Refactor task management structure and update documentation

- Changed the directory structure for task specifications to include a dedicated `todo/` folder within `_docs/02_tasks/` for tasks ready for implementation.
- Updated references in various skills and documentation to reflect the new task lifecycle, including changes in the `implementer` and `decompose` skills.
- Enhanced the README and flow documentation to clarify the new task organization and its implications for the implementation process.

These updates improve task management clarity and streamline the implementation workflow.

_docs/02_tasks/done/AZ-146_test_performance.md

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+# Performance Tests
+
+**Task**: AZ-146_test_performance
+**Name**: Performance Tests
+**Description**: Implement E2E tests measuring detection latency, concurrent inference throughput, tiling overhead, and video processing frame rate
+**Complexity**: 3 points
+**Dependencies**: AZ-138_test_infrastructure
+**Component**: Integration Tests
+**Jira**: AZ-146
+**Epic**: AZ-137
+
+## Problem
+
+Performance characteristics must be baselined and verified: single image latency, concurrent request handling with the 2-worker ThreadPoolExecutor, tiling overhead for large images, and video processing frame rate. These tests establish performance contracts.
+
+## Outcome
+
+- Single image latency profiled (p50, p95, p99) for warm engine
+- Concurrent inference behavior validated (2-at-a-time processing confirmed)
+- Large image tiling overhead measured and bounded
+- Video processing frame rate baselined
+
+## Scope
+
+### Included
+- NFT-PERF-01: Single image detection latency
+- NFT-PERF-02: Concurrent inference throughput
+- NFT-PERF-03: Large image tiling processing time
+- NFT-PERF-04: Video processing frame rate
+
+### Excluded
+- GPU vs CPU comparative benchmarks
+- Memory usage profiling
+- Load testing beyond 4 concurrent requests
+
+## Acceptance Criteria
+
+**AC-1: Single image latency**
+Given a warm engine
+When 10 sequential POST /detect requests are sent with small-image
+Then p95 latency < 5000ms for ONNX CPU or p95 < 1000ms for TensorRT GPU
+
+**AC-2: Concurrent throughput**
+Given a warm engine
+When 2 concurrent POST /detect requests are sent
+Then both complete without error
+And 3 concurrent requests show queuing (total time > time for 2)
+
+**AC-3: Tiling overhead**
+Given a warm engine
+When POST /detect is sent with large-image (4000x3000)
+Then request completes within 120s
+And processing time scales proportionally with tile count
+
+**AC-4: Video frame rate**
+Given a warm engine with SSE connected
+When async detection processes test-video with frame_period=4
+Then processing completes within 5x video duration (< 50s)
+And frame processing rate is consistent (no stalls > 10s)
+
+## Non-Functional Requirements
+
+**Performance**
+- Tests themselves should complete within defined bounds
+- Results should be logged for trend analysis
+
+## Integration Tests
+
+| AC Ref | Initial Data/Conditions | What to Test | Expected Behavior | NFR References |
+|--------|------------------------|-------------|-------------------|----------------|
+| AC-1 | Engine warm | 10 sequential detections | p95 < 5000ms (CPU) | ~60s |
+| AC-2 | Engine warm | 2 then 3 concurrent requests | Queuing observed at 3 | ~30s |
+| AC-3 | Engine warm, large-image | Single large image detection | Completes < 120s | ~120s |
+| AC-4 | Engine warm, SSE connected | Video detection | < 50s, consistent rate | ~120s |
+
+## Constraints
+
+- Pass criteria differ between CPU (ONNX) and GPU (TensorRT) profiles
+- Concurrent request tests must account for connection overhead
+- Video frame rate depends on hardware; test measures consistency, not absolute speed
+
+## Risks & Mitigation
+
+**Risk 1: CI hardware variability**
+- *Risk*: Latency thresholds may fail on slower CI hardware
+- *Mitigation*: Use generous thresholds; mark as performance benchmark tests that can be skipped in resource-constrained CI