Does learn-harness-engineering auto-invoke its skills?

Not yet. It is indexed on Flowy as a plain plugin. Request auto-invocation on its page and Flowy will route its skills for you as you prompt.

Is learn-harness-engineering free?

Yes. Flowy is free and open source, with nothing gated. You can read every skill in full before you install.

Education

Plugin

learn-harness-engineering

Harness engineering beginner tutorial, from 0 to 1

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walkinglabs@walkinglabs

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Plugin Signal68/100Solid

maintained87

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documented100

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Install

$ npx -y skills add walkinglabs/learn-harness-engineering --agent claude-code

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Anthropic OpenAI

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MIT

License

26d ago

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5mo ago

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20d ago

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Repo: walkinglabs/learn-harness-engineering

In this plugin

1 skill

- harness-creator

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What's inside

Skill(1 skill)

/harness-creatorBuild, audit, and improve harnesses that make AI coding agents reliable: AGENTS.md/CLAUDE.md instruction files, feature/state tracking, verification gates, scope boundaries, session handoff, memory persistence, context budgets, tool-permission safety, and multi-agent

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README

ExcerptRead the full README on GitHub

🌍 This course is available in 15 languages: English, 简体中文, 繁體中文, 日本語, 한국어, Español, Français, Русский, Deutsch, العربية, Tiếng Việt, Oʻzbekcha, Türkçe, Portuguese (BR), Українська. Choose your language from the badges above.

🆕 What's New — August 2026

Frontier Harness Design Breakdowns — new section (4 breakdowns)

What	Details
New section	Frontier Harness Design Breakdowns — Apply the course's five-subsystem framework (instructions, tools, environment, state, feedback) to reverse-engineer how four frontier products build real harnesses.
Pi	How Pi builds its harness — a minimal kernel, programmable expansion, and context engineering behind "ask Pi to build what you want."
Claude Code	How Claude Code builds its harness — four-layer memory, five-level compaction, hooks, and sub-agent isolation.
Codex	How Codex builds its harness — the repository as source of truth, AGENTS.md as a directory page, and worktree isolation.
DeepSeek	How DeepSeek builds its harness — "everything is a plugin," capability seams, and an event pipeline.
All 15 languages	Full translation coverage across all supported languages.

Key idea: The course gives you a framework; these breakdowns show you how the same principles actually play out in production harnesses.

Graph Engineering Update — 1 new lecture, 1 new project

What	Details
Lecture 14	From Single Loops to Graph Engineering — Why a single loop grows into a graph: the four stacked layers (prompt → context → loop → graph) and where harness sits in that stack, the four parts of a graph (nodes, edges, shared state, routing), why in-loop checkpoints can't fix the three structural failures at scale (Goodhart, blindness upward, conflict), a framework-agnostic six-step walkthrough for building your first graph, graph vs. workflow, anchors, which open-source "graph engineering" projects existed before the name vs. after it, the orchestration tax, and when a graph is actually worth drawing.
Project 08	Draw Your Workflow as a Graph — Three progressive experiments: draw your maker-checker loop as an explicit graph, add a parallel fan-out/fan-in node, then add a conditional rollback edge and a human-approval node.

What

Details

Lecture 14

From Single Loops to Graph Engineering — Why a single loop grows into a graph: the four stacked layers (prompt → context → loop → graph) and where harness sits in that stack, the four parts of a graph (nodes, edges, shared state, routing), why in-loop checkpoints can't fix the three structural failures at scale (Goodhart, blindness upward, conflict), a framework-agnostic six-step walkthrough for building your first graph, graph vs. workflow, anchors, which open-source "graph engineering" projects existed before the name vs. after it, the orchestration tax, and when a graph is actually worth drawing.

Project 08

Draw Your Workflow as a Graph — Three progressive experiments: draw your maker-checker loop as an explicit graph, add a parallel fan-out/fan-in node, then add a conditional rollback edge and a human-approval node.

Key idea: A loop is a graph with one node. When your task needs specialization, parallelism, shared state, verification, and recovery — it has stopped being a loop. It's a graph.

🆕 What's New — July 2026

Loop Engineering Update — 1 new lecture, 1 new project

What	Details
Lecture 13	Why You Need to Stop Prompting Your Agent — From `/goal` to the six primitives of loop engineering (automations, worktrees, skills, connectors, sub-agents, external state), the generator/evaluator split, four silent costs, and a step-by-step guide to building your first loop.
Project 07	Build Your First Automated Loop — Three progressive experiments: goal loop, timer loop, and maker-checker loop. Compare manual vs. automated, measure intervention reduction, and learn to step outside the loop.
Code templates	`goal-template.md`, `loop-state-template.md`, `maker-prompt.md`, `checker-prompt.md` — drop-in templates for building loops immediately.
All 15 languages	Full translation coverage across all supported languages.

Key idea: Harness engineering builds the vehicle. Loop engineering designs the road it drives on — and you design the road from outside the car.

Learn Harness Engineering is a course dedicated to the engineering of AI coding agents. We have deeply studied and synthesized the most advanced Harness Engineering theories and practices in the industry. Our core references include:

Quick start? The skills/harness-creator/ skill can help you scaffold a production-grade harness (AGENTS.md, feature lists, init.sh, verification workflows) for your own project in minutes.

🆕 What's New
✨ Visual Preview
What Harness Engineering Actually Means
Quick Start: Improve Your Agent Today
Capstone Project: A Real App
Learning Path
Syllabus
Skills
Other Courses

✨ Visual Preview

🏠 Course Homepage

A comprehensive course outline and introduction to core philosophies, providing a clear path to get started.

