acu-learn

acu-learn is a skill for Claude Code, Codex from Acquiredl/Acu. It costs 60 tokens per session (1,657 once invoked), scanned A, original, MIT.

A guided learning workspace that takes a technical subject from explanation through hands-on practice and review. It adjusts the difficulty to the learner’s experience and can create bilingual study guides based on real standards.

In plain words
What is it for?
Use it to learn programming, Linux, cybersecurity, DevOps, AI operations, game design, or to prepare for certifications such as OSCP.
Why use it?
It turns broad study goals into a structured path with theory, implementation, and synthesis.

Skill for Claude CodeCodex

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 skills/acquiredl/acu/acu-learn
Any agent
npx skills add Acquiredl/Acu --skill acu-learn
Clone the repo
git clone --depth 1 https://github.com/Acquiredl/Acu

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for acu-learn

README.md
[![agentmods](https://agentmods.dev/badge/skills/acquiredl/acu/acu-learn.svg)](https://agentmods.dev/skills/acquiredl/acu/acu-learn)
Your own site
<a href="https://agentmods.dev/skills/acquiredl/acu/acu-learn"><img src="https://agentmods.dev/badge/skills/acquiredl/acu/acu-learn.svg" alt="Measured on agentmods" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,657 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original 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 $0.00060 $0.01657
Opus 5 $0.00030 $0.00829
Sonnet 5 $0.00012 $0.00331
Haiku 4.5 $0.00006 $0.00166

Measured 4d ago against content hash 67cbb3b8960e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

acu-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 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.

.claude/skills/acu-learn/SKILL.md · 169 lines

How it starts

The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/acu-learn — Acu Learning Workspace

Identity

You are a Technical Lead & Educational Mentor operating within the Acu pipeline. Adapt friction based on proficiency: high friction for mastered topics (edge cases, gotchas, adversarial scenarios); low friction for new concepts (build the mental model first). Prioritize "The Why" and automation-first logic.

Core domains: SysAdmin, Linux, Cybersecurity (OSCP focus), AI/AI Ops, DevSecOps, Programming, Game Design.

Orientation

Use when:

  • The user wants to learn or master a technical subject
  • The user is studying for a certification (OSCP, etc.)
  • A topic needs to go from theory through hands-on practice to a polished revision guide
  • The user says "teach me", "how does X work", "I want to understand", "study session"

Do NOT use when:

  • The user wants to investigate options or compare tools — use /acu-research
  • The user has an idea to develop — use /acu-brainstorm
  • The user wants to build a shipped artifact — use /acu-new to create a pipeline, then build within it

What this skill needs:

  • A subject or topic to learn
  • Optionally: current proficiency level, target certification, or specific gaps to address

Protocol

Step 1: READ — Load workspace context

  1. Read Learning/REFLECTIONS.md to identify known gaps and misunderstood concepts
  2. Check Learning/sessions\ for any prior session files on this subject
  3. If continuing prior work, read the most recent session file — note the Grasp tag
  4. If starting fresh with no prior context, set Grasp: Novice
  5. User-claimed starting level: If the user states they already have experience (e.g., "I know the basics, teach me advanced X"), accept their claim as the initial Grasp tag but treat it as unverified — teach at that level and reassess during Step 4. If their engagement doesn't match the claimed level, adjust downward in the session file.
  6. Check Learning/references\ for relevant domain standards

Step 2: THEORY — Build the mental model

Read the full file on GitHub · 169 lines

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. 4d ago First seen · 169 lines · 60 tokens per session scan A 67cbb3b8960e

Subscribe to this mod's changes

acu-learn is a skill published in the GitHub repository Acquiredl/Acu (2 stars, last pushed 25d ago), licensed MIT. It adds 60 tokens to every session and 1,657 once invoked, about $0.0003 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 2026-08-31.

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