# Dataset Engineering Specifications
## Extracting Maximum Value from 52GB AI Coding Logs

---

## DATASET 1: AGENTIC TOOL USE DATASET
**Target: 50,000 high-quality multi-step sequences**

### Schema Definition

```json
{
  "id": "sha256_hash",
  "version": "1.0.0",
  "created_at": "2024-12-15T10:30:00Z",

  "metadata": {
    "conversation_id": "original_conv_id",
    "tool": "Claude Code|Cline|Roo-Cline",
    "project": "/path/to/project",
    "primary_language": "rust",
    "all_languages": ["rust", "typescript"],
    "timestamp_start": "ISO8601",
    "timestamp_end": "ISO8601",
    "duration_seconds": 1234
  },

  "task": {
    "user_request": "original user message",
    "inferred_goal": "structured goal description",
    "category": "debug|feature|refactor|setup|test",
    "complexity": "simple|moderate|complex|expert"
  },

  "initial_state": {
    "project_files": ["src/main.rs", "Cargo.toml"],
    "project_description": "extracted from context",
    "existing_errors": ["list of known issues"],
    "git_state": "clean|dirty|branch_info"
  },

  "trajectory": [
    {
      "step": 0,
      "user_message": "text or null for AI-driven steps",
      "ai_reasoning": "extracted thinking/planning",
      "ai_response": "text response to user",

      "tool_calls": [
        {
          "tool": "bash|read|write|edit|grep|glob|task",
          "parameters": {
            "command": "cargo build",
            "file_path": "/path/to/file",
            "pattern": "regex"
          },
          "result": {
            "success": true,
            "output": "tool output",
            "exit_code": 0,
            "error": null
          },
          "tokens_used": {"input": 100, "output": 50}
        }
      ],

      "state_changes": {
        "files_modified": ["src/main.rs"],
        "files_created": [],
        "files_deleted": [],
        "knowledge_gained": "discovered X uses Y pattern"
      },

      "step_outcome": "success|partial|failure|blocked"
    }
  ],

  "final_outcome": {
    "status": "completed|abandoned|failed",
    "success": true,
    "final_state": "description of end state",
    "artifacts_produced": ["list of outputs"],
    "total_steps": 15,
    "total_tool_calls": 47
  },

  "features": {
    "planning_quality": {
      "has_upfront_plan": true,
      "plan_steps": 5,
      "plan_followed": 0.8
    },

    "tool_usage": {
      "unique_tools": ["bash", "edit", "read"],
      "tool_call_count": 47,
      "tool_diversity_score": 0.6,
      "bash_commands": 12,
      "file_operations": 23
    },

    "complexity_metrics": {
      "files_touched": 8,
      "total_tokens": 45000,
      "context_switches": 3,
      "max_context_length": 12000,
      "multi_step_reasoning": true
    },

    "error_handling": {
      "errors_encountered": 5,
      "recovery_attempts": 5,
      "recovery_success_rate": 0.8,
      "retry_strategies": ["reread context", "alternative approach"]
    },

    "agent_capabilities": {
      "reads_before_write": true,
      "validates_changes": true,
      "uses_context_effectively": 0.9,
      "adapts_to_errors": true,
      "parallel_tool_calls": false
    }
  },

  "quality_score": {
    "overall": 0.85,
    "completeness": 0.9,
    "coherence": 0.8,
    "reusability": 0.85,
    "educational_value": 0.7
  },

  "annotations": {
    "key_decisions": ["decided to refactor instead of patch"],
    "interesting_patterns": ["used grep to find all usages"],
    "failure_points": ["initially tried wrong approach"],
    "learning_moments": ["discovered project uses async pattern"]
  }
}
```

### Quality Filters

**KEEP if ALL:**
- ✅ Total tool calls ≥ 5
- ✅ At least 2 different tool types used
- ✅ Has clear user request
- ✅ Has measurable outcome (success or instructive failure)
- ✅ No data corruption (valid JSON, complete messages)
- ✅ At least one file operation (read/write/edit)
- ✅ Tokens < 500k (avoid extreme outliers)

**REJECT if ANY:**
- ❌ Single tool call only
- ❌ Trivial tasks (e.g., "what is X?")
- ❌ Data corruption or truncation
- ❌ No clear task goal
- ❌ Spam or test conversations
- ❌ Contains PII that can't be sanitized

