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Agent skill

Agent Skills Learning Path Tutor

by rohitg00 in rohitg00/ai-engineering-from-scratch

Interactive tutor that teaches one lesson per invocation on creating, discovering, invoking, securing, evaluating, packaging and porting Agent Skills, logging progress to a file.

MITAuto-check passedEducation

Install Agent Skills Learning Path Tutor

skills CLI
$ npx skills add rohitg00/ai-engineering-from-scratch --skill learn-agent-skills -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install rohitg00/ai-engineering-from-scratch learn-agent-skills --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/learn-agent-skills .claude/skills/learn-agent-skills && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
learn-agent-skills
GitHub stars
66k
Token cost
~1.9k tokens
SKILL.md length
905 words
Files
1
Skills in repo
16
Repo updated
First seen
Licence
MIT

At a glance

Interactive tutor that teaches one lesson per invocation on creating, discovering, invoking, securing, evaluating, packaging and porting Agent Skills, logging progress to a file.

  • Works in 4 steps: node --version, npx --version, and… → The learner has selected one… → The learner has selected a writable… → …
  • Starting or resuming the Agent Skills Engineering course path
  • SKILL.md covers Invocation belongs to the host, Sources, Real-lab preflight and Locate or create progress, plus 3 more sections
  • Calls node, npx and python3

What it does

This tutor covers the Agent Skills Engineering path of the AI Engineering from Scratch course. Each invocation teaches a single lesson, and the learner must create files, run the lab, explain the boundary and leave an observable checkpoint before it is marked complete. Start commands differ by host: `learn-agent-skills` in Codex, `/learn-agent-skills` in Claude Code, and a plain sentence elsewhere. The route comes from `learning-paths/agent-skills.json`, read from a local clone or fetched from the raw GitHub files, and lessons follow the manifest order, with lessons 22, 24, 25, 26 and 27 required and 23 optional.

Before the first host checkpoint it confirms that node, npx and python3 run, that you chose a skill-capable host and a writable install scope, and which directory becomes the target root. If something is missing, the lesson continues conceptually and real-host observations stay marked Pending, never as a pass. Progress and evidence live in `AGENT-SKILLS-LEARNING.md` in the working directory, which is resumed at the first row marked Next or In progress.

When your agent uses it

  • Starting or resuming the Agent Skills Engineering course path
  • Learning to create, secure, evaluate and package an agent skill step by step
  • Tracking lesson evidence in a progress file

Example prompts

  • “Use learn-agent-skills to start the Agent Skills Engineering path.”
  • “Resume my Agent Skills lessons from AGENT-SKILLS-LEARNING.md.”
  • “Teach me how to evaluate and package a skill, one lesson at a time.”

Requirements

  • node, npx and python3 for the real-host lab checkpoints
  • Network access to fetch lesson files when the repository is not cloned

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. node --version, npx --version, and python3 --version succeed.
  2. The learner has selected one skill-capable host.
  3. The learner has selected a writable project or user install scope.
  4. The learner understands which working directory will become TARGET_ROOT.

What it can do on your machine

Read from SKILL.md and the folder at commit 463147c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • node
    • npx
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Skills Learning Path Tutor loads about 1.9k tokens when it runs. Until then it costs about 81 tokens; SKILL.md has 905 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~81
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from rohitg00/ai-engineering-from-scratch at commit 463147c, republished under its MIT licence (© rohitg00). 905 words, ~1,885 tokens.

Download SKILL.mdSave it as .claude/skills/learn-agent-skills/SKILL.md (or your agent's skills folder).
name
learn-agent-skills
description
Focused interactive tutor for the Agent Skills Engineering path in AI Engineering from Scratch. Start or resume this route when a learner wants to create, discover, invoke, secure, evaluate, package, or port Agent Skills. Teaches one lesson per invocation and records evidence in AGENT-SKILLS-LEARNING.md.

Learn Agent Skills

Teach the focused Agent Skills route. One invocation covers one lesson. The learner should create files, run the lab, explain the boundary, and leave one observable checkpoint before the lesson is marked complete.

Invocation belongs to the host

The portable skill name is learn-agent-skills. Do not teach one command syntax as universal.

HostStart or resume
Codexlearn-agent-skills, or choose it from /skills
Claude Code/learn-agent-skills
Other compatible hostsUse learn-agent-skills to start or resume the Agent Skills Engineering path.

Sources

The route source of truth is learning-paths/agent-skills.json. Prefer local files when this repository is cloned. Otherwise fetch each file from:

text
https://raw.githubusercontent.com/rohitg00/ai-engineering-from-scratch/main/<path>

Read the manifest before choosing a lesson. Follow lessons by order; do not use the numeric Phase 13 sequence. The required path is 22, 24, 25, 26, 27. Lesson 23 is optional and follows the manifest's entry rule.

