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Agent Skill · 研究检索

compound-eng-reflect(复合工程反映)

compound-eng-reflect 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。

compound-eng-reflect 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为权限未标注;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 14,431。

总安装 8,557周安装 353GitHub Stars 公开资料未说明下载 2,796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
3.0.5

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:compound-eng-reflect(复合工程反映)
来源仓库:https://github.com/iliaal/compound-eng-reflect
安装命令:
openclaw skills install compound-eng-reflect
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 compound-eng-reflect

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。

compound-eng-reflect 提供会话回顾与技能审核功能,支持经验教训总结。适用于 OpenClaw 中被要求反思进展顺利或出错环节、召开回顾会议时使用。帮助识别能力缺口与改进点,形成可持续优化闭环。安装通过 clawhub 完成,建议定期运行以积累团队知识资产。

涉及敏感信息回顾时应脱敏处理,遵守数据隐私规范。

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 compound-eng-reflect;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

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

compound-eng-reflect 怎么安装?

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

compound-eng-reflect 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。

compound-eng-reflect 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

compound-eng-reflect 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。

compound-eng-reflect 用的人多吗?

公开数据是累计安装 8,557 次,近一周安装 353 次,下载 2,796 次。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。

compound-eng-reflect 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。

不太适合

  • 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
compound-eng-reflect

复合工程反映 · 本页

8,557—权限未标注MIT-0有提醒

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
ia-reflect
class
tool
description
>-

Reflect

Success Criteria

  • Every mistake/friction point cites the specific moment and its impact
  • Improvements are actionable and prioritized (cap defined in step 4)
  • Each skill audit proposes measurable changes (not vague suggestions)
  • User is asked which items to persist to memory
  • If review activity occurred, review-trap patterns are captured to persistent memory, or explicitly marked as "none"

Process

1. Session Review

Scan the full conversation. For each finding, cite the specific exchange (quote or paraphrase) and its impact.

CategorySignal
MistakesWrong outputs, incorrect assumptions, hallucinated facts
FrictionRepeated clarifications, verbose responses, misread intent
Wasted effortWork discarded, wrong approaches tried first
WinsApproaches worth repeating, smooth interactions

Skip one-time typos, external tool failures, and issues outside agent control.

2. Review Activity Scan (if applicable)

If the session included PR or MR review activity in either direction, run this scan before moving on. Skip only if no reviews happened.

Inbound (my code was reviewed): For each review comment received:

  • Did I accept it? If yes, what pattern did the reviewer catch that I missed? Is it a recurring blind spot? Capture the one-liner to persistent memory.
  • Did I push back? If I was right and the reviewer was wrong, nothing to capture. If I was wrong and had to retract mid-thread, capture what I learned.

Outbound (I reviewed someone else's code): For each comment I authored:

  • Was it accepted? Nothing to capture -- good call.
  • Was it rejected with a valid counter? That's a review trap. Capture the pattern: what heuristic did I apply that produced a wrong comment?

"No harvestable items" is a valid outcome -- say so explicitly. Don't let the step quietly drop off.

3. Operational Learnings

Before listing improvements, scan the session for operational insights worth preserving. Apply the 5-minute filter: would knowing this save 5+ minutes in a future session? If yes, include it. Examples: a project-specific quirk, a command that failed unexpectedly, an approach that worked better than expected.

4. Improvements

Numbered list of concrete improvements, ranked by impact. Each item: one sentence, imperative, actionable. Cap at 10 items: if more surface, the bottom items are noise -- drop them rather than batching or splitting.

Ask: *"Which of these should I remember for future chats?"*

Save approved items to memory files at ~/.claude/projects/<project-slug>/memory/ (replace <project-slug> with the slug matching the current working directory, e.g., -home-ilia-ai-compound-engineering-plugin) using the Write tool with proper frontmatter (see MEMORY.md index).

5. Skill Audit (if skills were used)

For each skill invoked during the session:

A. Self-check gate -- If the skill lacks success criteria + verification loop:

  • Add ## Success Criteria at top (3-5 measurable checks)
  • Add ## Self-Check at bottom: "Verify all success criteria are met before presenting output. If not, iterate (max 5 times)."

B. Token efficiency -- Flag: redundant phrasing, mergeable sections, oversized examples, "Claude already knows this" content, inert frontmatter metadata.

C. Other -- Missing edge cases, vague directives (rewrite as measurable criteria or remove), naked negations (add "do Y instead" or remove).

Present proposed changes as diffs. Ask: *"Apply these? (all / pick / skip)"*

6. Capture Markers

The remember: prefix is the highest-confidence capture signal. When the user writes a message beginning with remember:, treat everything after the colon as a memory candidate — no interpretation required. Save directly to the appropriate memory file with a one-line summary and the user's exact phrasing. Example: remember: we never use Pest, always PHPUnit → save to feedback_phpunit_over_pest.md.

Correction patterns to watch for (lower-confidence, batch these for review at /ia-reflect time):

  • "no, use X" / "actually, X" / "don't use Y, use X"
  • "stop doing X" / "never X"
  • "that's wrong — the right way is..."
  • repeated clarifications of the same thing within a session

Optional capture hook: a UserPromptSubmit hook can pattern-match the markers above into ~/.claude/learnings-queue.json as the user types, so /ia-reflect processes the queue deterministically instead of re-scanning the full transcript. Not shipped with this skill; document the convention and leave implementation to users who need it.

7. Pattern Detection

If 2+ similar tasks appear that no existing skill covers, suggest a new skill (1-2 sentence description). Create only after confirmation.

Proactive trigger: When the user corrects you, clarifies the same thing twice, or shows frustration, append: "Tip: Type /ia-reflect when you're ready -- I'll review what we can improve."

Self-Check

Before presenting output, verify all success criteria are met. If any fail, revise (max 5 iterations).

平台分布

OpenClaw

83.78%
按下载量换算2,342

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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