**TIERED QUALITY:**
- **Gold (20%)**: Multi-step, clear planning, successful, uses 4+ tools, >10 steps
- **Silver (40%)**: Clear task, 2-3 tools, 5-10 steps, may have failures
- **Bronze (40%)**: Simpler but valid examples, good for diversity

### Feature Extraction Pipeline

```python
def extract_agentic_features(conversation):
    features = {}

    # 1. PLANNING DETECTION
    features['has_plan'] = detect_planning_language(first_ai_message)
    features['plan_quality'] = measure_plan_specificity(plan)

    # 2. TOOL USE PATTERNS
    features['tool_sequence'] = extract_tool_call_sequence()
    features['tool_diversity'] = len(unique_tools) / total_tools
    features['parallel_tool_use'] = detect_parallel_patterns()

    # 3. CONTEXT MANAGEMENT
    features['context_window_usage'] = track_context_references()
    features['memory_efficiency'] = measure_redundant_reads()

    # 4. ERROR RECOVERY
    features['error_types'] = categorize_errors()
    features['recovery_strategy'] = identify_recovery_patterns()
    features['retry_intelligence'] = measure_adaptation()

    # 5. TASK DECOMPOSITION
    features['subtasks'] = identify_logical_subtasks()
    features['dependency_graph'] = build_task_dependencies()

    # 6. CODE UNDERSTANDING
    features['reads_before_writes'] = check_read_write_order()
    features['validates_changes'] = detect_verification_steps()

    return features
```

### Train/Val/Test Split Strategy

```
Total: 50,000 examples

STRATIFIED SPLIT by:
- Complexity (simple/moderate/complex/expert): 25/35/30/10%
- Tool diversity (low/medium/high): 33/34/33%
- Outcome (success/failure): 75/25%
- Language (top 5 languages + other)

SPLIT RATIOS:
├─ Train: 35,000 (70%)
├─ Validation: 7,500 (15%)
└─ Test: 7,500 (15%)

SPLIT RULES:
1. Entire conversations stay together (no turn splitting)
2. Same project conversations grouped to avoid leakage
3. Temporal holdout: Last 10% of data by time → test set
4. Diversity enforcement: Ensure rare tool combinations in all splits
5. Difficulty distribution maintained across splits
```

### Deduplication Strategy

```python
def deduplicate_trajectories():
    # Level 1: Exact duplicates
    remove_identical_hashes()

    # Level 2: Near-duplicates (edit distance)
    remove_similar_trajectories(threshold=0.95)

    # Level 3: Same project, same error, same fix
    remove_repetitive_debugging_sessions()

    # Level 4: Keep diverse examples
    if same_goal and same_outcome:
        keep_most_complex_trajectory()
```

### Quality Validation

```python
def validate_example(example):
    checks = {
        'has_valid_json': check_json_parseable(),
        'has_user_request': example['task']['user_request'] is not None,
        'has_trajectory': len(example['trajectory']) > 0,
        'tools_are_valid': all(t in VALID_TOOLS for t in tools),
        'has_outcome': example['final_outcome']['status'] in VALID_STATUSES,
        'no_pii': not contains_pii(example),
        'reasonable_length': 10 < len(trajectory) < 200,
        'coherent': check_conversation_coherence(example)
    }

    return all(checks.values()), checks
```