Course homepage preview

📖 Immersive Lectures

Deep dives into real-world pain points and hands-on projects (like Project 01) for an immersive learning experience.

Course lecture preview

🗂️ Ready-to-Use Resource Library

Templates and reference configurations designed to solve common pitfalls in multi-turn AI agent development, such as context loss and premature task completion.

Resource library preview

PDF Coursebooks

The repository now includes a PDF build pipeline for the course content.

Run npm run pdf:build to generate the currently configured PDF coursebooks locally.
Output files are written to artifacts/pdfs/.
Run npm run screenshots:readme if you want to refresh the README preview images.
GitHub Actions workflow release-course-pdfs.yml can build the PDFs and publish them to GitHub Releases.

The Model Is Smart, The Harness Makes It Reliable

There's a hard truth most people learn the hard way: the strongest model in the world will still fail on real engineering tasks if you don't build a proper environment around it.

You've probably seen this yourself. You give Claude or GPT a task in your repo. It starts well — reads files, writes code, looks productive. Then something goes wrong. It skips a step. It breaks a test. It says "done" but nothing actually works. You spend more time cleaning up than if you'd done it yourself.

This isn't a model problem. It's a harness problem.

The evidence is clear. Anthropic ran a controlled experiment: same model (Opus 4.5), same prompt ("build a 2D retro game editor"). Without a harness, it spent $9 in 20 minutes and produced something that didn't work. With a full harness (planner + generator + evaluator), it spent $200 in 6 hours and built a game you could actually play. The model didn't change. The harness did.

OpenAI reported the same thing with Codex: in a well-harnessed repository, the same model goes from "unreliable" to "reliable." Not a marginal improvement — a qualitative shift.

This course teaches you how to build that environment.

                    THE HARNESS PATTERN
                    ====================

    You --> give task --> Agent reads harness files --> Agent executes
                                                        |
                                              harness governs every step:
                                              |
                                              +--> Instructions: what to do, in what order
                                              +--> Scope:        one feature at a time, no overreach
                                              +--> State:        progress log, feature list, git history
                                              +--> Verification: tests, lint, type-check, smoke runs
                                              +--> Lifecycle:    init at start, clean state at end
                                              |
                                              v
                                         Agent stops only when
                                         verification passes

What Harness Engineering Actually Means

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FAQ

learn-harness-engineering is a Claude Code plugin with 1 hand-picked skill for education work, indexed on Flowy. Install it with the command on its page. It includes harness-creator. Its skills do not fire on their own yet. Request auto-invocation to have Flowy route them as you prompt. Free and open source.

What is learn-harness-engineering?: Harness engineering beginner tutorial, from 0 to 1
How do I install learn-harness-engineering?: Run these in Claude Code: npx -y skills add walkinglabs/learn-harness-engineering --agent claude-code. Then prompt normally.
Does learn-harness-engineering auto-invoke its skills?: Not yet. It is indexed on Flowy as a plain plugin. Request auto-invocation on its page and Flowy will route its skills for you as you prompt.
Is learn-harness-engineering free?: Yes. Flowy is free and open source, with nothing gated. You can read every skill in full before you install.

Is this plugin yours?

Claim it with GitHub Submit a plugin Promote it

What

Details

New section

Frontier Harness Design Breakdowns — Apply the course's five-subsystem framework (instructions, tools, environment, state, feedback) to reverse-engineer how four frontier products build real harnesses.

How Pi builds its harness — a minimal kernel, programmable expansion, and context engineering behind "ask Pi to build what you want."

Claude Code

How Claude Code builds its harness — four-layer memory, five-level compaction, hooks, and sub-agent isolation.

Codex

How Codex builds its harness — the repository as source of truth, AGENTS.md as a directory page, and worktree isolation.

DeepSeek

How DeepSeek builds its harness — "everything is a plugin," capability seams, and an event pipeline.

All 15 languages

Full translation coverage across all supported languages.

What	Details
Lecture 14	From Single Loops to Graph Engineering — Why a single loop grows into a graph: the four stacked layers (prompt → context → loop → graph) and where harness sits in that stack, the four parts of a graph (nodes, edges, shared state, routing), why in-loop checkpoints can't fix the three structural failures at scale (Goodhart, blindness upward, conflict), a framework-agnostic six-step walkthrough for building your first graph, graph vs. workflow, anchors, which open-source "graph engineering" projects existed before the name vs. after it, the orchestration tax, and when a graph is actually worth drawing.
Project 08	Draw Your Workflow as a Graph — Three progressive experiments: draw your maker-checker loop as an explicit graph, add a parallel fan-out/fan-in node, then add a conditional rollback edge and a human-approval node.

What

Details

Lecture 14

Project 08

What

Details

Lecture 13

Why You Need to Stop Prompting Your Agent — From /goal to the six primitives of loop engineering (automations, worktrees, skills, connectors, sub-agents, external state), the generator/evaluator split, four silent costs, and a step-by-step guide to building your first loop.

Project 07

Build Your First Automated Loop — Three progressive experiments: goal loop, timer loop, and maker-checker loop. Compare manual vs. automated, measure intervention reduction, and learn to step outside the loop.

Code templates

goal-template.md, loop-state-template.md, maker-prompt.md, checker-prompt.md — drop-in templates for building loops immediately.

All 15 languages

Full translation coverage across all supported languages.

THE HARNESS PATTERN ==================== You --> give task --> Agent reads harness files --> Agent executes | harness governs every step: | +--> Instructions: what to do, in what order +--> Scope: one feature at a time, no overreach +--> State: progress log, feature list, git history +--> Verification: tests, lint, type-check, smoke runs +--> Lifecycle: init at start, clean state at end | v Agent stops only when verification passes