Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add isaacsight/kernel --skill autopoiesis-loopgit clone --depth 1 https://github.com/isaacsight/kernelWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/isaacsight/kernel/autopoiesis-loop)<a href="https://agentmods.dev/skills/isaacsight/kernel/autopoiesis-loop"><img src="https://agentmods.dev/badge/skills/isaacsight/kernel/autopoiesis-loop.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.00637 |
| Opus 5 | $0.00019 | $0.00318 |
| Sonnet 5 | $0.00008 | $0.00127 |
| Haiku 4.5 | $0.00004 | $0.00064 |
Grade A, and why
autopoiesis-loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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)
- 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. - During work — notice repeated patterns. Each repetition is a skill waiting to be written (
skill-self-authorship). - On friction — missing tool?
forge-reflex. Wrong specialist? Update the learned router via corrective feedback. - Session end — update
SCRATCHPAD.mdwith what you learned (not what you did). The next session's opening context reads this file. - 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-selffine-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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 48 lines · 39 tokens per session scan A f70ae6b5dda1
autopoiesis-loop is a skill published in the GitHub repository isaacsight/kernel (16 stars, last pushed 9d ago), licensed MIT. It adds 39 tokens to every session and 637 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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