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feat: prompt effectiveness tracking + feedback loops#5

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DarlingtonDeveloper merged 3 commits into
mainfrom
feat/prompt-optimisation-loop
Feb 16, 2026
Merged

feat: prompt effectiveness tracking + feedback loops#5
DarlingtonDeveloper merged 3 commits into
mainfrom
feat/prompt-optimisation-loop

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Summary

  • Adds prompt_effectiveness table (migration 006) tracking session-level effectiveness = outcome_score / cost_usd
  • New API endpoints: create/update/summary/model-tiers/discovery-accuracy/mission-cost-breakdown/compression-candidates/autonomy-candidates
  • NATS subscribers for swarm.usage.tokens, swarm.dredd.correction, swarm.cc.session.completed
  • Background analysers: verbose prompt detection (Loop 3) and autonomy expansion candidates (Loop 4)
  • 20 new unit tests across 3 test files

Test plan

  • pytest tests/test_effectiveness.py — 11 tests pass
  • pytest tests/test_analyser.py — 4 tests pass
  • pytest tests/test_autonomy.py — 5 tests pass
  • Apply migration 006 to Supabase
  • Verify NATS subscribers connect on startup

🤖 Generated with Claude Code

DarlingtonDeveloper and others added 3 commits February 13, 2026 21:12
Start PromptForge with mock Supabase client via scripts/e2e_server.py,
then exercise prompt CRUD, version lifecycle, subscriptions, and audit
via scripts/e2e.sh. No external services required.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds the prompt_effectiveness table, effectiveness API endpoints
(create, update, summary, model-tiers, discovery-accuracy, mission
cost breakdown), NATS subscribers for token/correction/session events,
and background analysers for verbose prompt detection and autonomy
expansion candidates.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
@DarlingtonDeveloper DarlingtonDeveloper merged commit 01a58a3 into main Feb 16, 2026
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@DarlingtonDeveloper DarlingtonDeveloper deleted the feat/prompt-optimisation-loop branch February 16, 2026 01:37
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