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agent-harness-engineeringAgent 线束工程

Agent Skill

agent-harness-engineering 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

13,218

周安装

562

GitHub Stars

公开资料未说明

下载量

4,631
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:agent-harness-engineering(Agent 线束工程)
来源仓库:https://github.com/yeyitech/agent-harness-engineering
安装命令:
openclaw skills install agent-harness-engineering
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install agent-harness-engineering

简介

引导软件仓库向代理优先工程模式演进。

  • 围绕 AGENTS.md 建立标准化开发规范。
  • 适用于团队级代理工程化体系建设。agent-harness-engineering 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 通过 clawhub 安装,需协调团队成员遵循新规范。
  • 建议从关键模块开始试点,逐步推广至全项目。

SKILL.md

name
agent-harness-engineering
description
Bootstrap or upgrade a software repository for agent-first engineering. Use when a user wants to improve project-wide development discipline around AGENTS.md, progressive-disclosure docs, agent-readable architecture/context, mechanical quality checks, CI-enforced structure, or optional garbage-collection/maintenance loops.

Agent Harness Engineering

Use this skill when the goal is to make a repository easier for coding agents to understand, change, and maintain over time.

This skill turns the main ideas from OpenAI's harness-engineering article into a reusable project pattern:

  • AGENTS.md stays short and acts as a router
  • durable knowledge moves into docs/
  • context is disclosed progressively instead of dumped all at once
  • quality rules become mechanical checks instead of tribal knowledge
  • optional garbage collection keeps agent-generated entropy under control

When to use it

Use this skill when the user asks to:

  • create a reusable engineering skill for many repos
  • bootstrap a repo for agent-first or AI-assisted development
  • redesign AGENTS.md so it routes to structured docs
  • add repo-readable architecture, spec, quality, reliability, or security docs
  • add mechanical checks for doc freshness, structure, and agent guardrails
  • add a low-friction cleanup loop for drift, stale docs, and code sprawl

Choose a rollout mode

Pick the least invasive mode that still improves the repo.

  • overlay: Default for existing repos. Add docs/agent/ as an agent-readable overlay without rewriting existing docs.
  • full: Use for greenfield repos or when the user explicitly wants a broader doc reorganization.

For most mature repos, start with overlay.

First-use workflow

When applying this pattern to a repo for the first time, do the following in order:

  1. Inspect the repo's current AGENTS.md, docs/, CI, and lint/test commands.
  2. Run the bundled bootstrap script in overlay or full mode.
  3. Review the generated AGENTS.md block and adapt command names to the repo.
  4. Keep existing project-specific instructions, but move durable detail from AGENTS.md into the generated docs.
  5. Wire the generated validation script into the repo's native check flow.
  6. If the repo moves fast or uses many agents, optionally enable garbage collection.

Bootstrap command

Run the bundled script from this skill directory:

python3 scripts/bootstrap_project.py --repo /path/to/repo --mode overlay

Optional flags:

  • --mode overlay|full
  • --with-gc to scaffold the garbage-collection report
  • --dry-run to preview changes
  • --force to overwrite generated files
  • --no-claude-link to skip the CLAUDE.md -> AGENTS.md symlink

What the bootstrap adds

On first application, the scaffold normally creates or updates:

  • AGENTS.md with a short agent-navigation block
  • CLAUDE.md symlink to AGENTS.md unless disabled
  • docs/agent/index.md
  • docs/agent/architecture.md
  • docs/agent/specs.md
  • docs/agent/plans.md
  • docs/agent/quality.md
  • docs/agent/reliability.md
  • docs/agent/security.md
  • scripts/agent_repo_check.py
  • optionally docs/agent/garbage-collection.md
  • optionally scripts/agent_gc_report.py

Operating rules

1. AGENTS.md is a router

Do not turn AGENTS.md into a giant handbook.

  • keep it short
  • link outward to durable docs
  • update links when docs move
  • reserve AGENTS.md for task-routing instructions and repo-specific operational constraints

2. Durable knowledge lives in docs

Put medium- and long-lived repo knowledge in docs/agent/ or the repo's main docs tree.

Examples:

  • architecture boundaries
  • product or integration specs
  • current plans
  • quality gates and invariants
  • reliability expectations
  • security assumptions and trust boundaries

3. Progressive disclosure beats giant prompts

Only read the docs needed for the task.

  • start at docs/agent/index.md
  • open the relevant leaf docs
  • avoid loading unrelated docs into context
  • add new docs to the index so future agents can discover them quickly

4. Mechanical checks beat soft reminders

Prefer checks that can fail fast in CI or local validation:

  • missing required docs
  • missing frontmatter fields
  • stale review dates
  • docs not linked from the index
  • AGENTS.md missing navigation links

5. Garbage collection is optional but useful

Enable the GC loop when the repo has high change velocity, many generated edits, or recurring drift.

The default GC report looks for:

  • stale docs
  • oversized files
  • suspicious filenames like final-final or v2
  • lingering TODO or FIXME clusters
  • docs that are not linked from the index

References to read only when needed

  • Read references/bootstrap-playbook.md when planning the first rollout for a repo.
  • Read references/docs-blueprint.md when adapting the doc taxonomy or frontmatter.
  • Read references/quality-gates.md when wiring checks into CI or repo-native tooling.
  • Read references/garbage-collection.md when enabling scheduled cleanup or review loops.

Acceptance checklist

Before you finish a rollout, confirm:

  • AGENTS.md routes to docs instead of duplicating them
  • docs/agent/index.md points to every active leaf doc
  • the generated docs have owners and last_reviewed dates
  • scripts/agent_repo_check.py passes
  • the repo's native check command includes the validation script or an equivalent wrapper
  • garbage collection is either enabled intentionally or documented as deferred

Do not do this

  • do not rewrite a mature doc system unless the user asks
  • do not duplicate the same guidance in AGENTS.md and docs/agent/*
  • do not add stack-specific CI assumptions without checking the repo
  • do not enable automatic destructive cleanup; GC should surface candidates, not delete code blindly

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

71.53%
按下载量换算3,313

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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