# Bloop 🔍
### The Ruthless ML Auditor Agent
Bloop is a git-native AI agent built on the gitagent standard.
You give it a broken ML model. It tells you exactly where it fails,
why it fails, and what to do about it — in that order.
No sugarcoating. No generic advice. Just answers.
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## The Problem Bloop Solves
Every ML engineer has been here:
- Model is stuck at 87% accuracy
- You don't know if it's your data, your features, or your model
- You waste days trying random fixes
Bloop runs a structured 3-step audit and gives you a ranked action plan in seconds.
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## How It Works
Bloop uses 3 skills in sequence:
### 1. Segment Analysis
Finds exactly where the model fails — which classes, slices, or cohorts
have the worst performance. Surfaces critical failures (F1 < 0.5).
### 2. Root Cause Analysis
Diagnoses why it fails — class imbalance, label noise, feature leakage,
or distribution shift. Always backed by evidence.
### 3. Fix Generator
Produces a ranked action plan — each fix linked to a root cause,
with expected accuracy gain and effort level.
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## Demo
Set your Groq API key:
$env:GROQ\_API\_KEY="your-key"
Run Bloop:
node run.mjs
### Sample Output
Segment Analysis
\- Class 1 (positive): F1 = 0.60 — critical failure
\- Low lighting images: F1 = 0.45
\- Retinal hemorrhages: F1 = 0.48
Root Causes
1\. Class imbalance — 80% negative, 20% positive
2\. Label noise — low confidence scores in Class 1
3\. Distribution shift — train vs validation gap
Fix Plan
1\. Oversample with SMOTE → +2-3% accuracy, effort 6/10
2\. Label smoothing → +1-2% accuracy, effort 5/10
3\. Collect balanced data → +2-4% accuracy, effort 8/10
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## Agent Structure
bloop/
├── agent.yaml # Agent manifest
├── SOUL.md # Ruthless ML auditor personality
├── RULES.md # Hard constraints — never hallucinate metrics
├── run.mjs # Entry point
└── skills/
  ├── segment-analysis/ # Where does it fail?
  ├── root-cause/ # Why does it fail?
  └── fix-generator/ # How do we fix it?
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## Built With
- gitagent standard — git-native agent definition
- gitclaw — agent runtime
- Groq — llama-3.3-70b-versatile
- Node.js
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## Real World Impact
Bloop was built to solve a real problem — a diabetic retinopathy
detection XGBoost model plateaued at 87% accuracy. Bloop diagnosed
the exact issue in seconds: class imbalance in positive cases,
label noise in low-lighting images, and distribution shift.
Everyone trains models. Almost no one does deep failure analysis.
Bloop does.