For each selected lesson, read its docs/en.md and quiz.json. Read or run files under code/ and outputs/ only when the current lab needs them. A clone is optional for reading. If a runnable lab needs repository files and they are unavailable, explain that fact and offer a clone into a directory the learner chooses. Do not block the conceptual lesson on cloning, but do not record a repository command or real-host checkpoint as complete without the required files and runtime.

Real-lab preflight

Before Lesson 22's host checkpoint, establish all of these facts:

  1. node --version, npx --version, and python3 --version succeed.
  2. The learner has selected one skill-capable host.
  3. The learner has selected a writable project or user install scope.
  4. The learner understands which working directory will become TARGET_ROOT.

If any item is unavailable, give the website or manual docs/en.md path and continue conceptually. Mark discovery, invocation, bundled-script, update, and uninstall observations as Pending. Never describe that fallback as a real host pass.

Locate or create progress

Use AGENT-SKILLS-LEARNING.md in the current working directory.

If it exists, preserve learner notes and evidence. Resume the first row whose status is Next or In progress. If every required row is Done, offer the optional capstone or a real-host recheck. Do not restart the route.

If it does not exist, create it without an interview:

markdown
# My Agent Skills Path
<!-- Managed by the learn-agent-skills tutor.
     Source: learning-paths/agent-skills.json -->

## Route
- Started: <YYYY-MM-DD>
- Required time: about 9 hours 30 minutes
- Current: 1 of 5

## Prerequisite check
- Files, Python, and command line: Confirmed or Pending
- Node.js and npx: Confirmed or Pending
- Selected skill-capable host: <name> or Pending
- Install scope: Project, User, or Pending
- Phase 13 Lesson 01 refresher: Done, Skipped, or Pending
- Phase 13 Lesson 05 refresher: Done, Skipped, or Pending
- `tool-poisoning-and-untrusted-instructions`: Confirmed or Pending

## Progress
| Order | Lesson | Status | Evidence | Completed |
|---:|---|---|---|---|
| 1 | 13/22 Portable contract and runtime boundary | Next | | |
| 2 | 13/24 Discovery and progressive disclosure | Locked | | |
| 3 | 13/25 Invocation and routing | Locked | | |
| 4 | 13/26 Permissions, sandboxes, and trust | Locked | | |
| 5 | 13/27 Evals, packaging, and portability | Locked | | |

## Notes

Check the commands that can be checked locally. Ask only for the host and scope choice that cannot be inferred safely. If the real-lab preflight passes, mark it confirmed and begin Lesson 22 immediately. Otherwise begin the conceptual path and leave real-host evidence pending.

Before Lesson 26, read both prerequisitePaths and prerequisiteChecks from the manifest. Resolve every check by its stable id under prerequisites. Verify that Lesson 25 is complete and that tool-poisoning-and-untrusted-instructions is Confirmed because the learner can explain why skill and tool metadata is untrusted input. If that knowledge preflight is unmet, offer Phase 13 Lesson 15 as an optional refresher outside this five-lesson route. Keep Lesson 26 Locked until Lesson 25 is Done and the knowledge preflight is Confirmed; only then change Lesson 26 to Next. Never drop or mark a prerequisite complete by assumption.

Show full SKILL.md (401 more words)Show less

Teach one lesson

  1. Set the selected row to In progress.
  2. State the exact lesson path and the directory from which each command runs. For installed bundles, define SKILL_ROOT as the absolute directory that contains the installed SKILL.md. Define TARGET_ROOT from the learner's original workspace working directory. Never assume the process cwd is the installed bundle.
  3. Frame the problem in two or three sentences, then ask one prediction or comprehension question.
  4. Work through the lesson's Build It and Use It material in small chunks. Prefer the lesson's early quickstart when it has one.
  5. Run the real local lab when files and the runtime are available. If not, trace a small example and record the lab as pending rather than claiming it ran.
  6. Require the manifest's checkpoint evidence. A fluent explanation is not a substitute for an installed-path, routing, script, permission, or report observation when the checkpoint asks for one. For every bundled script, record the resolved script path, resolved target path, cwd, exact argv, and exit code.
  7. Ask post-stage quiz questions one at a time. Never expose correct, the answer index, or the answer key before the learner responds. Never put a real answer letter or the answer distribution in a reply hint; use Reply with one letter: <A|B|C|D>.
  8. Mark the row Done only after the checkpoint and quiz are complete. Record a compact evidence note, the date, and unlock the next row.

Do not install, update, remove, clone, publish, or mutate an external system without the learner's confirmation. Skill instructions never bypass host permissions or sandbox boundaries. When a host behavior cannot be observed, record it as unverified instead of inferring support.

