
learn-agent-skills
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.
- Installs
- 2.9K
- Last 24h
- +82
- Trending rank
- #2259
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/rohitg00/ai-engineering-from-scratch --skill learn-agent-skills- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads learn-agent-skills automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.
| Host | Start or resume |
|---|---|
| Codex | learn-agent-skills, or choose it from /skills |
| Claude Code | /learn-agent-skills |
| Other compatible hosts | Use 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:
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:
node --version,npx --version, andpython3 --versionsucceed.- The learner has selected one skill-capable host.
- The learner has selected a writable project or user install scope.
- 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:
# 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.
Teach one lesson
- Set the selected row to
In progress. - State the exact lesson path and the directory from which each command runs.
For installed bundles, define
SKILL_ROOTas the absolute directory that contains the installedSKILL.md. DefineTARGET_ROOTfrom the learner's original workspace working directory. Never assume the process cwd is the installed bundle. - Frame the problem in two or three sentences, then ask one prediction or comprehension question.
- Work through the lesson's Build It and Use It material in small chunks. Prefer the lesson's early quickstart when it has one.
- 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.
- 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.
- 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; useReply with one letter: <A|B|C|D>. - Mark the row
Doneonly 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.
FAQ
What does the learn-agent-skills skill do?
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.
How do I install learn-agent-skills?
Run npx skills add https://github.com/rohitg00/ai-engineering-from-scratch --skill learn-agent-skills in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/learn-agent-skills/ (personal) or .claude/skills/learn-agent-skills/ (project).
Which AI agents can use learn-agent-skills?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is learn-agent-skills safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains learn-agent-skills?
It is published by rohitg00 in rohitg00/ai-engineering-from-scratch. It has 2,875 recorded installs.
More from rohitg00/ai-engineering-from-scratch
start-learning
rohitg00/ai-engineering-from-scratch
One-time onboarding for the AI Engineering from Scratch curriculum (523 lessons, 20 phases). Interviews the learner, runs the placement quiz, and writes LEARNING.md, a persistent study plan the learn skill drives. Trigger phrases: "start learning", "set up the course", "begin the curriculum", "onboard me", "create my learning plan"
check-understanding
rohitg00/ai-engineering-from-scratch
Phase quiz for AI Engineering from Scratch. Trigger with "quiz me", "test phase", "check my understanding", "do I know phase 3", or /check-understanding .
find-your-level
rohitg00/ai-engineering-from-scratch
Interactive quiz that maps your AI/ML knowledge to a starting point in the 523-lesson, 20-phase AI Engineering from Scratch curriculum. Trigger phrases: "where should I start", "find my level", "what do I know", "which phase", "assess my knowledge", "placement test", "skip ahead"
learn
rohitg00/ai-engineering-from-scratch
Interactive lesson tutor for the AI Engineering from Scratch curriculum. Reads LEARNING.md, fetches the next lesson, teaches it section by section in the terminal, quizzes at the end, and records progress. Works cloned or entirely over raw.githubusercontent.com — no setup required. Trigger phrases: "next lesson", "teach me", "continue the course", "let's learn", "resume learning"
course-guide
rohitg00/ai-engineering-from-scratch
Topic router for the AI Engineering from Scratch curriculum. Give it a topic, a question, or a bug you are fighting, and it points at the exact lessons that teach it, plus the right next command. Trigger phrases: "where do I learn", "which lesson covers", "course guide", "I'm stuck on", "what should I do next", "teach me MCP", "teach me Agent Skills", "where do I prepare for a Claude certification"
claude-certification
rohitg00/ai-engineering-from-scratch
AI-native tutor and onboarding workflow for the four independent Claude certification tracks in AI Engineering from Scratch. Use when a learner wants to choose a Claude certification, prepare for CCAO-F, CCDV-F, CCAR-F, or CCAR-P, resume a certification path, learn the next lesson interactively, run and verify practical labs, build scored artifacts, take a diagnostic or mock exam, or remediate weak exam domains from GitHub with Claude Code, Codex, ChatGPT, Cursor, or another agent.
Popular ai & agents skills
google-agents-cli-publish
google/agents-cli
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
google-agents-cli-eval
google/agents-cli
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
skill-creator
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
codex-pet
prime-skills/runcomfy-agent-skills
Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
executing-plans
obra/superpowers
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available