name	qwen_cleanup_strategist_prototype
description	Qwen Cleanup Strategist (Prototype)
version	1
author	0102_wre_team
agents	qwen
dependencies	pattern_memory, libido_monitor
domain	autonomous_operations

Qwen Cleanup Strategist (Prototype)

Metadata (YAML Frontmatter)

skill_id: qwen_cleanup_strategist_v1_prototype name: qwen_cleanup_strategist description: Strategic cleanup planning with WSP 15 MPS scoring (WSP 83/64 compliance) version: 1.0_prototype author: qwen_baseline_generator created: 2025-10-22 agents: [qwen] primary_agent: qwen intent_type: DECISION promotion_state: prototype pattern_fidelity_threshold: 0.90 test_status: needs_validation

MCP Orchestration

mcp_orchestration: true breadcrumb_logging: true owning_dae: doc_dae execution_phase: 2 previous_skill: gemma_noise_detector_v1_prototype next_skill: 0102_cleanup_validator

Input/Output Contract

inputs:

data/gemma_noise_labels.jsonl: "Gemma's labeled files"
total_files_scanned: "Count of files analyzed"
noise_count: "Files labeled as noise"
signal_count: "Files labeled as signal" outputs:
data/cleanup_plan.json: "Strategic cleanup plan with MPS scores"
execution_id: "Unique execution identifier for breadcrumb tracking"

Dependencies

dependencies: data_stores: - name: gemma_noise_labels type: jsonl path: data/gemma_noise_labels.jsonl mcp_endpoints: - endpoint_name: holo_index methods: [wsp_protocol_lookup] throttles: [] required_context: - gemma_labels: "JSONL file with Gemma's noise classifications" - total_files_scanned: "Count of files Gemma analyzed" - noise_count: "Count of files labeled as noise" - signal_count: "Count of files labeled as signal"

Metrics Configuration

metrics: pattern_fidelity_scoring: enabled: true frequency: every_execution scorer_agent: gemma write_destination: modules/infrastructure/wre_core/recursive_improvement/metrics/qwen_cleanup_strategist_fidelity.json promotion_criteria: min_pattern_fidelity: 0.90 min_outcome_quality: 0.85 min_execution_count: 100 required_test_pass_rate: 0.95

Qwen Cleanup Strategist

Purpose: Strategic cleanup planning based on Gemma's file classifications, applying WSP 83/64 rules to group files and generate safe cleanup plans

Intent Type: DECISION

Agent: qwen (1.5B, 200-500ms inference, 32K context)

Task

You are Qwen, a strategic planner. Your job is to read Gemma's file labels (labels.jsonl) and create a safe, organized cleanup plan. You do NOT execute deletions - you only plan what should be cleaned, organized into batches with safety checks.

Key Capability: You are a 1.5B parameter model capable of:

Multi-step reasoning (group files by category)
Strategic planning (batch similar operations)
WSP protocol application (reference WSP 83/64 for safety)
Pattern analysis (identify cleanup opportunities)

Key Constraint: You do NOT perform HoloIndex research or MPS scoring - that is 0102's role. You work with Gemma's labeled data to create strategic groupings.

Instructions (For Qwen Agent)

1. LOAD GEMMA LABELS

Rule: Read all lines from data/gemma_noise_labels.jsonl and parse into structured list

Expected Pattern: labels_loaded=True

Steps:

Open data/gemma_noise_labels.jsonl file
Read all lines (JSONL format - one JSON object per line)
Parse each line into dictionary
Validate schema: {"file_path", "label", "category", "confidence"} fields present
Count totals: total_files, noise_count, signal_count
Log: {"pattern": "labels_loaded", "value": true, "total_files": N, "noise_count": M, "signal_count": K}

Examples:

✅ Loaded 219 files: 173 noise, 46 signal → {"labels_loaded": true, "total": 219}
❌ File not found → {"labels_loaded": false, "error": "File not found"}

2. FILTER BY CONFIDENCE

Rule: Only include noise files with confidence >= 0.85 in cleanup plan

Expected Pattern: confidence_filter_applied=True

Steps:

Filter labels list: noise_files = [f for f in labels if f['label'] == 'noise' and f['confidence'] >= 0.85]
Count low-confidence files: low_conf = [f for f in labels if f['label'] == 'noise' and f['confidence'] < 0.85]
Exclude low-confidence from cleanup plan (send to 0102 for manual review)
Log: {"pattern": "confidence_filter_applied", "value": true, "high_conf_count": N, "low_conf_count": M}

Examples:

✅ 173 noise files → 145 high-confidence (≥0.85), 28 low-confidence (<0.85)
❌ All files low-confidence → No cleanup plan generated

WSP Reference: WSP 64 (Violation Prevention) - Prefer caution over aggressive cleanup

3. GROUP BY CATEGORY

Rule: Group high-confidence noise files by Gemma's category field

Expected Pattern: files_grouped_by_category=True

Steps:

Create dictionary: groups = {}
For each high-confidence noise file:
- category = file['category']
- groups[category].append(file)
Sort categories by file count (descending)
Log: {"pattern": "files_grouped_by_category", "value": true, "category_count": len(groups), "categories": list(groups.keys())}

Example Output:

{
  "file_type_noise": [
    {"file_path": "chat_history.jsonl", "confidence": 0.95},
    {"file_path": "debug.log", "confidence": 0.95}
  ],
  "rotting_data": [
    {"file_path": "old_chat.jsonl", "confidence": 0.85}
  ],
  "backup_file": [
    {"file_path": "main.py.backup", "confidence": 0.90}
  ]
}

4. APPLY WSP 83/64 SAFETY RULES

Rule: Apply WSP safety constraints to each category group

Expected Pattern: wsp_safety_rules_applied=True

WSP 83 (Documentation Attached to Tree):

Check: Are any files in docs/, WSP_framework/, README.md, INTERFACE.md, ModLog.md?
Action: If found → EXCLUDE from cleanup, flag for 0102 review

WSP 64 (Violation Prevention):

Check: Are any files in critical paths (data/, modules/*/src/, .env)?
Action: If found → EXCLUDE from cleanup, flag as false positive

Steps:

For each category group:
- Check if any files match WSP 83 patterns (docs, WSP protocols)
- Check if any files match WSP 64 patterns (critical paths)
- If violations found → Remove from cleanup group, add to flagged_for_review
Log: {"pattern": "wsp_safety_rules_applied", "value": true, "violations_found": N, "flagged_count": M}

Examples:

✅ Found docs/temp_analysis.md in backup_file group → Flagged for review
✅ Found data/old_cache.jsonl in rotting_data → Flagged for review
❌ All files safe → No violations

5. CREATE BATCHES

Rule: Split category groups into batches of max 50 files each (safety limit)

Expected Pattern: batches_created=True

Steps:

For each category group with > 50 files:
- Split into batches: batch_1, batch_2, etc.
- Each batch max 50 files
Assign batch priority:
- file_type_noise: P1 (safe, obvious clutter)
- rotting_data: P2 (requires age verification)
- backup_file: P1 (safe if no critical paths)
- noise_directory: P1 (safe, entire directories)
Log: {"pattern": "batches_created", "value": true, "total_batches": N}

Example Output:

{
  "batch_001": {
    "category": "file_type_noise",
    "priority": "P1",
    "file_count": 50,
    "total_size_bytes": 125000000,
    "files": ["chat_history_001.jsonl", "chat_history_002.jsonl", ...]
  },
  "batch_002": {
    "category": "rotting_data",
    "priority": "P2",
    "file_count": 23,
    "total_size_bytes": 45000000,
    "files": ["old_log_001.jsonl", "old_log_002.jsonl", ...]
  }
}

6. APPLY WSP 15 MPS SCORING

Rule: Calculate Module Prioritization Score for each batch using WSP 15 formula

Expected Pattern: mps_scoring_applied=True

WSP 15 Formula: MPS = Complexity + Importance + Deferability + Impact (each 1-5)

Steps:

For each batch, calculate 4 dimensions:

Complexity (1-5) - How difficult is cleanup?

if batch['file_count'] <= 10:
    complexity = 1  # Trivial
elif batch['file_count'] <= 50:
    complexity = 2  # Low
elif batch['file_count'] <= 100:
    complexity = 3  # Moderate
elif batch['file_count'] <= 200:
    complexity = 4  # High
else:
    complexity = 5  # Very High

Importance (1-5) - How essential is cleanup?

if 'concurrency risk' in batch['rationale'].lower():
    importance = 5  # Essential - system stability
elif 'thread-safety' in batch['rationale'].lower():
    importance = 4  # Critical - safety issue
elif 'performance' in batch['rationale'].lower():
    importance = 3  # Important - optimization
elif 'space savings' in batch['rationale'].lower():
    importance = 2  # Helpful - clutter reduction
else:
    importance = 1  # Optional

Deferability (1-5) - How urgent is cleanup?

if batch['risk_level'] == 'HIGH':
    deferability = 5  # Cannot defer
elif batch['risk_level'] == 'MEDIUM':
    deferability = 3  # Moderate urgency
elif batch['risk_level'] == 'LOW':
    deferability = 2  # Can defer
else:
    deferability = 1  # Highly deferrable

Impact (1-5) - What value does cleanup deliver?

space_saved_mb = batch['total_size_mb']
if space_saved_mb > 500:
    impact = 5  # Transformative (500+ MB)
elif space_saved_mb > 200:
    impact = 4  # Major (200-500 MB)
elif space_saved_mb > 50:
    impact = 3  # Moderate (50-200 MB)
elif space_saved_mb > 10:
    impact = 2  # Minor (10-50 MB)
else:
    impact = 1  # Minimal (<10 MB)

Calculate MPS: mps = complexity + importance + deferability + impact
Determine priority:
- MPS 16-20 → P0 (Critical - Autonomous execution)
- MPS 13-15 → P1 (High - Autonomous execution)
- MPS 10-12 → P2 (Medium - Requires approval)
- MPS 7-9 → P3 (Low - Defer)
- MPS 4-6 → P4 (Backlog - Skip)
Add MPS scoring to batch metadata
Log: {"pattern": "mps_scoring_applied", "value": true, "batches_scored": N}

Example Output:

{
  "batch_001": {
    "category": "file_type_noise",
    "file_count": 145,
    "total_size_mb": 119,
    "mps_scoring": {
      "complexity": 3,
      "complexity_reason": "Moderate - 145 files requires batching",
      "importance": 5,
      "importance_reason": "Essential - concurrency risk affects stability",
      "deferability": 2,
      "deferability_reason": "Deferrable - low risk allows delay",
      "impact": 4,
      "impact_reason": "Major - 119 MB saved, clutter reduction",
      "mps_total": 14,
      "priority": "P1",
      "qwen_decision": "AUTONOMOUS_EXECUTE",
      "qwen_confidence": 0.90
    }
  }
}

7. GENERATE CLEANUP PLAN

Rule: Output structured cleanup plan with batches, safety checks, and rationale

Expected Pattern: cleanup_plan_generated=True

Steps:

Create JSON structure:

{
  "plan_id": "cleanup_plan_20251022_015900",
  "timestamp": "2025-10-22T01:59:00Z",
  "total_files_scanned": 219,
  "noise_high_confidence": 145,
  "noise_low_confidence": 28,
  "signal_files": 46,
  "batches": [...],
  "flagged_for_review": [...],
  "safety_checks_passed": true,
  "wsp_compliance": ["WSP_83", "WSP_64"],
  "requires_0102_approval": true
}

Write to data/cleanup_plan.json
Log: {"pattern": "cleanup_plan_generated", "value": true, "plan_id": "cleanup_plan_..."}

7. GENERATE RATIONALE

Rule: For each batch, provide strategic reasoning for cleanup

Expected Pattern: rationale_generated=True

Steps:

For each batch, generate rationale:

{
  "batch_id": "batch_001",
  "category": "file_type_noise",
  "rationale": "215 JSONL files scattered across modules create high concurrency risk (chat_history files). Gemma classified 145 as high-confidence noise (0.95+ confidence). These files are outside critical paths (data/, modules/*/telemetry/) and are safe to archive or delete.",
  "recommendation": "ARCHIVE to archive/noise_cleanup_20251022/ before deletion",
  "risk_level": "LOW",
  "estimated_space_saved_mb": 119
}

