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Self-Improving Agent Kit

Your AI agent repeats the same mistakes. This kit gives it a 6-component feedback pipeline that turns corrections into rules, tracks whether those rules hold, and shows measurable performance improvement.

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$49One-time purchase.

What you get

+Signal collection — append-only JSONL log of tasks, corrections, approvals, and errors

+Correction detection — pattern-matching categorizer (verbosity, style, wrong approach, etc.)

+Style learning — banned phrases, voice patterns, verbosity ratios

+Performance tracking — 0-100 score from task success rate, correction rate, efficiency

+Error registry — structured database for systemic errors and resolutions

+Lessons system — behavioral rules with graduation (max 50 active)

+All pure Python, no external dependencies, no API keys required

Package includes

•5 standalone Python scripts (signal_collector, correction_detector, style_learner, performance_tracker, error_registry)
•setup.sh — one-command installation
•CLAUDE.md integration snippet
•Templates (lessons.md, error-registry.json, signals.jsonl)
•Real examples (correction report, sample signals)
•Comprehensive guide (10 sections, architecture to production)
•QUICKSTART — working pipeline in 10 minutes

FAQ

Does this need an LLM to analyze corrections?

No. Correction detection uses Python regex pattern matching — fast, free, deterministic, and ~90% accurate for common categories.

How is this different from just updating CLAUDE.md manually?

Manual updates are guesswork. This kit uses data: it counts corrections, finds patterns, proposes rules, and tracks whether your changes actually reduce correction rates.

How long until I see improvement?

Week 1: signals accumulate. Week 2: patterns emerge. Week 3: rules applied, score starts climbing. Month 2: agent rarely repeats old mistakes.

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