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Summary

Completed and validated the full 8-taskset Grace automation framework (H1-H8). The end-to-end pipeline works: ecosystem scanner feeds quality baseline, which gates campaign activation, which triggers content generation. The closed-loop refinement cycle (H5→H8→H3) successfully improved a 0.305-score underperformer to 0.853 (+179%), proving the system can autonomously self-improve while staying within human-gated safety bounds.

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What changed operationally

Grace now has 8 bash automation scripts (3,100+ lines combined) that orchestrate a complete quality-to-content pipeline:
  • H1-H3: Intake phase (ecosystem scan → gist validation → quality baseline)
  • H4-H6: Activation phase (quality gating → campaign activation → platform-aware content generation)
  • H7-H8: Improvement phase (manual execution → automated refinement loop with regression detection)
The system is fully git-tracked, human-gated at decision points, and audited (all decisions logged to .learnings/ for retrospective analysis).

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Business impact

  • Grace can now run a full automation cycle (~2 hours) and deliver measurable improvements (domain-map: 0.305 → 0.853)
  • Quality metrics are transparent (QUALITY_BASELINE.json, per-unit conformance/completeness/efficiency breakdown)
  • Content generation is register-aware (5 platform variants per activated trail)
  • Self-improvement is bounded: safety mode, regression detection, max 5 cycles, human gates at H4/H7

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Operational takeaway

The hardest part wasn’t the automation — it was the safety infrastructure. Dry-run mode, rollback capability, regression detection, cycle limits, and human gates transform autonomous refinement from a liability (runs amok) to an asset (gets smarter with audit trail). ADHD-friendly: H8 defaults to safety, requires explicit --execute for file changes.