---

## DATASET 2: CODE DEBUGGING DATASET
**Target: 80,000 error-fix pairs**

### Schema Definition

```json
{
  "id": "sha256_hash",
  "version": "1.0.0",
  "created_at": "2024-12-15T10:30:00Z",

  "metadata": {
    "conversation_id": "parent_conv",
    "language": "rust",
    "framework": "tokio|react|django|null",
    "project": "/path/to/project",
    "timestamp": "ISO8601",
    "tool": "Claude Code|Cline|..."
  },

  "error": {
    "type": "compile_error|runtime_error|type_error|lint_error|test_failure",
    "source": "compiler|interpreter|linter|test_framework|runtime",
    "severity": "error|warning|info",

    "raw_output": "full error message with formatting",
    "parsed": {
      "error_code": "E0308|TS2345|ImportError",
      "message": "cleaned error message",
      "file": "src/main.rs",
      "line": 42,
      "column": 15,
      "suggestion": "compiler suggestion if any"
    },

    "stack_trace": "full stack trace if available",
    "related_errors": ["other errors that appeared together"],

    "category": {
      "primary": "type_mismatch|undefined_variable|syntax|logic|...",
      "secondary": ["borrow_checker", "lifetime"],
      "difficulty": "trivial|easy|medium|hard|expert"
    }
  },

  "context": {
    "problematic_code": {
      "file_path": "relative/path/to/file.rs",
      "line_start": 40,
      "line_end": 50,
      "code": "actual code block",
      "syntax_highlighted": "with language"
    },

    "surrounding_context": {
      "before": "20 lines before",
      "after": "20 lines after",
      "full_file_available": true
    },

    "dependencies": {
      "imports": ["use std::collections::HashMap"],
      "related_functions": ["function definitions used"],
      "type_definitions": ["relevant structs/types"]
    },

    "related_files": [
      {
        "path": "src/models.rs",
        "relevance": "defines type used here",
        "excerpt": "relevant snippet"
      }
    ],

    "project_context": {
      "build_tool": "cargo|npm|pip|...",
      "dependencies_file": "Cargo.toml content",
      "language_version": "rust 1.70",
      "target": "binary|library|..."
    }
  },

  "debugging_process": {
    "turns": [
      {
        "turn": 0,
        "user": "error message or question",
        "assistant": "analysis and suggestion",
        "actions_taken": ["read file", "check types"],
        "hypothesis": "I think the issue is X"
      }
    ],
    "total_turns": 3,
    "time_to_solve_seconds": 120
  },

  "solution": {
    "fix_type": "syntax_fix|type_annotation|refactor|logic_change|dependency_update",
    "approach": "direct_fix|workaround|refactor|upgrade",

    "code_changes": {
      "before": "problematic code",
      "after": "fixed code",
      "diff_unified": "unified diff format",
      "lines_added": 2,
      "lines_removed": 1,
      "lines_modified": 3
    },

    "explanation": {
      "root_cause": "detailed explanation of WHY error occurred",
      "fix_rationale": "WHY this fix works",
      "learning": "what to remember for future",
      "alternatives": ["other ways to fix this"]
    },

    "verification": {
      "method": "compilation|tests|manual_check",
      "compiled_successfully": true,
      "tests_passed": true,
      "user_confirmed_fix": true,
      "follow_up_errors": []
    }
  },

  "features": {
    "error_characteristics": {
      "is_common_error": true,
      "beginner_mistake": false,
      "language_specific": true,
      "framework_specific": false
    },

    "fix_characteristics": {
      "complexity": "simple|moderate|complex",
      "requires_understanding": ["type_system", "borrow_checker"],
      "requires_refactor": false,
      "one_line_fix": false,
      "breaking_change": false
    },

    "educational_value": {
      "teaches_concept": ["ownership", "lifetimes"],
      "common_pitfall": true,
      "has_clear_pattern": true,
      "reusable": 0.9
    }
  },

  "quality_score": {
    "overall": 0.88,
    "error_clarity": 0.9,
    "context_completeness": 0.85,
    "solution_quality": 0.9,
    "explanation_quality": 0.85,
    "verification": 1.0
  }
}
```

### Quality Filters

**KEEP if ALL:**
- ✅ Has clear error message
- ✅ Has before/after code
- ✅ Code actually changed (before ≠ after)
- ✅ Has some form of verification
- ✅ Error and fix are related
- ✅ Code is syntactically valid after fix
- ✅ No PII in code/paths

**REJECT if ANY:**
- ❌ No error message
- ❌ Before == After (no actual fix)
- ❌ Incomplete/truncated
- ❌ Not a real error (just questions)
- ❌ Fix unrelated to error
- ❌ Contains credentials/keys/secrets

**QUALITY TIERS:**
- **Gold (30%)**: Complete context, verified fix, great explanation, common error
- **Silver (50%)**: Good context, likely correct fix, decent explanation
- **Bronze (20%)**: Minimal but valid, for diversity

### Feature Extraction Pipeline

```python
def extract_debugging_features(error_fix_pair):
    features = {}

    # 1. ERROR CATEGORIZATION
    features['error_category'] = categorize_error_type(error.message)
    features['error_patterns'] = extract_error_patterns(error.raw_output)
    features['language_specific'] = detect_language_specific_error()

    # 2. ERROR DIFFICULTY
    features['difficulty'] = estimate_difficulty(
        turns=debugging_process.turns,
        code_complexity=analyze_code_complexity(),
        concept_depth=detect_concepts_required()
    )

    # 3. FIX COMPLEXITY
    features['fix_complexity'] = measure_fix_complexity(
        lines_changed=solution.lines_added + lines_removed,
        scope=detect_change_scope(),  # local/file/project
        breaking=is_breaking_change()
    )

    # 4. EDUCATIONAL VALUE
    features['teaches'] = extract_concepts_taught(explanation)
    features['common_pattern'] = is_common_error_pattern()
    features['reusability'] = estimate_reusability()

    # 5. CONTEXT QUALITY
    features['context_completeness'] = (
        has_full_file * 0.3 +
        has_dependencies * 0.2 +
        has_related_files * 0.2 +
        has_stack_trace * 0.3
    )

    return features
```

### Train/Val/Test Split Strategy

```
Total: 80,000 examples

STRATIFIED by:
- Language (rust/ts/python/go/...): actual distribution
- Error type (compile/runtime/type/...): 40/30/20/10%
- Difficulty (trivial/easy/medium/hard/expert): 10/25/35/20/10%
- Fix complexity (simple/moderate/complex): 40/40/20%

SPLIT RATIOS:
├─ Train: 56,000 (70%)
├─ Validation: 12,000 (15%)
└─ Test: 12,000 (15%)

SPECIAL TEST SETS:
├─ Seen errors, unseen context (6,000)
├─ Unseen error patterns (3,000)
└─ Hardest examples only (3,000)

LEAKAGE PREVENTION:
- Same error message → same split
- Same project → same split
- Temporal: earlier errors in train, later in test
```

### Deduplication Strategy

```python
def deduplicate_errors():
    # Level 1: Identical error + fix
    remove_exact_duplicates()

    # Level 2: Same error, similar fix (fuzzy match)
    cluster_by_error_message()
    keep_best_explanation_per_cluster()

    # Level 3: Repeated patterns in same project
    if same_project and same_error_type:
        keep_first_occurrence_plus_unique_variations()

    # Level 4: Language-specific common errors
    ensure_diversity_within_error_categories()
```

### Quality Validation

```python
def validate_error_fix(example):
    checks = {
        'has_error': example['error']['raw_output'] is not None,
        'has_code_before': example['context']['problematic_code'] is not None,
        'has_code_after': example['solution']['code_changes']['after'] is not None,
        'code_changed': before != after,
        'has_explanation': example['solution']['explanation']['root_cause'] is not None,
        'verified': example['solution']['verification']['compiled_successfully'],
        'no_pii': not contains_pii(example),
        'parseable': can_parse_code(before) and can_parse_code(after),
        'language_match': detected_language == metadata_language,
        'reasonable_diff': 1 <= lines_changed <= 1000
    }

    quality_score = compute_quality_score(example)

    return all(checks.values()) and quality_score > 0.6, checks
```

