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neltharion11

Agent Harness

by neltharion11 · GitHub ↗ · v1.8.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install agent-harness
Description
Agent Work Framework. Unified entry thinking framework + workflow Skill. Trigger Words (Thinking Modes): research, plan, design, think, pattern, process, ste...
README (SKILL.md)

Agent Harness

One-line summary: First decide "what combination to use", then execute layer by layer.

Core Relationship:

Decision Tree → Choose "Pipeline + which WORKFLOWS"
Pipeline = Execution Framework (universal)
WORKFLOWS = Work Content (specific)

⚠️ Pre-Execution Check (Must Confirm Each Item)

When receiving a task, first answer these questions:

1. [ ] What is the core task? (multi-step / single-step)
2. [ ] Is the requirement clear? (clear → next / unclear → load 06-INVERSION.md)
3. [ ] Does it need multi-step execution? (yes → Pipeline + other)
4. [ ] What is the specific content? (corresponds to which WORKFLOWS)

Response Format:

[Pre-Execution Check]
Q1: xxx → Conclusion
Q2: xxx → Conclusion
...
Final Decision: Pipeline + WORKFLOWS/{name}
Or: Final Decision: Inversion (standalone mode)

Layer Relationship Diagram

┌─────────────────────────────────────────────────────────────┐
│                   Layer Execution Order                       │
│                                                               │
│  Step 1 ──→ Step 2 ──→ Step 3 ──→ Step 4 ──→ Step 5      │
│     │           │           │           │           │        │
│     ▼           ▼           ▼           ▼           ▼        │
│  ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐     │
│  │Decision│→│Pipeline │→│WORKFLOW│→│Template│→│Quality │     │
│  │01-     │ │02-      │ │03-     │ │04-     │ │05-     │     │
│  │DECISION│ │PIPELINE │ │WORKFLOWS│ │TEMPLATE│ │QUALITY │     │
│  └────────┘ └────────┘ └────────┘ └────────┘ └────────┘     │
│                                                               │
│  Decision Tree decides "which combination",                  │
│  Pipeline decides "universal execution flow",                │
│  WORKFLOWS decides "specific work content"                   │
└─────────────────────────────────────────────────────────────┘

Analogy

🍳 Cooking Scenario:
- Decision Tree = Customer orders ("spicy today")
- Pipeline = Cooking method (heat pan first, no matter what)
- WORKFLOWS = Specific recipe (how to make fish-flavored pork)
- Template = Plating style (how to arrange the plate)

💻 Software Scenario:
- Decision Tree = Task classification ("this is a research task")
- Pipeline = Execution flow (decompose first, no matter the research)
- WORKFLOWS = Research method (4-step method)
- Template = Report format (title-abstract-body-conclusion)

Execution Flow

Step 1 — Decision (Load 01-DECISION.md)

[Step 1] Read references/01-DECISION.md
         Decide: Pipeline + which WORKFLOWS

Common Combinations:
- Pipeline + research    → Research tasks
- Pipeline + subagent    → Coordination tasks
- Pipeline + context     → Compression tasks
- Pipeline + analysis    → Analysis tasks

Step 2 — Execution Framework (Load 02-PIPELINE.md)

[Step 2] Read references/02-PIPELINE.md
         Understand: Universal 4-step execution flow

Pipeline Steps:
Step 1: Plan
Step 2: Execute
Step 3: Summarize
Step 4: Check

Step 3 — Specific Content (Load 03-WORKFLOWS/{name}.md)

[Step 3] Read references/03-WORKFLOWS/{corresponding workflow}.md
         Execute: Specific work content

research = 4-step research (decompose→research→synthesize→report)
subagent = 4-step coordination (analyze→decompose→parallel→merge)
context  = 4-step compression (assess→strategy→execute→verify)
analysis = 4-step analysis (decompose→collect→compare→conclude)

Step 4 — Output Template (Load 04-TEMPLATES/{name}.md)

[Step 4] Read references/04-TEMPLATES/{corresponding template}.md
         Generate: Structured final output

Step 5 — Quality Check (Load 05-QUALITY.md)

[Step 5] Read references/05-QUALITY.md
         Verify: Output quality meets standards

Quick Reference: Common Combinations

Task Type Combination Description
📝 Deep Research Report Pipeline + research Multi-step research flow
🤖 Multi-Agent Coordination Pipeline + subagent Decompose+parallel+merge
📦 Long-Task Compression Pipeline + context Context management strategy
⚖️ Competitive Analysis Pipeline + analysis Multi-dimensional comparison
❓ Unclear Requirements Inversion (standalone) Gather requirements first

Forbidden Behaviors

  • Skip Steps: Go to Step 3 without completing Step 1
  • Mix Layers: Treat Pipeline and WORKFLOWS as the same thing
  • Missing Template: Output content but format is chaotic
  • Missing Check: End without quality verification

Completion Flag

[agent-harness execution complete]
✓ Steps 1-5 completed
✓ Combination: Pipeline + WORKFLOWS/{name}
✓ Forbidden behaviors check: Passed

