self-learning

A self-learning setup for agents whose source book is open to updates. After a conversation, the agent may append examples and commitments to its source, while keeping earlier authored content unchanged.

In plain words
What is it for?
It is for agents that need to accumulate conversation-based examples and guidance over time.
Why use it?
It lets an agent retain lessons from conversations without rewriting or corrupting its original instructions.

Agent

Install

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.

agentmods
npx agentmods add agents/webgptorg/promptbook/self-learning
Clone the repo
git clone --depth 1 https://github.com/webgptorg/promptbook
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 785 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00785
Opus 5 $0.00000 $0.00392
Sonnet 5 $0.00000 $0.00157
Haiku 4.5 $0.00000 $0.00078

Measured yesterday against content hash 362989208130, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

self-learning 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 yesterday.

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.

specs/agents/self-learning.md · 44 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. yesterday First seen · 44 lines · 0 tokens per session scan A 362989208130

Subscribe to this mod's changes

self-learning is an agent published in the GitHub repository webgptorg/promptbook (167 stars, last pushed yesterday), with no licence file. It costs nothing until one of its globs matches a file; then it loads 785 tokens. 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 2026-08-30.