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compound-eng-reflect复合工程反映

Agent Skill

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。

总安装

8,557

周安装

353

GitHub Stars

公开资料未说明

下载量

2,796
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: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

简介

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。

SKILL.md

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).

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

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

权限需确认

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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 warning/failed 结果,不能写成本站确认安全。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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