File Index

File Role Load Time
SKILL.md Entry + Layer Description On trigger
references/01-DECISION.md Decision Tree Step 1
references/02-PIPELINE.md Execution Framework Step 2
references/03-WORKFLOWS/research.md Research Content Step 3
references/03-WORKFLOWS/subagent.md Coordination Content Step 3
references/03-WORKFLOWS/context.md Compression Content Step 3
references/03-WORKFLOWS/analysis.md Analysis Content Step 3
references/04-TEMPLATES/research-report.md Research Template Step 4
references/04-TEMPLATES/analysis-report.md Analysis Template Step 4
references/06-INVERSION.md Requirements Clarification When unclear
references/05-QUALITY.md Quality Check Pipeline Step 4

Last updated: 2026-04-07 by neltharion11 | https://github.com/neltharion11/skill-agent-harness

Usage Guidance
This skill appears coherent and is just a set of instructions/templates for multi-step work and multi-agent coordination. Before installing: (1) Confirm you are comfortable with agents spawning sub-sessions (sessions_spawn / sessions_yield) in your environment; (2) Decide and control the workspace path used for subagent output files (the skill recommends writing reports to {user workspace}/subagent_reports/) so you know where files will be written; (3) If you run this in an environment with sensitive files, restrict the agent's filesystem permissions or avoid granting it write access to sensitive locations; (4) Because the skill can be invoked autonomously (platform default), consider whether you want it able to launch parallel sub-agents without explicit confirmation. Overall the behavior matches the described purpose, but pay attention to filesystem and session-level capabilities when you enable it.
Capability Analysis
Type: OpenClaw Skill Name: agent-harness Version: 1.8.0 The agent-harness skill bundle is a comprehensive thinking framework designed to structure AI agent workflows into modular layers (Decision, Pipeline, Workflow, Template, and Quality). It provides detailed instructions for complex tasks such as deep research, multi-agent coordination, and context management. The use of OpenClaw APIs like sessions_spawn and file-based reporting in references/subagent.md is consistent with the stated purpose of managing long-running tasks and avoiding output truncation. No indicators of data exfiltration, malicious code execution, or harmful prompt injection were found.
Capability Assessment
Purpose & Capability
Name/description (Agent Work Framework / workflows for research, plan, subagents, context, analysis) match the actual contents: markdown workflows, templates, and instructions for pipeline + workflows. No unrelated env vars, binaries, or external services are requested.
Instruction Scope
SKILL.md instructs the agent to load the included reference files and to use OpenClaw session APIs (sessions_spawn, sessions_yield, sessions_send) for subagent orchestration. It also recommends subagents write full outputs to a user workspace path (e.g., {user configured workspace}/subagent_reports/) and for the parent to confirm file existence. This file-write / session spawn behavior is coherent with multi-agent coordination but is operationally significant (requires filesystem and session APIs).
Install Mechanism
Instruction-only skill with no install spec, no downloaded code, and no declared dependencies — lowest install risk.
Credentials
No environment variables, credentials, or config path requirements are declared. The instructions reference a user-configured workspace (TOOLS.md) but do not request secrets or unrelated credentials; this is proportionate for a multi-agent reporting workflow.
Persistence & Privilege
always:false and default model-invocation behavior. The skill does not request persistent elevated privileges or modify other skills. It does recommend writing subagent output files to a user workspace, which implies filesystem use but doesn't change skill privilege settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install agent-harness
  3. After installation, invoke the skill by name or use /agent-harness
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.8.0
Remove Generator and Reviewer from Decision Tree.
v1.7.0
Upload users verified clean English version. No privacy issues.
v1.6.0
English assets: translate all 4 files. Root README: Chinese users section in Chinese. No Generator files.
v1.5.0
Clean English-only version. Root README is bilingual entry with links. Sub-READMEs link back to root.
v1.4.0
Set English as ClawHub default version. Add bilingual choice note in README.
v1.3.0
Bilingual version with clean README. Remove Generator/Tool Wrapper. Fix accuracy issues.
v1.2.0
Remove english/. Fix README: remove Generator/Tool Wrapper mentions. Simplify to single Chinese version.
v1.1.0
Add bilingual structure: chinese/ + english/. Root SKILL.md now serves as entry point.
v1.0.1
Update author attribution to neltharion11 with GitHub link
v1.0.0
Initial release with Pipeline, Generator, Reviewer, Inversion, Tool Wrapper modes
Metadata
Slug agent-harness
Version 1.8.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 10
Frequently Asked Questions

What is Agent Harness?

Agent Work Framework. Unified entry thinking framework + workflow Skill. Trigger Words (Thinking Modes): research, plan, design, think, pattern, process, ste... It is an AI Agent Skill for Claude Code / OpenClaw, with 143 downloads so far.

How do I install Agent Harness?

Run "/install agent-harness" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Agent Harness free?

Yes, Agent Harness is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Agent Harness support?

Agent Harness is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Agent Harness?

It is built and maintained by neltharion11 (@neltharion11); the current version is v1.8.0.

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