Harness Skill Generator for Openclaw

Harness Skill Generator guides you from an initial problem definition to a tested, stateful, multi-stage AI agent skill.

iichaner
v0.1.0
Aug 18, 2026
0
1.3k
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install harness-skill-generator

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install harness-skill-generator using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is Harness Skill Generator?

Harness Skill Generator is a structured development workflow for creating new AI agent skills based on the Harness methodology. It is designed for complex tasks involving multiple stages, branching decisions, quality assurance, human checkpoints, and state persistence.

The skill produces a complete skill package containing SKILL.md, references/, and templates/, while guiding the user through problem scanning, agent architecture, scaffolding, incremental content authoring, sample validation, full test runs, delivery confirmation, and project retrospectives. As part of Openclaw Skills, it helps teams turn repeatable human workflows into reliable, maintainable agent workflows rather than oversized prompts.

Harness Skill Generator Use Cases

  • Create a new skill for a workflow with more than two stages.
  • Design agent automations with multiple branches, routing rules, or exception paths.
  • Convert a documented human process into an AI-agent workflow with explicit inputs and outputs.
  • Build skills that require quality checks, SubAgent reviews, or human approval checkpoints.
  • Persist decisions and progress to files so interrupted workflows can resume safely.
  • Generate reusable references, templates, repair rules, and style contracts for complex tasks.
  • Validate a newly created skill with a small sample before running a realistic end-to-end test.
  • Decide whether a request needs a Harness workflow or can be handled with a simple prompt.

How Harness Skill Generator Works

  1. Trigger and suitability scan: Detect requests such as creating, building, or generating a skill, then determine whether the task has enough complexity to justify Harness. Single-step tasks are redirected toward simpler prompts.
  2. Problem Scan: Capture the actual problem, user pain points, boundaries, and the complete human workflow, including each step's actions, inputs, outputs, and likely failure points. Save planning artifacts such as plan/problem-definition.md and plan/human-workflow.md.
  3. Problem and boundary checkpoint: Independently confirm the pain points, scope, human workflow, and step ownership before proceeding.
  4. Agent Architecture: Research mature market solutions first, then evaluate agent-native capabilities and browser or RPA-style approaches where necessary. Define phases, dependencies, branch routing, quality controls, state files, and human decision points.
  5. Architecture checkpoints: Confirm the overall design first, then review every phase individually, including methods, outputs, quality checks, and minimal state requirements. Record the complete design in plan/agent-workflow.md.
  6. Scaffold: Create the skill directory, SKILL.md skeleton, references/ directory, and templates/ directory without prematurely filling in implementation details.
  7. Incremental Fill: Author references one at a time, beginning with quality standards and style contracts, followed by phase rules, templates, routing rules, and repair rules. Obtain user confirmation after each reference.
  8. Consistency review: Use a SubAgent to check for contradictions between references and SKILL.md, incomplete quality protocols, and delivery risks.
  9. Sample Test checkpoint: Run a small example through the early workflow, confirm trigger recognition and initial outputs, and require human acceptance before full testing.
  10. Test Run: Execute the complete skill against a real, moderately complex task, record defects in test-run-report.md, and fix one issue at a time.
  11. Delivery and retrospective: Deliver the finished skill, README, test report, and project-summary.md; confirm completion with the user before documenting lessons and optimization opportunities.

Harness Skill Generator Setup

Prerequisites

  • An agent environment that loads skills from a shared skills directory.
  • A clearly defined workflow with enough complexity to benefit from phases, branching, quality checks, or persistent state.
  • User availability for architecture, reference, sample-test, and delivery confirmations.

Scaffold a new skill

Use the generated skill name in place of <skill-name>:

mkdir -p <skill-name>/{references,templates}

Create the initial files:

touch <skill-name>/SKILL.md
mkdir -p <skill-name>/references <skill-name>/templates

Populate SKILL.md with frontmatter containing the skill name, description, and trigger phrases. Add the generated phase workflow, boundaries, checkpoints, quality protocol, and core rules. Then add one reference file per phase, plus optional files such as quality-checklist.md, style-contract.md, branch-routing.md, and repair-rules.md.

Install the completed skill

After testing and user approval, copy the complete directory to the shared agent skill location:

cp -R <skill-name> /Users/ii/.agents/skills/<skill-name>/

Update AGENTS.md with the skill entry when required, and record the creation event in MEMORY.md. This generator creates skills; it does not execute the newly created skill or modify and upgrade an existing skill.

Harness Skill Generator Data Schema & Taxonomy

Generated skill package

Path Purpose
SKILL.md Main skill contract, trigger description, boundaries, phase workflow, checkpoints, and non-negotiable rules
references/ Detailed phase instructions, decision rules, quality criteria, routing logic, and repair guidance
templates/ Reusable output structures and phase-specific document templates
README.md Installation, triggering, usage guidance, and operational notes
test-run-report.md Full workflow test results, defects, and one-at-a-time fixes
project-summary.md Delivery assessment, interruptions, root-cause categories, and future optimization recommendations

Planning and state artifacts

  • plan/problem-definition.md stores the problem statement and pain points.
  • plan/human-workflow.md records the manually performed workflow with inputs, outputs, and failure risks.
  • plan/solution-research.md compares mature market solutions, agent-native approaches, and simulated human interaction options.
  • plan/agent-workflow.md defines phases, dependencies, branches, outputs, quality checks, checkpoints, and state files.
  • Project-level state files persist important decisions and enable recovery after interruption.
  • Output files, state files, scripts, and review documents remain separated rather than scattered in the project root.

Reference metadata taxonomy

Each reference should define: what the phase does, how to perform it, input and output formats, anti-patterns, and a three-to-five-item self-checklist. Quality controls may be inline self-review, SubAgent message-based review, SubAgent file-based review, or user acceptance, depending on risk.

Harness Skill Generator Advanced Features

  • Multi-phase Harness architecture for complex agent workflows.
  • Branch routing tables with explicit conditions, priorities, and a mandatory ask-the-user fallback when ambiguity remains.
  • Human-in-the-loop checkpoints that prevent silent substitution of user decisions.
  • Progressive disclosure and incremental loading of references according to the active phase.
  • SubAgent-based consistency audits before delivery.
  • Risk-matched quality assurance using inline checks, review messages, or review artifacts.
  • Persistent state-file design for interruption recovery and durable decision tracking.
  • Reference-by-reference authoring with user confirmation after each document.
  • Sample-first validation that tests early trigger and output behavior before full execution.
  • Real-task end-to-end testing with defect tracking and controlled iteration.
  • Research-first solution selection that prioritizes mature tools, then agent-native methods, and finally simulated human interaction.
  • Post-delivery retrospectives that classify changes as unclear goals, user expectation changes, or skill capability gaps.
  • Clear scope enforcement: it creates new skills but does not execute, market, publish, modify, or upgrade existing skills.

SKILL.md


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