Executing Plans

Overview

Load plan, review critically with Shannon's quantitative lens, execute tasks in batches, run validation gates, report for review between batches.

Core principle: Batch execution with checkpoints + Shannon validation gates = systematic quality.

Announce at start: "I'm using the executing-plans skill to implement this plan."

The Process

Step 1: Load and Review Plan with Shannon Analysis

1. Read plan file (use forced-reading-protocol if >3000 lines)

2. Extract Shannon metadata:

metadata = {
    "complexity": extract_complexity_score(plan),  # 0.00-1.00
    "total_tasks": count_tasks(plan),
    "estimated_duration": extract_duration(plan),
    "validation_tiers": extract_validation_tiers(plan),
    "mcp_requirements": extract_mcps(plan),
    "domain_distribution": extract_domains(plan)
}

3. Review critically:

• Identify any questions or concerns about the plan
• Check if Shannon metadata is present
• Verify validation gates are specified
• Confirm MCP requirements are available

4. If concerns: Raise them with your human partner before starting

5. If no concerns: Create TodoWrite and proceed

Shannon tracking: Save plan review to Serena:

serena.write_memory(f"execution/{execution_id}/review", {
    "plan_id": plan_id,
    "metadata": metadata,
    "concerns": [],
    "ready_to_execute": True,
    "timestamp": ISO_timestamp
})

Step 2: Calculate Batch Size (Shannon Enhancement)

Complexity-based batch sizing:

def calculate_batch_size(plan_complexity: float) -> int:
    """
    Shannon formula: More complex = smaller batches
    
    complexity=0.1 → batch=5 (simple, can do many)
    complexity=0.5 → batch=3 (moderate, typical)
    complexity=0.9 → batch=1 (complex, one at a time)
    """
    base_size = max(1, min(5, int(10 * (1 - plan_complexity))))
    return base_size

# Example:
plan_complexity = 0.62  # From plan header
batch_size = calculate_batch_size(0.62)  # = 2 tasks per batch

Report to user:

Shannon batch sizing: Complexity 0.62 → 2 tasks per batch
Total batches: 9 (18 tasks / 2 per batch)

Step 3: Execute Batch

For each task in batch:

Mark as in_progress:

todo_write(task_id, status="in_progress")

Follow each step exactly (plan has bite-sized steps):

• Step 1: Write failing test (TDD RED)
• Step 2: Verify test fails
• Step 3: Implement minimal code (TDD GREEN)
• Step 4: Verify test passes
• Step 5: Run validation gates
• Step 6: Commit

Shannon enhancement: Track metrics per task:

task_metrics = {
    "task_id": task_id,
    "start_time": start_time,
    "end_time": end_time,
    "duration_minutes": duration,
    "validation_results": {
        "tier1": {"pass": True, "errors": 0},
        "tier2": {"pass": True, "tests": "12/12"},
        "tier3": {"pass": True, "no_mocks": True}
    },
    "files_changed": 3,
    "lines_added": 45,
    "commits": 1
}

serena.write_memory(f"execution/{execution_id}/task_{task_id}", task_metrics)

Run verifications as specified:

Tier 1 (Flow):

tsc --noEmit
ruff check .

Tier 2 (Artifacts):

npm test
npm run build

Tier 3 (Functional - NO MOCKS):

npm run test:e2e
# VERIFY: Tests use real systems (Puppeteer, real DB, etc.)

Shannon requirement: All tiers must pass before marking task complete.

Mark as completed:

todo_write(task_id, status="completed")

Step 4: Report (Shannon Quantitative Style)

When batch complete:

## Batch N Complete

**Shannon Metrics**:
- Tasks completed: 2/2
- Batch complexity: 0.58 (avg of task complexities)
- Duration: 45 minutes (estimated: 40 min, variance: +12%)
- Validation: 3/3 tiers PASS

**Tier 1 (Flow)**: ✅ PASS
- TypeScript: 0 errors
- Linter: 0 errors

**Tier 2 (Artifacts)**: ✅ PASS
- Tests: 24/24 pass (12 new tests added)
- Build: exit 0

**Tier 3 (Functional)**: ✅ PASS
- E2E tests: 6/6 pass
- NO MOCKS verified: ✅ (using Puppeteer + real database)

**Files Changed**:
- src/auth/middleware.ts (+67 lines)
- src/auth/validation.ts (+34 lines)
- tests/auth/middleware.test.ts (+89 lines)

**Commits**: 2
- feat: add auth middleware validation
- test: add middleware integration tests

**Ready for feedback.**

Shannon tracking: Save batch metrics:

batch_metrics = {
    "batch_id": batch_id,
    "tasks": [task_ids],
    "complexity": 0.58,
    "duration_minutes": 45,
    "estimated_minutes": 40,
    "variance_percent": 12.5,
    "validation_results": {tier_results},
    "files_changed": 3,
    "lines_added": 190,
    "commits": 2,
    "success": True
}

serena.write_memory(f"execution/{execution_id}/batch_{batch_id}", batch_metrics)

Step 5: Continue

Based on feedback:

• Apply changes if needed
• Execute next batch
• Repeat Steps 3-4 until complete

Shannon enhancement: Learn from feedback:

if feedback_requires_changes:
    # Track what type of feedback
    serena.write_memory(f"execution/{execution_id}/feedback_{batch_id}", {
        "feedback_type": "missing_validation" / "logic_error" / "style",
        "tasks_affected": [task_ids],
        "time_to_fix": 15  # minutes
    })

Step 6: Complete Development

After all tasks complete and verified:

Shannon final verification:

## Execution Complete - Shannon Verification

**Overall Metrics**:
- Total tasks: 18/18 ✅
- Total batches: 9
- Total duration: 7.5 hours (estimated: 8-10h, -12.5% under estimate)
- Avg batch duration: 50 minutes

**Validation Summary**:
- Tier 1: 18/18 tasks PASS
- Tier 2: 18/18 tasks PASS  
- Tier 3: 18/18 tasks PASS
- NO MOCKS: ✅ Verified (all tests use real systems)

**Code Metrics**:
- Files created: 12
- Files modified: 8
- Lines added: 1,247
- Lines removed: 34
- Commits: 18

**Complexity vs Actual**:
- Planned complexity: 0.62
- Actual complexity: 0.59 (simpler than expected)

**Risk Mitigation**:
- HIGH risks: Resolved (security patterns verified)
- MEDIUM risks: Resolved (session management tested)
- LOW risks: No issues

**MCP Usage**:
- puppeteer: 18 tasks (E2E testing)
- sequential: 3 tasks (security analysis)

**Ready for integration testing and deployment.**

Integration with other skills:

• REQUIRED SUB-SKILL: Use shannon:verification-before-completion
• Verify ALL tests pass
• Run complete validation suite
• Present deployment options

When to Stop and Ask for Help

STOP executing immediately when:

• Hit a blocker mid-batch (missing dependency, test fails, instruction unclear)
• Plan has critical gaps preventing starting
• You don't understand an instruction
• Verification fails repeatedly
• Batch exceeds estimated time by >50%

Ask for clarification rather than guessing.

Shannon tracking: Record blockers:

blocker = {
    "type": "missing_dependency" / "test_failure" / "unclear_instruction",
    "task_id": task_id,
    "batch_id": batch_id,
    "description": "...",
    "time_spent_before_blocker": 25,  # minutes
    "resolution": "awaiting_feedback"
}

serena.write_memory(f"execution/{execution_id}/blocker_{blocker_id}", blocker)

When to Revisit Earlier Steps

Return to Review (Step 1) when:

• Partner updates the plan based on your feedback
• Fundamental approach needs rethinking
• Discover plan is missing critical Shannon metadata

Don't force through blockers - stop and ask.

Shannon Enhancement: Wave Orchestration

For tasks that can run in parallel:

If plan indicates tasks are independent:

# Detect parallelizable tasks
parallel_tasks = find_independent_tasks(current_batch)

if len(parallel_tasks) > 1:
    # Use wave orchestration
    wave_result = execute_wave(parallel_tasks)
    # Still validate each task's results

Integration with wave-orchestration skill:

• Batch can contain sequential OR parallel execution
• Shannon's wave orchestration handles parallelization
• Each task still gets individual validation

Shannon Pattern Learning

After execution completes:

# Query similar executions
similar = serena.query_memory("execution/*/metadata:complexity~0.6")

# Learn patterns
patterns = {
    "avg_duration_for_complexity_0.6": average([e["duration"] for e in similar]),
    "common_blockers": most_common([b for e in similar for b in e["blockers"]]),
    "avg_variance": average([e["variance_percent"] for e in similar]),
    "recommendations": [
        "Complexity 0.6: Plan for 8 hours, batch size 2-3",
        "Common blocker: Missing test database - prepare in advance"
    ]
}

serena.write_memory(f"patterns/execution/complexity_0.6", patterns)

Use patterns for future estimates:

• More accurate time estimates
• Better batch sizing
• Proactive blocker prevention

Remember

• Review plan critically first
• Calculate batch size based on complexity (Shannon formula)
• Follow plan steps exactly
• Run validation gates per task (Shannon 3-tier)
• Don't skip verifications
• Verify NO MOCKS compliance
• Reference skills when plan says to
• Between batches: quantitative report and wait
• Stop when blocked, don't guess
• Track everything in Serena for pattern learning

Integration with Other Skills

This skill requires:

• writing-plans - Plan created by this skill
• forced-reading-protocol - If plan >3000 lines
• test-driven-development - For TDD steps
• verification-before-completion - Before claiming batch complete

This skill may use:

• wave-orchestration - For parallel task execution
• systematic-debugging - If tasks encounter bugs
• defense-in-depth - When adding validation layers

Shannon integration:

• Serena MCP - Track all execution metrics
• Sequential MCP - Deep analysis for complex decisions
• MCP requirements - Use MCPs specified in plan

The Bottom Line

Systematic execution + Shannon quantitative validation = reliable delivery.

Not "tasks completed" - "18/18 tasks complete, 3/3 validation tiers PASS, complexity 0.59, 7.5 hours (-12% under estimate), NO MOCKS verified".

Measure everything. Learn from patterns. Never skip validation.