feat: Deployment Readiness Report (M119)

[hlin99]

Summary

Unified go/no-go deployment readiness assessment combining multiple existing analyzers into a single verdict.

Changes

• ReadinessAssessor class in readiness.py
• ReadinessConfig, ReadinessCheck, ReadinessVerdict, ReadinessReport Pydantic models
• Combines: quality gate result, SLA risk score, SLA headroom, cost efficiency, rate limit headroom
• Per-check pass/warn/fail with configurable thresholds
• Overall verdict: READY (all pass), CAUTION (any warn, no fail), NOT_READY (any fail)
• Actionable deployment recommendations and blockers list
• CLI readiness subcommand with --benchmark, --sla-ttft, --sla-tpot, --sla-total, --cost-per-request, --optimal-cost, --measured-qps, --max-safe-qps, table + JSON output
• Programmatic assess_readiness() API
• 26 new tests

Closes #261

[hlin99-Review-Bot]

✅ Approved — M119 Deployment Readiness Report

Idea: Valuable — unifying quality gate, SLA risk, headroom, cost efficiency, and rate limit checks into a single go/no-go verdict is exactly the right abstraction for deployment decisions.

Code: Clean implementation. ReadinessAssessor composes existing analyzers well. Pydantic models are well-defined. Per-check pass/warn/fail with configurable thresholds is flexible. CLI and programmatic API both covered. 26 tests with good coverage of edge cases (zero max QPS, config overrides, serialization roundtrip).

Ship it. 🚀

[hlin99-Review-BotX]

✅ Approved — M119 Deployment Readiness Report

Idea: Strong value — consolidating quality gate, SLA risk, headroom, cost efficiency, and rate limit checks into a single go/no-go verdict is the right abstraction for deployment decisions.

Code: Clean composition of existing analyzers. Pydantic models well-structured. Per-check pass/warn/fail with configurable thresholds gives good flexibility. CLI and programmatic API both covered. 26 tests with solid edge case coverage. current.md updated.

Second approval — should auto-merge. 🚀