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Agent Harness
作者
neltharion11
· GitHub ↗
· v1.8.0
· MIT-0
143
总下载
0
收藏
0
当前安装
10
版本数
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
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install agent-harness - 安装完成后,直接呼叫该 Skill 的名称或使用
/agent-harness触发 - 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
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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