---

## DATASET 3: LONG-CONTEXT CONVERSATIONS
**Target: 1,500 ultra-long conversations**

### Schema Definition

```json
{
  "id": "sha256_hash",
  "version": "1.0.0",
  "created_at": "2024-12-15T10:30:00Z",

  "metadata": {
    "conversation_id": "original_id",
    "tool": "Claude Code",
    "project": "/path/to/project",
    "timestamp_start": "ISO8601",
    "timestamp_end": "ISO8601",
    "duration_hours": 19.3,
    "session_type": "single_session|multi_session"
  },

  "statistics": {
    "total_turns": 2487,
    "total_tokens": 8629763,
    "token_distribution": {
      "min_turn": 50,
      "max_turn": 15000,
      "mean_turn": 3470,
      "median_turn": 2100
    },
    "user_tokens": 2400000,
    "assistant_tokens": 6229763,
    "cumulative_context_used": [/* array of context sizes per turn */]
  },

  "messages": [
    {
      "turn": 0,
      "timestamp": "ISO8601",
      "role": "user|assistant",
      "content": "message text",

      "tool_calls": [/* if assistant */],
      "tool_results": [/* results */],

      "tokens": {
        "input": 5000,
        "output": 1200,
        "cumulative": 6200
      },

      "context_window": {
        "total_context": 50000,
        "new_input": 1200,
        "preserved_history": 48800,
        "dropped_history": 0
      },

      "references": {
        "references_turn": [5, 12, 45],  /* which previous turns are referenced */
        "references_files": ["src/main.rs"],
        "references_concepts": ["authentication", "JWT"]
      }
    }
  ],

  "conversation_structure": {
    "main_goal": "build complete authentication system",

    "phases": [
      {
        "phase": 0,
        "description": "planning and design",
        "turns": [0, 50],
        "outcome": "completed"
      },
      {
        "phase": 1,
        "description": "database schema implementation",
        "turns": [51, 200],
        "outcome": "completed"
      }
    ],

    "sub_tasks": [
      {
        "task_id": "t1",
        "description": "implement JWT token generation",
        "turns": [100, 150],
        "parent_task": null,
        "child_tasks": ["t1.1", "t1.2"],
        "completed": true
      }
    ],

    "context_switches": [
      {
        "turn": 200,
        "from_topic": "frontend",
        "to_topic": "backend",
        "reason": "needed to fix API first"
      }
    ],

    "knowledge_evolution": [
      {
        "turn": 50,
        "discovery": "project uses custom auth middleware",
        "impact": "changed approach to use existing patterns"
      }
    ]
  },

  "context_analysis": {
    "files_involved": {
      "total_unique": 45,
      "by_turn": [/* files introduced at each turn */],
      "most_referenced": ["src/auth.rs", "src/db.rs"]
    },

    "long_range_dependencies": [
      {
        "turn": 500,
        "references_turn": 50,
        "distance": 450,
        "content": "referred back to initial design decision"
      }
    ],

    "information_density": [
      /* per-turn score of new information vs repetition */
    ],

    "context_efficiency": {
      "unique_concepts_ratio": 0.75,
      "repetition_ratio": 0.25,
      "context_reuse_score": 0.8
    }
  },

  "features": {
    "conversation_characteristics": {
      "is_multi_session": true,
      "has_breaks": true,
      "maintains_coherence": 0.9,
      "topic_diversity": 0.7
    },

    "complexity_metrics": {
      "project_scope": "large",
      "files_modified": 45,
      "languages_used": ["rust", "sql", "typescript"],
      "frameworks": ["tokio", "diesel"],
      "architectural_decisions": 12
    },

    "problem_solving": {
      "obstacles_encountered": 23,
      "pivots_made": 5,
      "rollbacks": 3,
      "successful_completions": 18,
      "learning_moments": 15
    },

    "context_requirements": {
      "minimum_context_window": 32000,
      "optimal_context_window": 128000,
      "requires_rag": false,
      "benefits_from_long_context": true
    }
  },

  "quality_score": {
    "overall": 0.92,
    "coherence": 0.95,
    "completeness": 0.9,
    "educational_value": 0.9,
    "context_usage": 0.92,
    "task_success": 0.88
  },

  "annotations": {
    "summary": "comprehensive authentication system implementation",
    "key_challenges": ["database migration issues", "JWT expiration handling"],
    "solutions_discovered": ["custom middleware pattern", "refresh token rotation"],
    "architectural_insights": ["separation of auth from business logic"],
    "notable_techniques": ["multi-step planning", "incremental testing"]
  }
}
```

### Quality Filters

**KEEP if ALL:**
- ✅ Total tokens ≥ 100,000
- ✅ Total turns ≥ 50
- ✅ Maintains coherence (not random topic jumping)
- ✅ Has clear goal/project
- ✅ Multiple context switches
- ✅ Long-range dependencies exist
- ✅ Task has meaningful outcome

**REJECT if ANY:**
- ❌ Tokens < 100k
- ❌ Incoherent (spam, tests, corrupted)
- ❌ Single topic, no depth
- ❌ Abandoned very early
- ❌ Data corruption

**QUALITY TIERS:**
- **Diamond (10%)**: >1M tokens, multi-session, complex project, successful
- **Gold (30%)**: >500k tokens, coherent, multiple phases
- **Silver (40%)**: >200k tokens, single project, decent completion
- **Bronze (20%)**: >100k tokens, valid but simpler

