Token导航 LogoToken导航

Agent Skill · 研究检索

harness-engineer(线束工程师)

harness-engineer 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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。

harness-engineer 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 9,307。

总安装 14,512周安装 617GitHub Stars 4下载 5,084核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
5.3.0

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:harness-engineer(线束工程师)
来源仓库:https://github.com/louis-szeto/harness-engineer
安装命令:
openclaw skills install harness-engineer
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 harness-engineer

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于记录任务执行中的错误与经验,推动 Agent 持续改进能力边界。适合在 OpenClaw 中希望让代理沉淀问题、修正逻辑或积累最佳实践时使用。将存储库转换为自我优化的软件系统,支持长期自主演进。安装前需确认写入权限,防止因权限不足导致日志丢失或中断。建议定期审查日志内容,避免无效信息堆积影响后续决策。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install harness-engineer;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

harness-engineer 怎么安装?

在终端执行 openclaw skills install harness-engineer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

harness-engineer 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

harness-engineer 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

harness-engineer 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

harness-engineer 用的人多吗?

公开数据是累计安装 14,512 次,近一周安装 617 次,下载 5,084 次,GitHub 4 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

harness-engineer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
harness-engineer

线束工程师 · 本页

14,5124接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
harness-engineer
description
>
metadata
platform_enforcement
required
platform_note
>
enforcing
MCP tool router (blocked sensitive reads, protected writes, output
required_platform_capabilities
safe_defaults

HARNESS ENGINEER

A production-grade skill for Claude Code and OpenClaw that transforms a repository into a self-improving software system using six core harness engineering principles.


SIX CORE PRINCIPLES

P1: CONTEXT ENGINEERING

Treat context as a finite, precious resource. Curate aggressively. See: runtime/context-engineering.md, runtime/compaction.md

P2: TOOL USAGE

Each sub-agent receives only the tools it needs -- no more. See: tools/TOOL_REGISTRY.md, references/mcp-tools.md

P3: VERIFICATION MECHANISM

Every output is verified by someone other than who produced it. See: agents/reviewer.md, references/testing-standards.md

P4: STATUS MANAGEMENT

State lives outside the context window, in the repo. See: runtime/status-management.md, templates/handoff.md

P5: OBSERVABILITY AND FEEDBACK CLOSED-LOOP

Track what happens. Feed failures back into the harness, not the code. See: runtime/observability.md, runtime/memory-system.md

P6: HUMAN SUPERVISION

Humans approve high-impact events. The harness surfaces them explicitly. See: runtime/autonomy-rules.md, runtime/prioritization.md


NON-NEGOTIABLE RULES

  1. CLAUDE.md / AGENTS.md IS GROUND TRUTH -- read it first, every session.
  2. CODEBASE OVER DOCS -- when they conflict, trust the code.
  3. 40% CONTEXT RULE -- compact or sub-agent before crossing 40% of context window.
  4. NO IMPLEMENTATION WITHOUT a research output, a plan, and validation criteria.
  5. GENERATION AND REVIEW ARE ALWAYS SEPARATE -- never the same agent.
  6. FAILURE = HARNESS GAP -- fix the harness, not just the symptom.
  7. OPTIMIZATION PRIORITY: Security => Correctness => Reliability => Performance =>

Memory => Maintainability => Cost

  1. MINIMAL SCOPE PER SUBAGENT — Estimate codebase size first (use platform file-count or line-count capability — never raw shell). If >5K lines, split into multiple subagents by module/feature/layer. Pin exact files to read (no wandering). One research doc + one code area per subagent max. If a subagent gets killed or times out, the scope was too large — split further.

Adaptive timeouts: Default timeouts are guidelines, not hard kills. Check process logs before killing — if the agent is actively producing output, extend the timeout instead of killing. Only kill-and-split if the agent is silent/stuck for >10min or producing garbage. Scale timeouts by effort: S-effort=15min, M-effort=20min, L-effort=30-40min.

