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 agentmods add skills/agenttoolkit/altk-evolve/learnnpx skills add AgentToolkit/altk-evolve --skill learngit clone --depth 1 https://github.com/AgentToolkit/altk-evolveWhat 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 | $0.00040 | $0.02450 |
| Opus 5 | $0.00020 | $0.01225 |
| Sonnet 5 | $0.00008 | $0.00490 |
| Haiku 4.5 | $0.00004 | $0.00245 |
Grade A, and why
learn 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 2d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Entity Generator
Overview
This skill analyzes the current conversation to extract actionable instructions that would help on similar tasks in the future. It identifies errors encountered during the conversation - tool failures, exceptions, wrong approaches, retry loops - and provides recommendations to prevent those errors from recurring. This skill should take note of the concrete solution which solved a concrete problem, not an abstract idea. When the successful resolution involves a non-trivial workaround, parser, command sequence, or fallback pipeline that could be used to avoid wasted effort, capture that solution as a reusable artifact first, then save entities that point future agents to use it.
When To Use
Use this skill after completing meaningful work in the turn, especially when encountering:
- tool failures
- permission issues
- missing dependencies
- retries or abandoned approaches
- reusable command sequences or scripts
Examples of artifacts that must be immediately created once proven as the successful solution include:
- an inline Python, shell, or other heredoc script
- a command assembled interactively over multiple retries
- a parser or extractor implemented ad hoc during the turn
- a fallback path triggered by missing dependencies or restricted tooling
Unless that artifact happens to be:
- code which is a trivial one-liner that future agents would not benefit from reusing
- code which embeds secrets, tokens, or user-specific sensitive data
- a guideline that would instruct the agent to invoke a skill, tool, or external command by name (e.g. "run /evolve-lite:learn", "call save_trajectory") - such guidelines trigger prompt-injection detection when retrieved by the recall skill in a future session
- the user explicitly asked for a one-off result and not to persist helper code
- redundant because an equivalent local artifact on disk would be just as effective
Workflow
Step 0: Save and Load the Conversation
First, use the /evolve-lite:save-trajectory skill to save the current conversation to .evolve/trajectories/. Capture the exact path from its output as saved_trajectory_path. You will attach this exact path to each entity's trajectory field in Step 6.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 199 lines · 40 tokens per session scan A 0091f788f000
learn is a skill published in the GitHub repository AgentToolkit/altk-evolve (105 stars, last pushed 7d ago), licensed Apache-2.0. It adds 40 tokens to every session and 2,450 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 2026-08-30.
Other skills, from other repositories
generating-mod-envs
Generates and reviews mod learning env JSON files for Letta Code local mods. Use when asked to teach, learn, or optimize a mod behavior; create, draft, validate, improve, or explain envs for /mods learn --env; or design evaluation scenarios, memory fixtures, requiredResultMarkers, requiredTraceMarkers, negative…
memclaw
The agent's persistent long-term memory — the only knowledge that survives across sessions, shared across the fleet under access control. Consult it at the start of a task to recall prior decisions, findings, and rules before acting, and write outcomes, decisions, and lessons as work completes. Use whenever a caura…
run-history-skill-builder
Turn a completed task, browser flow, artifact pipeline, failure-recovery trace, or repeatedly refined workflow into a new reusable skill package or a reviewed skill-design plan. Use when the user asks to make a new skill from real run history, extract a reusable workflow from conversation/logs/files, summarize lessons…
memory-curation
When you have read / processed a workspace asset in this session and learned something durable about it, write a memory page so future sessions benefit. Maintain the workspace wiki's hierarchical structure as it grows.
plur-memory
Persistent learning for AI agents. Open engram format. Your agent learns from corrections, remembers across sessions, and transfers knowledge across domains.
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.