Library

Self-Improving Agent Kit

Name: Self-Improving Agent Kit
Brand: Digital Thoughts
SKU: self-improving-agent

Kit files are in English.

Turn repeated corrections into rules your agent can use next time.

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Self-Improving Agent Kit: An excerpt from the included kit files. — An excerpt from the included kit files.

Access, downloads and refund policy

Yearly: every available kit and bundle, unlimited downloads.

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How it works

6-component pipeline that turns corrections into rules. Signal collection, pattern detection, performance scoring, and style enforcement.

Now with promotion gates and rollback rules.

Latest update: 2026-05-11

Added bounded feedback loops, rule rollback, and promotion gates so learning does not become runaway behavior.

Added bounded-feedback-loops.md
Added rule-rollback-playbook.md
Added CHANGELOG.md and quickstart safety notes

Best for

+Agents that receive corrections and need to stop repeating them
+Builders who want measurable behavior improvement without fine-tuning

Not for

-Autonomous rule rewriting without human review for sensitive workflows

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)

+Bounded feedback loops: limits for new rules, signal thresholds, and test windows

+Rule rollback playbook: retire rules that make the agent worse

+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)
•bounded-feedback-loops.md and rule-rollback-playbook.md
•Real examples (correction report, sample signals)
•Comprehensive guide (10 sections, architecture to production)
•QUICKSTART: working pipeline in 10 minutes
•CHANGELOG.md

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.