  1. SUBAGENT PERMISSION MODE — Subagents are spawned by the platform using its native agent mechanism. The permission mode is set by the platform, NOT by this skill. The skill MUST NOT mandate any specific spawn command or permission mode — that decision belongs to the platform's enforcement layer (see PLATFORM_REQUIREMENTS.md Section 8). If the platform's default permission mode is insufficient, the platform operator configures it — the skill never overrides it.
  2. ACTIVE MONITORING — Every time you launch a new batch of subagents, track session IDs, expected output files, and remaining queue. If the platform provides a cron/scheduler, use it to detect dead agents. If no scheduler is available, check agent status before each dispatch step. Dead agents stall the pipeline — detect them early.
  3. MAX PARALLEL = 5 — Up to 5 Claude Code agents running simultaneously. If rate/API limit errors encountered, drop to 4, then 3, etc until no errors. Resume increasing after 5 clean minutes.
  4. TOKEN EXHAUSTION RECOVERY: If ALL active agents hit rate/API limits (429/500), tokens are exhausted. Wait for token refresh before retrying. If the platform provides a scheduler, set a recovery job to resume after refresh. If no scheduler is available, the human operator must manually restart the cycle.
  5. 10-MIN STUCK KILL — If any agent produces no output for >10 minutes, log the issue, kill it, and split the task into smaller subtasks before respawning. MUST ALWAYS set a cron job when a subagent is given a command that will run for a while, to periodically check on its progress.
  6. TRACKING EVERYWHERE — Every phase, cycle, and step writes to tracking logs. DISPATCH-TRACK, error log, compact summaries, progress logs. Recovery must be able to pick up from any interruption point.

SAFE START GUIDE

Before anything else: read PLATFORM_REQUIREMENTS.md and verify every item. The harness depends on platform enforcement that cannot be checked from these files alone.

Step 1 -- Verify platform requirements (PLATFORM_REQUIREMENTS.md) Run through the five platform capability checks before any other step.

Step 2 -- Sandbox first Run on a throwaway branch. Observe one single-pass cycle before enabling continuous mode.

Step 2 -- Review CONFIG.yaml before every run | loop_mode | single-pass | Change after sandbox validation | | max_parallel_agents | 3 | Increase after confirming behavior | | block_destructive... | true | Never change |

PRs always require human approval. There is no auto-merge.

Step 3 -- Protect main branch Require human reviewers on main/trunk in your git host.

Step 4 -- Graduation path: single-pass => maintenance => continuous


HOW TO USE THIS SKILL

When activated in Claude Code or OpenClaw, read in this order:

  1. CLAUDE.md or AGENTS.md if present (base context)
  2. CONFIG.yaml (runtime settings)
  3. runtime/loop.md (execution model)
  4. runtime/context-engineering.md (context budget rules)
  5. runtime/status-management.md (restore checkpoint if resuming)
  6. MEMORY.md (prior failure context)
  7. agents/dispatcher.md (task decomposition model, worktree agent)
  8. Begin the loop

REFERENCE FILES

FileWhen to read
CLAUDE.md / AGENTS.mdFirst, every session -- base knowledge
CONFIG.yamlAt startup
MEMORY.mdAt startup and after every failure
runtime/loop.mdEach loop cycle
runtime/context-engineering.mdContinuously -- governs context budget
runtime/compaction.mdWhen compacting context within a phase
runtime/status-management.mdAt startup (resume) and after each task
runtime/observability.mdAfter VERIFY phase
runtime/memory-system.mdWhen writing or querying memory
runtime/self-improvement.mdAfter any failure
runtime/prioritization.mdWhen selecting the next task
runtime/autonomy-rules.mdWhen blocked or at human gate
agents/dispatcher.mdBefore decomposing any task (worktree agent)
agents/researcher.mdResearch phase (Q-Agent + R-Agent model)
agents/planner.mdPlan phase (3-phase: design, outline, master plan)
agents/implementer.mdImplement phase (worktree-driven execution)
agents/reviewer.mdReview cycle
agents/debugger.mdOn any failure
agents/optimizer.mdOptimization mode
agents/garbage-collector.mdGC interval
tools/TOOL_REGISTRY.mdBefore any tool call
tools/tool-router.mdRouting and redaction rules
tools/execution-protocol.mdFull tool call lifecycle
references/harness-rules.mdCore constraints
references/testing-standards.mdBefore writing or running tests
references/security-performance.mdBefore any implementation
references/simplification-checklist.mdDuring review and refactoring
references/git-workflow.mdBefore any commit or PR
references/mcp-tools.mdMCP tool definitions and per-agent sets
references/sensitive-paths.mdForbidden read paths -- enforced in-skill
references/constraints.mdActive prevention rules
templates/Plans, ADRs, handoffs, status docs

平台分布

OpenClaw

83.29%
按下载量换算4,234

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills