autopoiesis-loop

name	autopoiesis-loop
description	Use when planning multi-session work. The autopoiesis loop is kbot using itself to improve itself — every session should end a little sharper than it started.
version	1.0.0
author	kbot
license	MIT
metadata	{"kbot":{"tags":["self-improvement","meta","dogfood","autopoiesis"],"related_skills":["skill-self-authorship","teacher-trace-curation","dream-to-commit"]}}

The Autopoiesis Loop

kbot is the tool and the workbench. Every session has two outputs: the thing the user asked for, and the incremental improvement to kbot itself. Sessions that only produce the first are leaving compound interest on the table.

The Five Moves (once per session)

1. Session start — run kbot bootstrap. The bootstrap agent surfaces the highest-leverage improvement based on accumulated signals. Do this before feature work, not instead of it.
2. During work — notice repeated patterns. Each repetition is a skill waiting to be written (skill-self-authorship).
3. On friction — missing tool? forge-reflex. Wrong specialist? Update the learned router via corrective feedback.
4. Session end — update SCRATCHPAD.md with what you learned (not what you did). The next session's opening context reads this file.
5. Overnight — the dream engine consolidates transcripts into memory entries. The daemon reviews diffs, runs code quality scans, translates i18n. Work continues while the user sleeps.

Iron Law

NEVER END A SESSION WORSE THAN IT STARTED.

If kbot hit a wall and you didn't leave a corrective signal behind (a skill, a memory, a scratchpad note, a corrected learned-router pattern), the loop is broken.

The Three Signals That Compound

• Corrections — user says "no, do X instead." These go into ~/.kbot/corrections/ and load as closed-loop prompts.
• Teacher traces — every non-local Claude call is logged to ~/.kbot/teacher/traces.jsonl. Weekly, kbot train-self fine-tunes local models on the best ones.
• Skills — successful patterns distilled into ~/.kbot/skills/. Loaded on relevance.

Each of these runs automatically once wired up. The skill is knowing to wire them up in the first place.

What Emerges

Three weeks of active use and kbot's answers start feeling tuned to this user specifically. Six weeks in, the local model (via train-self) is answering basic questions at zero cost. Three months in, kbot's corrections archive has more collective wisdom than the user's own notes.

Anti-Pattern

Running kbot as a pure consumer — asking questions, using answers, never looking at what's in ~/.kbot/. You're paying for the loop with every API call but not collecting the dividend.