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 AgentToolkit/altk-evolve --skill learngit clone --depth 1 https://github.com/AgentToolkit/altk-evolveWrote 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/agenttoolkit/altk-evolve/learn)<a href="https://agentmods.dev/skills/agenttoolkit/altk-evolve/learn"><img src="https://agentmods.dev/badge/skills/agenttoolkit/altk-evolve/learn/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agenttoolkit/altk-evolve/learn"><img src="https://agentmods.dev/badge/skills/agenttoolkit/altk-evolve/learn.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00040 | $0.02450 |
| Opus 5.5 | $0.00016 | $0.00980 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo ago First seen · 199 lines · 40 tokens per session scan A 0091f788f000
learn is a skill published in the GitHub repository AgentToolkit/altk-evolve (124 stars, last pushed today), licensed Apache-2.0. It adds 40 tokens to every session and 2,450 once invoked, about $0.0002 per session on Opus 5.5. 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
memory-to-skill
Turn workflows from your MemSearch memory into reusable skills. Use when the user asks to make/create/extract/distill a skill from what they just did or from past work, review skill candidates, install a distilled skill, or 'turn this into a skill'. Manages MemSearch procedural-memory candidates under…
knowledge-pack
Build a durable knowledge pack in the hub for one subject — a small always-read index plus numbered load-on-demand files, every claim sourced and graded. Use when the user says "build a knowledge pack", "make a reference on X", or when you notice you keep re-deriving or re-researching the same subject across sessions.
session-complete
Run at end of session OR before compaction. Extracts reasoning artifacts with priority matrix, updates NEXT.md, appends to session log. Two modes: end-of-session (default) and pre-compaction (--compact). Triggers: "done", "wrap up", "session complete", "end of session", "about to compact", "context getting long".
session-start
Session opening ritual. Loads context via L0→L1 protocol, displays briefing with active decisions and pending items, checks for stale artifacts by priority. Triggers: "start session", "what's the context", "where were we", "catch me up".
vault-audit
Vault health check. Validates structure, detects stale reasoning artifacts by priority level, processes inbox, and suggests cleanup. Triggers: "vault audit", "check vault", "clean up vault", "process inbox", "what's stale", "vault health".
decay
Find aged [D] artifacts whose invalidation triggers may have fired. Scans vault context.md tables, parses the invalidates-if clause, scores decay from git log keyword search + vault contradiction + age. Emits ranked candidates with the triggering signal. User confirms → artifact marked superseded. Triggers: "decay…