Receives via prompt:
Generates a final improvement report by synthesizing selected code changes and their validation results. Use this as the final step to produce a consolidated summary of all applied improvements with actionable next steps.
/plugin marketplace add abossenbroek/abossenbroek-claude-plugins/plugin install context-engineering@abossenbroek-claude-pluginsReceives via prompt:
plugin_path: Path to plugin directoryselected_improvement_ids: IDs of improvements user selected to applyNOT PROVIDED (context isolation):
Read Selected Improvements from State
python scripts/state_manager.py read "$plugin_path" --field mutable
Extract:
improvements (filter to selected IDs)grounding_results (filter to selected IDs)immutable (for scope metadata)Prepare Synthesis Input Build METADATA-tier input:
selected_improvements:
- improvement_id: CTX-001
description: [what was selected]
code_change:
before: [current code]
after: [improved code]
grounding_results:
pattern_compliant: true
token_estimate: [reduction data]
risk_level: LOW
scope_metadata:
plugin_name: [from analysis]
files_analyzed: [count]
improvements_available: [total count from state]
improvements_selected: [selected count]
focus_area: [from immutable state]
Launch Improvement Synthesizer
Task: Generate final improvement report
Agent: coordinator-internal/improvement-synthesizer.md
Prompt:
selected_improvements: [metadata above]
scope_metadata: [plugin context]
Return Report Directly Return the synthesizer's ImprovementReport UNCHANGED to coordinator.
improvement_report:
executive_summary:
improvements_applied: [count]
estimated_token_reduction: [total %]
patterns_improved: [list]
changes:
- file: [path]
change_type: [type]
before: [code]
after: [code]
next_steps:
- [actionable step]
mutable.improvements (selected subset)mutable.grounding_results (selected subset)immutable (plugin_path, focus_area, session_id)This phase operates at METADATA tier:
This represents a 97% context reduction from Phase 1 to Phase 6.
Use this agent when analyzing conversation transcripts to find behaviors worth preventing with hooks. Examples: <example>Context: User is running /hookify command without arguments user: "/hookify" assistant: "I'll analyze the conversation to find behaviors you want to prevent" <commentary>The /hookify command without arguments triggers conversation analysis to find unwanted behaviors.</commentary></example><example>Context: User wants to create hooks from recent frustrations user: "Can you look back at this conversation and help me create hooks for the mistakes you made?" assistant: "I'll use the conversation-analyzer agent to identify the issues and suggest hooks." <commentary>User explicitly asks to analyze conversation for mistakes that should be prevented.</commentary></example>