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Agent Harness

作者 neltharion11 · GitHub ↗ · v1.8.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install agent-harness
功能描述
Agent Work Framework. Unified entry thinking framework + workflow Skill. Trigger Words (Thinking Modes): research, plan, design, think, pattern, process, ste...
使用说明 (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

安全使用建议
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.
功能分析
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.
能力评估
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.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install agent-harness
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /agent-harness 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
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
元数据
Slug agent-harness
版本 1.8.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 10
常见问题

Agent Harness 是什么?

Agent Work Framework. Unified entry thinking framework + workflow Skill. Trigger Words (Thinking Modes): research, plan, design, think, pattern, process, ste... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 143 次。

如何安装 Agent Harness?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install agent-harness」即可一键安装,无需额外配置。

Agent Harness 是免费的吗?

是的,Agent Harness 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Agent Harness 支持哪些平台?

Agent Harness 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Agent Harness?

由 neltharion11(@neltharion11)开发并维护,当前版本 v1.8.0。

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