables, captions)
• Numeric-results digest (headline numbers, max ~15 lines)
• Own-result card IDs created (EV-####)
• Reproducibility note (how to re-run)

4. Receive and Verify Results

Agent returns:

Files created:
  - analysis/01_eda.py (script)
  - analysis/figures/distribution.pdf (300 dpi)
  - analysis/figures/distribution.png (web-safe)
  - analysis/tables/summary_stats.csv
  - analysis/captions.md (figure/table captions)
  - analysis/RUNLOG.md (reproducibility info)

Results (headline digest):
  Dataset: N=1250 samples, p=15 features
  Missing: 2.3% (imputed via KNN)
  Class distribution: 60% normal, 40% faulty
  
  Model performance (10-fold CV, seeds 42–51):
    CNN: 94.2 ± 2.1% accuracy, F1=0.92 ± 0.03
    SVM: 89.5 ± 3.0% accuracy, F1=0.86 ± 0.05
  
  Ablation (CNN, removing components):
    - No attention: 91.8% → attention contributes 2.4%
    - No batch norm: 87.3% → batch norm contributes 6.9%

Card IDs created: EV-0087, EV-0088, EV-0089 (own-result type)

Reproducibility: `python analysis/01_eda.py` (seed 42) reproduces figures.

Verify own-result cards were registered:

python scripts/evidence_db.py query --type own-result

Check: do the new card IDs appear? Print count:

✅ Kanıt kartları kaydedildi: [N] yeni own-result kartı
   (EV-0087, EV-0088, EV-0089, ...)

5. Surface Numeric Digest & File Paths

Print to user:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
✅ Veri analizi tamamlandı!

Sonuçlar (özet):
───────────────────────────────────────────────────
Dataset:
  Örneklem sayısı: 1,250
  Özellik sayısı: 15
  Eksik değer: 2.3%
  Sınıf dağılımı: 60% normal, 40% arızalı

Model Performansı (10-katlı çapraz doğrulama):
  CNN:
    Doğruluk: 94.2 ± 2.1%
    F1 puanı: 0.92 ± 0.03
    Eğitim süresi: 2m 34s
  
  SVM (temel çizgi):
    Doğruluk: 89.5 ± 3.0%
    F1 puanı: 0.86 ± 0.05

Ayrıntı çalışması:
  Dikkat mekanizması: +2.4% iyileştirme
  Toplu normalizasyon: +6.9% iyileştirme

Yaratılan Kanıt Kartları:
  EV-0087: CNN modeli performansı
  EV-0088: Ayrıntı analizi sonuçları
  EV-0089: Toplu veri istatistikleri

Çıktı Dosyaları:
  Figürler:
    analysis/figures/learning_curve.pdf (300 dpi)
    analysis/figures/learning_curve.png (web)
    analysis/figures/confusion_matrix.pdf
    analysis/figures/confusion_matrix.png
    ... [full list]
  
  Tablolar:
    analysis/tables/model_comparison.csv
    analysis/tables/model_comparison.tex (LaTeX)
    analysis/tables/hyperparameters.csv
    analysis/tables/ablation_results.csv
    ... [full list]
  
  Betikler (yeniden üretilebilir):
    analysis/01_eda.py
    analysis/02_train_cnn.py
    analysis/03_ablation.py
    analysis/RUNLOG.md (ortam + komutlar)

Yeniden Üretim:
  python analysis/01_eda.py          # EDA'yı yeniden çalıştır
  python analysis/02_train_cnn.py    # CNN modelini yeniden eğit (seed 42–51)
  # Tam durum: analysis/RUNLOG.md dosyasına bakın
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

6. Citation Note

Remind user:

ℹ️ Results Bölümü Yazarken:
   - Bu figürler ve tablolar, literatür gibi kanıt kartları aracılığıyla alıntılanır.
   - Örnek: "CNN modeli ortalama 94.2% doğruluk elde etti [@EV-0087]."
   - Own-result kartları, dış kaynaklar gibi kaydedilir.

Yazı Örneği:
   "Ours, state-of-the-art CNN'ler ve geleneksel baseline'lar karşılaştırdık.
    Sonuçlar Tablo X'te gösterilmektedir [@EV-0088].
    CNN modeli 94.2% doğruluk elde ederken [@EV-0087], 
    SVM baseline 89.5% elde etti [@EV-0089]."

7. Next Steps Suggestion

Sonraki Adımlar:
  1. /draft results — Results bölümünü yazarken figür/tablo kartlarını kullanın
  2. /outline — iskelet henüz yoksa oluşturun; bu kartlar Results bölümüne bağlanacak
  3. /export-excel — analiz sonuçlarını Excel'e aktarın

---

GATES SUMMARY

Gate	Trigger	Action
PROJECT.md TBD fields	Yes	ABORT, explain which fields need filling
Data path not found	Yes	ABORT, ask user to verify path or copy data to analysis/
Analysis task too vague	Yes	WARN, ask for clarification (EDA? model? specific task?)

---

ARGUMENT EXAMPLES

/analyze-data data/vibration.csv
/analyze-data "train CNN for bearing fault detection"
/analyze-data analysis/experiment-results.csv
/analyze-data "compare 3 model architectures on balanced vs imbalanced data"

---

SCRIPT USAGE (by data-scientist agent, not by user)

Agent will use:

python analysis/01_eda.py          # reads from analysis/, writes figures + tables
python analysis/02_train_models.py # trains CNN, SVM, etc.; logs results
python scripts/evidence_db.py add --stdin  # register own-result cards

# User may re-run later:
python analysis/01_eda.py          # regenerates figures if data/code unchanged

---

TOKEN ECONOMY NOTES

• Subagent runs locally inside analysis/ directory.
• Verbose outputs (dataframes, training logs, tracebacks) die in subagent's context.
• Only numeric digest + file paths return (~200 tokens).
• Full reproducibility: scripts on disk can be re-run without subagent.

---

ERROR MESSAGES (Turkish)

❌ PROJECT.md tamamlanmamış. Lütfen /new-project çalıştırın.
❌ Veri dosyası bulunamadı: [path]. Lütfen dosya yolunu kontrol edin.
⚠️ Analiz görev açıklaması çok vague. Açıklayınız:
   1) EDA (keşfedici veri analizi)
   2) Model eğitimi
   3) Belirli görev (detay yazın)

---

FOLLOWING STEPS

After /analyze-data:

• User runs /draft results to write Results section, citing own-result cards.
• User may run /export-excel to create summary tables for presentation/reviewer.
• All figures/tables can be embedded in final paper via standard referencing mechanisms.