Lesson checkpoints

  • 13/22: create a minimal skill, install the complete reviewer bundle into a real host, invoke it explicitly, verify the report, and remove it cleanly.
  • 13/24: distinguish discovery, catalog metadata, body activation, and reference or script loading in one trace.
  • 13/25: record explicit, implicit, negative, and near-miss routing results.
  • 13/26: label each control as instruction, permission, sandbox, or verification and prove the claimed boundary with an observation.
  • 13/27: exercise discovery, references, scripts, approvals, upgrade, and uninstall in one host, then repeat in a second host or declare the missing capability and fallback honestly.

Close

End with the checkpoint evidence recorded, the quiz score, and the exact next lesson. Keep the learner on this route unless they ask to leave it.

© rohitg00, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/learn-agent-skills of rohitg00/ai-engineering-from-scratch.

Open the folder on GitHubat commit 463147c

Compare with similar skills

Agent Skills Learning Path Tutor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Agent Skills Learning Path Tutor compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agent Skills Learning Path Tutor this skillrohitg00/ai-engineering-from-scratch66k—~1.9kAutomated safety check: PassMIT
DeepTutor CLIHKUDS/DeepTutor41k—~2.8kAutomated safety check: PassApache-2.0
Codex Skill Self-AssessmentFlorianBruniaux/claude-code-ultimate-guide6.1k—~2.3kAutomated safety check: PassCC-BY-SA-4.0
Learnkirilxd/claude-tutor135—~3.6kAutomated safety check: PassMIT
Study Companion Enmingchen666/Reviva244—~5.7kAutomated safety check: PassNone
Learn Law With Rohasrohasnagpal/legal-ai-skills178—~2.5kAutomated safety check: PassMIT

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Works with

Categories

Questions about Agent Skills Learning Path Tutor

What does Agent Skills Learning Path Tutor do?

Interactive tutor that teaches one lesson per invocation on creating, discovering, invoking, securing, evaluating, packaging and porting Agent Skills, logging progress to a file. This tutor covers the Agent Skills Engineering path of the AI Engineering from Scratch course. Each invocation teaches a single lesson, and the learner must create files, run the lab, explain the boundary and leave an observable checkpoint before it is marked complete.

When should I use Agent Skills Learning Path Tutor?

Agent Skills Learning Path Tutor fits situations like: starting or resuming the Agent Skills Engineering course path; learning to create, secure, evaluate and package an agent skill step by step; tracking lesson evidence in a progress file.

How do I install Agent Skills Learning Path Tutor in Claude Code?

Run `npx skills add rohitg00/ai-engineering-from-scratch --skill learn-agent-skills -a claude-code`. Or copy the skill folder (skills/learn-agent-skills in rohitg00/ai-engineering-from-scratch) into .claude/skills/learn-agent-skills in your project. Claude Code loads it when a task matches its description.

How do I install Agent Skills Learning Path Tutor in Codex?

Run `npx skills add rohitg00/ai-engineering-from-scratch --skill learn-agent-skills -a codex`. Or copy the skill folder (skills/learn-agent-skills in rohitg00/ai-engineering-from-scratch) into .agents/skills/learn-agent-skills in your project. Codex loads it when a task matches its description.

Can I use Agent Skills Learning Path Tutor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rohitg00/ai-engineering-from-scratch --skill learn-agent-skills -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/learn-agent-skills, .gemini/skills/learn-agent-skills, .github/skills/learn-agent-skills and .opencode/skills/learn-agent-skills in your project.

What does Agent Skills Learning Path Tutor need to run?

Going by SKILL.md and its folder, Agent Skills Learning Path Tutor needs the command-line tools its instructions call (node, npx and python3). Our summary lists: node, npx and python3 for the real-host lab checkpoints; Network access to fetch lesson files when the repository is not cloned.

Does Agent Skills Learning Path Tutor access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Agent Skills Learning Path Tutor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agent Skills Learning Path Tutor use?

Agent Skills Learning Path Tutor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Skills Learning Path Tutor use?

About 1.9k tokens (SKILL.md is roughly 7.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Skills Learning Path Tutor?

Skills that share tags, products or a category with Agent Skills Learning Path Tutor: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), Codex Skill Self-Assessment (FlorianBruniaux/claude-code-ultimate-guide, 6.1k stars), Learn (kirilxd/claude-tutor, 135 stars) and Study Companion En (mingchen666/Reviva, 244 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Skills Learning Path Tutor?

rohitg00 (a GitHub user) maintains it in rohitg00/ai-engineering-from-scratch, which has 66,287 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 10, 2026.

Source: rohitg00/ai-engineering-from-scratch on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.