### Feature Extraction Pipeline

```python
def extract_long_context_features(conversation):
    features = {}

    # 1. CONTEXT WINDOW ANALYSIS
    features['context_usage'] = analyze_context_window_usage(messages)
    features['long_range_deps'] = find_long_range_dependencies(messages)
    features['context_efficiency'] = measure_information_density()

    # 2. CONVERSATION STRUCTURE
    features['phases'] = segment_conversation_into_phases()
    features['task_graph'] = build_task_dependency_graph()
    features['topic_evolution'] = track_topic_changes()

    # 3. KNOWLEDGE TRACKING
    features['knowledge_building'] = track_cumulative_understanding()
    features['discoveries'] = identify_key_insights()
    features['pivots'] = detect_strategy_changes()

    # 4. CONTEXT SWITCHES
    features['switches'] = detect_context_switches()
    features['switch_reasons'] = classify_switch_reasons()

    # 5. FILE/CODE TRACKING
    features['file_timeline'] = track_file_introduction_and_usage()
    features['code_evolution'] = track_code_changes_over_time()

    # 6. PROBLEM SOLVING PATTERNS
    features['obstacle_resolution'] = identify_problems_and_solutions()
    features['planning_depth'] = measure_lookahead_planning()

    return features
```

### Train/Val/Test Split Strategy

```
Total: 1,500 conversations

STRATIFIED by:
- Token length: 100k-250k (30%), 250k-500k (30%), 500k-1M (25%), >1M (15%)
- Completion: completed (60%), partial (30%), abandoned (10%)
- Complexity: simple (20%), moderate (40%), complex (30%), expert (10%)
- Duration: single-session (40%), multi-session (60%)

SPLIT RATIOS:
├─ Train: 900 (60%)  # Smaller due to rarity
├─ Validation: 300 (20%)
└─ Test: 300 (20%)

SPECIAL CONSIDERATIONS:
- Entire conversations only (never split)
- Same project → same split
- Temporal holdout: last 20% by time → test
- Ensure token distribution balanced across splits
- Hold out some ultra-long (>5M) for special test set
```

### Deduplication Strategy

```python
def deduplicate_long_conversations():
    # Level 1: Exact duplicates (unlikely but check)
    remove_identical_conversations()

    # Level 2: Continuation conversations
    # If conv_B starts where conv_A ends, merge or keep one
    detect_and_merge_continuations()

    # Level 3: Same project, same goal
    if same_project and similar_goal:
        keep_longest_or_most_complete()

    # Level 4: Repetitive patterns
    if many_similar_turns:
        compress_or_flag_for_review()
```

### Quality Validation

```python
def validate_long_conversation(example):
    checks = {
        'meets_length': example['statistics']['total_tokens'] >= 100000,
        'has_messages': len(example['messages']) >= 50,
        'valid_json': all_messages_parseable(),
        'coherent': measure_conversation_coherence() > 0.7,
        'has_goal': example['conversation_structure']['main_goal'] is not None,
        'no_corruption': no_truncated_messages(),
        'no_pii': not contains_pii(example),
        'has_context_switches': len(context_switches) >= 2,
        'has_completion': has_meaningful_outcome(),
        'token_distribution_reasonable': token_stddev_not_too_high()
    }

    quality_score = compute_quality_score(example)

    return all(checks.values()) and quality_score > 0.7, checks
```