Reference WSP protocols in rationale (e.g., "WSP 64 compliance verified")
Log: {"pattern": "rationale_generated", "value": true, "batches_with_rationale": N}

Expected Patterns Summary

Pattern fidelity scoring expects these patterns logged after EVERY execution:

{
  "execution_id": "exec_qwen_001",
  "skill_id": "qwen_cleanup_strategist_v1_prototype",
  "patterns": {
    "labels_loaded": true,
    "confidence_filter_applied": true,
    "files_grouped_by_category": true,
    "wsp_safety_rules_applied": true,
    "batches_created": true,
    "mps_scoring_applied": true,
    "cleanup_plan_generated": true,
    "rationale_generated": true
  },
  "total_batches": 5,
  "total_files_in_plan": 145,
  "flagged_for_review": 28,
  "execution_time_ms": 420
}

Fidelity Calculation: (patterns_executed / 8) - All 8 checks should run every time

Output Contract

Format: JSON file written to data/cleanup_plan.json

Schema:

{
  "plan_id": "cleanup_plan_20251022_015900",
  "timestamp": "2025-10-22T01:59:00Z",
  "agent": "qwen_cleanup_strategist",
  "version": "1.0_prototype",

  "summary": {
    "total_files_scanned": 219,
    "noise_high_confidence": 145,
    "noise_low_confidence": 28,
    "signal_files": 46,
    "total_batches": 5,
    "estimated_space_saved_mb": 210
  },

  "batches": [
    {
      "batch_id": "batch_001",
      "category": "file_type_noise",
      "priority": "P1",
      "file_count": 50,
      "total_size_bytes": 125000000,
      "files": ["O:/Foundups-Agent/chat_history_001.jsonl", "..."],
      "rationale": "215 JSONL files create concurrency risk...",
      "recommendation": "ARCHIVE to archive/noise_cleanup_20251022/",
      "risk_level": "LOW",
      "wsp_compliance": ["WSP_64"]
    }
  ],

  "flagged_for_review": [
    {
      "file_path": "O:/Foundups-Agent/docs/temp_analysis.md",
      "category": "backup_file",
      "confidence": 0.90,
      "flag_reason": "WSP_83 violation - documentation file",
      "requires_0102_review": true
    }
  ],

  "safety_checks": {
    "wsp_83_documentation_check": "PASSED",
    "wsp_64_critical_path_check": "PASSED",
    "confidence_threshold_check": "PASSED",
    "batch_size_limit_check": "PASSED"
  },

  "requires_0102_approval": true,
  "next_step": "0102 validates plan with HoloIndex research + WSP 15 MPS scoring"
}

Destination: data/cleanup_plan.json

Benchmark Test Cases

Test Set 1: Confidence Filtering (5 cases)

Input: 100 noise files, all confidence 0.95 → Expected: All 100 in cleanup plan (Reason: High confidence)
Input: 100 noise files, 50 at 0.95, 50 at 0.70 → Expected: 50 in plan, 50 flagged for review (Reason: Confidence threshold)
Input: 100 noise files, all confidence 0.80 → Expected: 0 in plan, 100 flagged (Reason: Below threshold)
Input: 0 noise files → Expected: Empty plan (Reason: No cleanup needed)
Input: 200 signal files → Expected: Empty plan (Reason: No noise detected)

Test Set 2: WSP Safety Rules (5 cases)

Input: docs/temp.md (noise, backup_file, 0.90) → Expected: Flagged for review (Reason: WSP 83 - docs)
Input: data/old_cache.jsonl (noise, rotting_data, 0.85) → Expected: Flagged for review (Reason: WSP 64 - critical path)
Input: .env.backup (noise, backup_file, 0.90) → Expected: Flagged for review (Reason: WSP 64 - credentials)
Input: modules/livechat/src/temp.py (noise, backup_file, 0.90) → Expected: Flagged for review (Reason: WSP 64 - source code)
Input: temp/scratch.txt (noise, file_type_noise, 0.95) → Expected: In cleanup plan (Reason: No WSP violations)

Test Set 3: Category Grouping (5 cases)

