Archive Launcher

Archive completed orchestrations with full data extraction, performance analysis, lessons-learned capture, and template generation for future reference and organizational learning.

When to Use This Command

Run this command when an orchestration project is complete and ready to be preserved for historical analysis and future planning.

• You completed a multi-agent orchestration and want to preserve all metrics, decisions, and outcomes
• You need to extract lessons learned and performance patterns for future project estimation
• You want to create reusable templates from successful orchestration patterns
• You are cleaning up completed projects while maintaining searchable historical data

Use it also when:

• You need to compare current project metrics against archived historical data
• You want to auto-archive all completed orchestrations on a scheduled basis

Quick Start

Copy
# .claude/commands/archive-launcher.md
name: archive-launcher
description: Archive completed orchestrations with analysis
arguments:
  options: Archive mode and target specification

Copy
# Archive a specific completed orchestration with analysis
claude archive-launcher "--date 03_15_2026 --project auth_system --analyze"

Expected output:
Pre-Archive Analysis:
  Total Tasks: 24 (24 completed, 0 active)
  Duration: 8 days (estimated: 6 days)
  Velocity: 3.0 tasks/day
  Quality Score: 92%

Archived to: archived-orchestrations/2026/Q1/03_15_2026_auth_system/
  ARCHIVE-SUMMARY.md
  LESSONS-LEARNED.md
  METRICS-REPORT.json
  original-files/ (preserved)
  analytics/ (generated)

Core Concepts

Concept	Description
Orchestration	A multi-agent project with tasks, dependencies, and coordination
Pre-Archive Analysis	Completeness and quality check before archiving
Metrics Report	Quantitative data: velocity, duration, quality scores
Lessons Learned	Qualitative insights: what worked, what to improve
Template Extraction	Converting successful patterns into reusable orchestration templates

Archive Lifecycle:
  Complete ──> Pre-Analysis ──> Data Extraction ──> Archive
                                     │
                    ┌────────────────┼────────────────┐
                    v                v                v
               Metrics        Lessons Learned    Templates
               Report         Document           for Reuse
                    │                │                │
                    v                v                v
              archived-orchestrations/{year}/{quarter}/

Configuration

Parameter	Default	Description
date	auto-detect	Orchestration date identifier
project	auto-detect	Project name for the orchestration
analyze	false	Run deep analysis before archiving
format	standard	Archive format: standard, light, research, template
location	./archived-orchestrations/	Archive storage directory

Best Practices

1. Archive regularly, not in bulk -- Process completed orchestrations promptly while context is fresh. Batch archiving months later loses valuable tacit knowledge.

2. Always run with --analyze for non-trivial projects -- The analysis extracts estimation accuracy, team performance, and bottleneck patterns that improve future planning.

3. Create templates from successful patterns -- Use --format template to convert a well-executed orchestration into a reusable starting point for similar future projects.

4. Share lessons learned with the team -- The extracted lessons are most valuable when reviewed by the team. Schedule a brief retrospective around the archive output.

5. Use consistent naming conventions -- Follow the {date}_{project_name} pattern for archive directories to enable chronological browsing and keyword searching.

Common Issues

1. Orchestration has active tasks -- The archive process requires all tasks to be completed. Resolve or cancel remaining active tasks before archiving.

2. Git branch correlation fails -- If branches were deleted before archiving, the git correlation report will be incomplete. Archive before cleaning up feature branches.

3. Large archive size -- Use --format light for simple orchestrations that do not need full analytics visualizations, reducing storage requirements significantly.