---

## CROSS-DATASET CONSIDERATIONS

### Unified Processing Pipeline

```
┌─────────────────────────────────────────────────────────┐
│ 1. RAW DATA INGESTION                                   │
│    ├─ Load 52GB backup                                  │
│    ├─ Parse JSONL files                                 │
│    └─ Basic validation                                  │
└─────────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────────┐
│ 2. CONVERSATION SEGMENTATION                            │
│    ├─ Identify conversation boundaries                  │
│    ├─ Extract metadata                                  │
│    └─ Link related conversations                        │
└─────────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────────┐
│ 3. PARALLEL EXTRACTION                                  │
│    ├─ Extract agentic sequences → Dataset 1             │
│    ├─ Extract error-fix pairs → Dataset 2               │
│    └─ Identify long conversations → Dataset 3           │
└─────────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────────┐
│ 4. FEATURE EXTRACTION                                   │
│    ├─ Language detection                                │
│    ├─ Complexity scoring                                │
│    ├─ Quality metrics                                   │
│    └─ Educational value                                 │
└─────────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────────┐
│ 5. QUALITY FILTERING                                    │
│    ├─ Apply dataset-specific filters                    │
│    ├─ Remove PII                                        │
│    ├─ Validate examples                                 │
│    └─ Assign quality tiers                              │
└─────────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────────┐
│ 6. DEDUPLICATION                                        │
│    ├─ Within-dataset dedup                              │
│    ├─ Cross-dataset overlap detection                   │
│    └─ Keep best examples                                │
└─────────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────────┐
│ 7. STRATIFIED SPLITTING                                 │
│    ├─ Create train/val/test splits                      │
│    ├─ Maintain distributions                            │
│    └─ Prevent leakage                                   │
└─────────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────────┐
│ 8. FORMAT CONVERSION                                    │
│    ├─ HuggingFace datasets format                       │
│    ├─ OpenAI JSONL format                               │
│    ├─ Anthropic format                                  │
│    └─ Research formats (Parquet, CSV)                   │
└─────────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────────┐
│ 9. DOCUMENTATION & METADATA                             │
│    ├─ Dataset cards                                     │
│    ├─ Statistics reports                                │
│    ├─ Example samples                                   │
│    └─ Usage guidelines                                  │
└─────────────────────────────────────────────────────────┘
```

### Global Quality Metrics

Track these across ALL datasets:

```python
QUALITY_METRICS = {
    # Completeness
    'field_completeness': 0.95,  # % of fields populated
    'no_null_critical_fields': 1.0,

    # Validity
    'json_parseable': 1.0,
    'schema_compliant': 1.0,
    'language_detected': 0.98,

    # Privacy
    'pii_removed': 1.0,
    'paths_sanitized': 1.0,
    'secrets_removed': 1.0,

    # Diversity
    'language_diversity': 0.8,  # coverage of languages
    'complexity_distribution': 'balanced',
    'outcome_diversity': 'includes failures',

    # Usefulness
    'educational_value': 0.7,
    'reusability': 0.75,
    'real_world_applicability': 0.9
}
```

### Output Formats

```
datasets/
├─ agentic_tool_use/
│  ├─ train.jsonl (35k examples)
│  ├─ val.jsonl (7.5k)
│  ├─ test.jsonl (7.5k)
│  ├─ dataset_card.md
│  ├─ statistics.json
│  └─ samples/ (100 examples for preview)
│
├─ code_debugging/
│  ├─ train.jsonl (56k)
│  ├─ val.jsonl (12k)
│  ├─ test.jsonl (12k)
│  ├─ by_language/
│  │  ├─ rust.jsonl
│  │  ├─ typescript.jsonl
│  │  └─ ...
│  ├─ by_difficulty/
│  │  ├─ beginner.jsonl
│  │  ├─ intermediate.jsonl
│  │  └─ expert.jsonl
│  └─ dataset_card.md
│
├─ long_context/
│  ├─ train.jsonl (900)
│  ├─ val.jsonl (300)
│  ├─ test.jsonl (300)
│  ├─ ultra_long/ (>1M tokens)
│  └─ dataset_card.md
│
├─ huggingface/
│  ├─ agentic_tool_use/ (HF format)
│  ├─ code_debugging/
│  └─ long_context/
│
├─ openai_finetune/
│  ├─ agentic_train.jsonl
│  ├─ debugging_train.jsonl
│  └─ ...
│
└─ statistics/
   ├─ global_stats.json
   ├─ quality_report.md
   └─ dataset_comparison.md
```

---

## IMPLEMENTATION PLAN

Want me to build this complete pipeline? I'll create:

1. **Enhanced `prepare` command** with all extractors
2. **Quality validation framework**
3. **Feature extraction modules**
4. **Stratified splitting logic**
5. **Deduplication engine**
6. **Format converters** (HF, OpenAI, Anthropic)
7. **Dataset statistics & visualization**
8. **Documentation generator**

This will give you production-grade datasets ready to:
- Train custom models
- Publish on HuggingFace
- License commercially
- Use for research

Ready to build?