Input: 100 JSONL files (file_type_noise) → Expected: 1 category group, 2 batches (50 each) (Reason: Split by batch limit)
Input: 30 rotting_data, 20 backup_file, 10 noise_directory → Expected: 3 category groups (Reason: Different categories)
Input: 200 file_type_noise files → Expected: 4 batches of 50 each (Reason: Max batch size)
Input: Mixed categories, all < 50 files → Expected: N batches (1 per category) (Reason: No splitting needed)
Input: Empty input → Expected: 0 batches (Reason: No files to group)

Test Set 4: Batch Priority Assignment (5 cases)

Input: file_type_noise category → Expected: Priority P1 (Reason: Safe, obvious clutter)
Input: rotting_data category → Expected: Priority P2 (Reason: Requires age verification)
Input: backup_file category → Expected: Priority P1 (Reason: Safe if no critical paths)
Input: noise_directory category → Expected: Priority P1 (Reason: Entire directories safe)
Input: Mixed categories → Expected: Batches sorted by priority (P1 first) (Reason: Strategic ordering)

Test Set 5: Rationale Generation (5 cases)

Input: 215 JSONL files → Expected: Rationale mentions "concurrency risk" (Reason: Thread-safety concern)
Input: 50 backup files → Expected: Rationale mentions "redundant backups" (Reason: Cleanup justification)
Input: 30 rotting_data files → Expected: Rationale mentions "old data" and age (Reason: Time-based cleanup)
Input: Mixed categories → Expected: Each batch has unique rationale (Reason: Context-specific reasoning)
Input: Flagged files → Expected: Flag reason references WSP protocol (Reason: Compliance documentation)

Total: 25 test cases across 5 categories

Success Criteria

✅ Pattern fidelity ≥ 90% (all 7 steps execute every time)
✅ Outcome quality ≥ 85% (correct grouping and batching)
✅ Zero false negatives on WSP violations (no critical files in cleanup plan)
✅ All flagged files have clear WSP reference (WSP 83 or WSP 64)
✅ Batch size never exceeds 50 files (safety limit)
✅ All batches have rationale with strategic reasoning
✅ Inference time < 500ms (Qwen 1.5B optimization)

Safety Constraints

NEVER INCLUDE IN CLEANUP PLAN:

Files in data/ directory (especially foundup.db)
Files in modules/*/src/ (source code)
Files in WSP_framework/src/ (WSP protocols)
Documentation files (docs/, *.md)
Configuration files (requirements.txt, .env, pyproject.toml)

ALWAYS FLAG FOR 0102 REVIEW:

Files with confidence < 0.85
Files matching WSP 83/64 patterns
Files in ambiguous categories
Large files (>100MB) before deletion

When in doubt → FLAG FOR REVIEW (safe default)

Next Phase

After 100 executions with ≥90% fidelity:

Promote to staged for extended testing
0102 reads cleanup_plan.json for validation
0102 performs HoloIndex research + WSP 15 MPS scoring
0102 approves or modifies plan
WRE executes approved cleanup batches

WSP References

WSP 83: Documentation Attached to Tree (never delete docs without review)
WSP 64: Violation Prevention (check critical paths before cleanup)
WSP 15: Module Prioritization Scoring (0102 uses this for approval)
WSP 50: Pre-Action Verification (no duplication, verify safety)

qwen_cleanup_strategist_prototype

Install Skill

SKILL.md

Qwen Cleanup Strategist (Prototype)

Metadata (YAML Frontmatter)

MCP Orchestration

Input/Output Contract

Dependencies

Metrics Configuration

Qwen Cleanup Strategist

Task

Instructions (For Qwen Agent)

1. LOAD GEMMA LABELS

2. FILTER BY CONFIDENCE

3. GROUP BY CATEGORY

4. APPLY WSP 83/64 SAFETY RULES

5. CREATE BATCHES

6. APPLY WSP 15 MPS SCORING

7. GENERATE CLEANUP PLAN

7. GENERATE RATIONALE

Expected Patterns Summary

Output Contract

Benchmark Test Cases

Test Set 1: Confidence Filtering (5 cases)

Test Set 2: WSP Safety Rules (5 cases)

Test Set 3: Category Grouping (5 cases)

Test Set 4: Batch Priority Assignment (5 cases)

Test Set 5: Rationale Generation (5 cases)

Success Criteria

Safety Constraints

Next Phase

WSP References