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Agent Harness Construction
作者
2072932870wh-ui
· GitHub ↗
· v1.0.0
· MIT-0
56
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install agent-harness-construction
功能描述
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
使用说明 (SKILL.md)
\r \r
Agent Harness Construction\r
\r Use this skill when you are improving how an agent plans, calls tools, recovers from errors, and converges on completion.\r \r
Core Model\r
\r Agent output quality is constrained by:\r
- Action space quality\r
- Observation quality\r
- Recovery quality\r
- Context budget quality\r \r
Action Space Design\r
\r
- Use stable, explicit tool names.\r
- Keep inputs schema-first and narrow.\r
- Return deterministic output shapes.\r
- Avoid catch-all tools unless isolation is impossible.\r \r
Granularity Rules\r
\r
- Use micro-tools for high-risk operations (deploy, migration, permissions).\r
- Use medium tools for common edit/read/search loops.\r
- Use macro-tools only when round-trip overhead is the dominant cost.\r \r
Observation Design\r
\r Every tool response should include:\r
status: success|warning|error\rsummary: one-line result\rnext_actions: actionable follow-ups\rartifacts: file paths / IDs\r \r
Error Recovery Contract\r
\r For every error path, include:\r
- root cause hint\r
- safe retry instruction\r
- explicit stop condition\r \r
Context Budgeting\r
\r
- Keep system prompt minimal and invariant.\r
- Move large guidance into skills loaded on demand.\r
- Prefer references to files over inlining long documents.\r
- Compact at phase boundaries, not arbitrary token thresholds.\r \r
Architecture Pattern Guidance\r
\r
- ReAct: best for exploratory tasks with uncertain path.\r
- Function-calling: best for structured deterministic flows.\r
- Hybrid (recommended): ReAct planning + typed tool execution.\r \r
Benchmarking\r
\r Track:\r
- completion rate\r
- retries per task\r
- pass@1 and pass@3\r
- cost per successful task\r \r
Anti-Patterns\r
\r
- Too many tools with overlapping semantics.\r
- Opaque tool output with no recovery hints.\r
- Error-only output without next steps.\r
- Context overloading with irrelevant references.\r
安全使用建议
This skill is a coherent, read-only set of best practices for designing agent harnesses and does not itself access secrets or install code. Before using it in production, (1) review and vet any skills or files the agent will be instructed to load or reference (the guide encourages referencing files/on-demand skills), (2) enforce least privilege for any micro-tools you implement (especially for deploy/migration/permissions), and (3) monitor agent tool invocations and artifact paths to ensure sensitive data isn't exposed or written where it shouldn't be.
功能分析
Type: OpenClaw Skill
Name: agent-harness-construction
Version: 1.0.0
The skill bundle contains only high-level architectural guidance and best practices for designing AI agent toolsets, observation formats, and error recovery strategies. There is no executable code, shell commands, or instructions that could be interpreted as malicious or suspicious in SKILL.md or _meta.json.
能力评估
Purpose & Capability
The name and description (designing agent action spaces, tool definitions, observation formatting) match the SKILL.md content. There are no unrelated requirements (no env vars, binaries, or install steps) that would be inconsistent with the stated purpose.
Instruction Scope
The instructions are high-level design guidance for building agent harnesses and do not instruct the agent to read specific system files, call external endpoints, or collect credentials. A mild caveat: the doc recommends 'prefer references to files over inlining long documents' and suggests loading skills on demand — these are reasonable design options but imply the agent may be directed to reference files or load other skills at runtime, so reviewers should ensure any referenced files/skills are vetted in their environment.
Install Mechanism
No install spec and no code files are present (instruction-only). Nothing will be written to disk or downloaded by the skill itself, which minimizes install-related risk.
Credentials
The skill declares no environment variables, credentials, or config paths. The guidance does not request secrets or cross-service credentials, so there is no disproportionate access requested.
Persistence & Privilege
always is false and model invocation is allowed (default). The skill does not request permanent presence or modify other skills or system settings. Autonomous invocation is normal for skills and does not raise additional concerns here.
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-construction - 安装完成后,直接呼叫该 Skill 的名称或使用
/agent-harness-construction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
v1.0.0
Initial release of agent-harness-construction.
- Provides a comprehensive guide for designing and optimizing AI agent action spaces and tool usage.
- Introduces standardized observation formats and error recovery contracts.
- Offers practical granularity rules for tool design and clear anti-patterns to avoid.
- Includes benchmarking recommendations to measure agent output quality and completion rates.
- Suggests architecture patterns for effective agent planning and execution.
元数据
常见问题
Agent Harness Construction 是什么?
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 56 次。
如何安装 Agent Harness Construction?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install agent-harness-construction」即可一键安装,无需额外配置。
Agent Harness Construction 是免费的吗?
是的,Agent Harness Construction 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Agent Harness Construction 支持哪些平台?
Agent Harness Construction 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 Construction?
由 2072932870wh-ui(@2072932870wh-ui)开发并维护,当前版本 v1.0.0。
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