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Replace amazoncorretto:23-al2023-jdk with eclipse-temurin:23-jdk-alpine to address 14 security vulnerabilities. The Alpine-based image provides a smaller attack surface while maintaining JDK 23 compatibility. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
…25-0840 - Update libexpat to fix CVE-2024-8176 (CVSS 7.5) - Update binutils to fix CVE-2025-0840 (CVSS 6.3) - Explicitly upgrade vulnerable packages during runtime image build 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
jpicklyk
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Oct 22, 2025
…u compatibility Feature Architect optimizations: - Add conditional template logic (Step 4a/4b) - detect Technical vs Business PRDs - Skip business templates for Technical PRDs (~2,000 token savings) - Add section routing tags (Step 7) for specialist-specific content filtering - Enhance file path handoff behavior for orchestrator integration Planning Specialist optimizations: - Add CRITICAL OUTPUT REQUIREMENTS section emphasizing 50-100 token limit - Optimize Step 8 response format for brevity (85 tokens vs 500 tokens) - Simplify Step 6 guidance - make custom sections optional (skip for complexity ≤6) - Add cost awareness messaging for Haiku model usage - Multiple reinforcements of brevity requirements throughout definition Token efficiency improvements: - Optimization #1: Selective section reading (~3,000 token savings, 43% reduction) - Optimization #2: Scoped overview pattern (~2,000 token savings) - Optimization #3: Conditional template application (~2,000 token savings) - Optimization #4: Section routing tags (downstream ~2-3,000 token savings) - Optimization #5: File path handoff (~5,000 token savings, 94% reduction) Testing: Validated with StatusManagementImplementationPlan.md - Planning Specialist output: 500 tokens → 85 tokens (83% reduction) - Quality maintained: 11 tasks, 10 dependencies, perfect domain isolation - Haiku model compatibility confirmed 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
jpicklyk
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Oct 29, 2025
Added two critical token optimizations to the task-orchestrator output style: **Optimization #5 - File Handoff for Feature Architect:** - Pass file paths to subagents instead of embedding file content - Pattern: Detect file path → Pass reference → Subagent reads directly - Token savings: ~4,900 tokens per file (49% reduction on handoff) - Total cost: ~5,100 tokens (subagent reads) vs ~10,000 tokens (read + embed) - Implementation guidance includes file path detection patterns and code examples **Optimization #6 - Trust Planning Specialist's Execution Graph:** - Task Orchestration Skill should trust Planning Specialist's graph - Avoid redundant dependency re-querying after Planning Specialist already mapped dependencies - Pattern: Read Planning Specialist graph → Query only task status → Recommend batch to start - Token savings: ~300-400 tokens per feature execution start - Benefits: Eliminates redundant queries, faster execution, consistent analysis Both optimizations are documented with: - ❌ Token-wasteful approach (what NOT to do) - ✅ Optimized approach (what TO do) - Implementation patterns with code examples - Benefits and use cases - When to apply each optimization These optimizations complement existing token efficiency strategies: - Optimization #1: Selective section reading - Optimization #2: Scoped overview queries - Optimization #3: Conditional template application - Optimization #4: Section routing tags - Optimization #7: Graph quality analysis (trainer only) Impact: - File handoff: 49% reduction per file reference - Task execution start: 300-400 token savings per feature - Combined with other optimizations: 58%+ token reduction in workflows 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
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Updating docker image for a smaller package and to address all high and medium CVE's.