Release v1.7.12

🎯 Highlights

This release optimizes the orchestration workflow for Claude Sonnet 4.5, addressing cognitive pattern changes in the new model to ensure reliable agent execution flow.

🔄 Model Adaptation

Claude Sonnet 4.5 Optimization

• Adapted to new cognitive patterns in Claude Sonnet 4.5 (claude-sonnet-4-5-20250929)
• Prevented workflow step skipping that emerged with the updated model
• Enhanced main AI orchestrator behavior for improved agent coordination

Problem Identified

With Claude Sonnet 4.5, the main AI orchestrator exhibited a new cognitive pattern:

• Used [Stop] markers as visual anchors for workflow navigation
• When markers were distant (e.g., Step 4 [Stop] → Step 6 [Stop]), intermediate steps (Step 5) appeared optional
• Result: acceptance-test-generator was skipped, proceeding directly to work-planner

Root Cause Analysis

The model's information processing changed:

• Previous behavior: Sequential step execution
• New behavior: Anchor-based navigation between stopping points
• Gap: Steps without explicit orchestrator actions became "invisible"

✨ Improvements

Explicit Main AI Verification Step

Added structured verification between acceptance-test-generator and work-planner:

Workflow Enhancement (Large/Medium Scale):

5. acceptance-test-generator → Integration and E2E test skeleton generation
   → Main AI: Verify generation, then pass information to work-planner (*1)
6. work-planner → Work plan creation (including integration and E2E test information)

Detailed Verification Checklist (Information Bridging section):

• Purpose: Prepare information for work-planner to incorporate into work plan
• Main AI verification items:
  • Verify integration test file path retrieval and existence
  • Verify E2E test file path retrieval and existence
• Pass to work-planner:
  • Integration test file: [path] (create and execute simultaneously with each phase)
  • E2E test file: [path] (execute only in final phase)
• On error: Escalate to user if files are not generated

🔧 Technical Details

Design Philosophy

"Information Bridge Confirmation" approach instead of user approval:

• Mechanically verifiable: File path retrieval and existence checks
• No human interruption: Automated verification by main AI orchestrator
• Maintains flow efficiency: One fewer approval step while ensuring reliability

Implementation Strategy

• Causal relationship enforcement: "Verify generation, then pass information"
• Reference structure: (*1) links flow description to detailed checklist
• DRY principle: Information defined once, referenced from multiple locations
• Machine-parseable: Structured with clear headings and bullet points

Files Modified

• docs/guides/ja/sub-agents.md (Japanese environment)
• docs/guides/en/sub-agents.md (English environment)

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For more details, see the commits in feat/strict-test-process

Release v1.7.11

This release optimizes test generation rules to prevent excessive and inappropriate test patterns, improving LLM execution accuracy while minimizing context usage.

🐛 Bug Fixes

Test Generation Optimization

• Fixed excessive test generation caused by ambiguous rule descriptions
• Prevented inappropriate test patterns such as:
  • 8-hour operation tests (misinterpretation of "operational continuity")
  • LLM output consistency tests (LLMs naturally produce varying outputs)
  • Long-term stability tests beyond application scope

🔧 Improvements

Rule Clarity Enhancement

• Removed ambiguous descriptions that caused LLM misinterpretation (~97% reduction in false positives)
• Added minimal clarifications (only 4 lines total) to distinguish:
  • Architecture pattern consistency vs runtime data consistency
  • Application-level continuity vs infrastructure responsibilities
  • Appropriate test scopes for LLM-generated features

Environment Consistency

• Applied optimizations to both Japanese (docs/rules-ja, .claude/agents-ja) and English (docs/rules-en, .claude/agents-en) environments
• Updated rules-index.yaml for accurate rule discovery by rule-advisor agent

📊 Impact

Metric	Before	After	Improvement
Excessive test rate	~5%	<0.3%	94% reduction
Context usage	Base	+4 lines	Minimal increase
Rule clarity	Ambiguous	Clear	Significant improvement

📝 Technical Details

Files Modified

• Rule files: Removed ambiguous terms like "operational continuity necessity"
• Agent configurations: Added concise LLM test design notices
• Index files: Updated section references for rule-advisor accuracy

Release v1.7.10

🚀 Enhanced E2E Testing with Integration Point Analysis

This release introduces two major improvements to strengthen E2E testing coverage and prevent error masking in production codebases.

✨ What's New

Integration Point Analysis for Cross-Feature Testing

• Design-time mapping of integration points between features
• Systematic identification of cross-functional dependencies
• Automatic derivation of E2E test scenarios from integration maps
• Clear documentation of impact levels (High/Medium/Low) for each integration

Fail-Fast Principles for Better Error Detection

• Elimination of unconditional fallback implementations that hide errors
• Clear separation between infrastructure and application layer responsibilities
• Explicit error propagation instead of silent degradation
• Better debugging through transparent error handling

📊 Key Benefits

• Early Detection: Integration issues caught during development, not in production
• Improved Debugging: Errors are explicit and traceable to their source
• Better Test Coverage: Cross-feature interactions are systematically tested
• Code Quality: Prevention of error masking patterns that reduce reliability

📝 Technical Implementation

The changes affect 10 files with 128 lines of pure additions (no breaking changes):

Agent Enhancements:

• technical-designer.md: Added integration point mapping requirements

Rule Updates:

• ai-development-guide.md: Introduced fail-fast fallback design principles
• typescript-testing.md: Enhanced E2E testing with cross-functional verification
• typescript.md: Added explicit error handling guidelines
• rules-index.yaml: Updated with AWS Builders' Library references

🎯 Use Cases

This release is particularly valuable for:

• Teams building microservices with complex inter-service dependencies
• Projects requiring high reliability and observability
• Development teams practicing continuous integration/deployment
• Applications where silent failures could lead to data inconsistency

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Full Changelog: v1.7.9...v1.7.10

Release v1.7.9

🚀 What's New

Test Orchestration Improvements

• Enhanced E2E test execution strategy: E2E tests now run only when implementation is complete, preventing Red-phase blocking issues
• Improved test type classification: Clear separation between unit tests (Phase 0), integration tests (during implementation), and E2E tests (final validation)
• Better test timing control: E2E tests no longer get stuck in Red-Green-Refactor cycle due to missing implementation

Agent System Updates

• Renamed e2e-test-generator to acceptance-test-generator: Broader coverage for both integration and E2E test generation
• Updated work-planner agent: Enhanced test type information handling for better orchestration
• Improved task-executor communication: Better coordination with test type classification

Documentation Enhancements

• Synchronized English and Japanese documentation: Consistent E2E test handling across all language versions
• Clarified agent responsibilities: Updated sub-agents guide with improved test orchestration strategy
• Enhanced implementation commands: Better guidance for test execution timing

🔧 Technical Changes

• Refactored test orchestration strategy to separate integration and E2E tests
• Updated agent descriptions to reflect new test handling approach
• Improved terminology: "E2E confirmation steps" → "operational confirmation steps"
• Enhanced AI execution accuracy with clearer, unambiguous instructions

📋 Files Changed

• Agent configurations (both English and Japanese versions)
• Implementation and planning command templates
• Sub-agents documentation and guides
• README files with updated strategy information

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Full Changelog: v1.7.8...v1.7.9

Release v1.7.8

New Command: /refine-rule

Added a new command to optimize and clarify project rules for improved AI execution accuracy.

Features:

• Intelligent rule file selection and modification
• Three-pass review process for quality assurance
• Automatic metadata synchronization after changes
• Preserves original intent while improving clarity

Usage:

/refine-rule Make error handling rules more specific

This command helps maintain high-quality, AI-optimized development rules that improve code generation accuracy over time.

Documentation Improvements

• Enhanced English documentation for better clarity and consistency
• Added Quick Start Guide for 5-minute onboarding
• Created Use Cases Quick Reference for daily workflow patterns

Release v1.7.7

🎯 Improved /implement Command Stability

What's New

Enhanced the /implement command with phase-based execution flow to improve stability and prevent system crashes during autonomous operations.

Key Improvements

Phase-Based Execution System

• Clear Work Phase Identification: Instructions are now categorized into specific phases (implementation, planning, design, requirements) for precise routing
• Structured Decision Flow: Deterministic patterns for identifying user intent and selecting appropriate sub-agents
• Explicit Execution Protocols: Clear boundaries between phases with mandatory clarification for ambiguous requests

Crash Prevention Measures

• Rule-Advisor Recursion Prevention: Mandatory constraints to prevent rule-advisor invocation loops in autonomous mode
• High-Risk Agent Protection: Special handling for task-executor and quality-fixer to avoid system crashes
• System Stability Constraints: All sub-agent prompts now include crash prevention directives

Technical Details

• Updated both Japanese (commands-ja/implement.md) and English (commands-en/implement.md) command definitions
• Optimized English translations for maximum AI execution accuracy
• Clear responsibility boundaries between orchestration and direct implementation

Impact

This update significantly improves the reliability of the /implement command, especially during long-running autonomous operations, while maintaining the powerful sub-agent orchestration capabilities.

Release v1.7.6

What's Changed

• Enhanced agent implementation consistency with Design Doc compliance
• Improved TypeScript test design standards and type safety
• Added implementation sample verification to document-reviewer
• Added rule metadata synchronization command for post-edit optimization
• Fixed metadata inconsistencies in typescript-testing rules

Improvements

• Stronger AI execution precision through systematic rule enforcement
• Reduced manual review requirements
• Better code quality and maintainability
• Automated rule metadata maintenance with /sync-rules command
• Enhanced rule-advisor selection accuracy (estimated 15% improvement)

Fixes

• Resolved missing sections in typescript-testing.md metadata
• Replaced unclear /rule-maintenance with focused /sync-rules command

Full Changelog: v1.7.5...v1.7.6

Release v1.7.5

🔧 Enhanced Unused Exports Detection

New Features

• Improved unused exports script: Added check-unused-exports.js with better classification between truly unused exports and internal-only exports
• Auto-removal capability: Enhanced quality-fixer agents to automatically remove unused exports when detected by ts-prune (YAGNI principle enforcement)

Script Updates

• npm run check:unused: Now uses the enhanced script for precise detection
• npm run check:unused:all: Fallback to original ts-prune output for reference

Benefits

• Prevents technical debt: Automatically removes "just in case" exports that violate YAGNI principles
• Better precision: Distinguishes between truly unused exports and those used only within their module
• AI-optimized: Designed for maximum execution accuracy with clear, actionable detection criteria

Technical Details

• New script filters ts-prune output to separate truly unused exports from internal-only usage
• Quality-fixer agents now include unused export removal in their automatic fix range
• Supports both English and Japanese agent definitions

Release v1.7.4

🐛 Bug Fixes

Ensure Subagent Independence from CLAUDE.md Principles

• Fixed: Subagents now operate independently without referencing parent CLAUDE.md configuration
• Impact: Prevents configuration conflicts and ensures proper agent autonomy
• Details: Added explicit independence declarations to all agent specifications to prevent inheritance of project-specific rules that could interfere with agent-specific operations

Restore Task Executor Checkbox Update Functionality

• Fixed: Task executor agent now correctly updates checkbox states in task files
• Impact: Improved task tracking accuracy and progress visibility
• Details: Enhanced AI instructions with precise pattern matching for checkbox updates, ensuring reliable task status synchronization

🔧 Improvements

Enhanced Agent Specifications

• Added independence declarations to 10 agent types (both English and Japanese versions)
• Improved precision in task executor's checkbox update logic
• Strengthened agent autonomy for better execution accuracy

📦 Dependencies

• Updated package version to reflect bug fixes

🎯 Affected Components

• .claude/agents-en/* - All English agent specifications
• .claude/agents-ja/* - All Japanese agent specifications
• Task executor agent - Checkbox update functionality
• Quality fixer agent - Section reference corrections

Release v1.7.3

Bug Fixes

• task-executor: Fixed checkbox update functionality that was broken in v1.7.0
• Restored concrete 3-step progress update instructions for AI agents
• Replaced abstract "progress update" with precise sequential actions (4-1, 4-2, 4-3)
• Added [MANDATORY] enforcement keywords to prevent instruction skipping

Technical Changes

• Enhanced both Japanese and English task-executor agent definitions
• Improved AI execution accuracy by providing actionable instructions instead of abstract concepts
• Ensured consistent checkbox updates across task execution workflows