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Agent Skill · 研究检索

evanflow-compact(Evanflow 紧凑型)

evanflow-compact 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

evanflow-compact 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,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 71,089。

总安装 196周安装 8GitHub Stars 337下载 63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
unknown

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:evanflow-compact(Evanflow 紧凑型)
来源仓库:https://github.com/evanklem/evanflow
仓库路径:skills/evanflow-compact
安装命令:
npx skills add https://github.com/evanklem/evanflow --skill evanflow-compact
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/evanklem/evanflow --skill evanflow-compact

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。

evanflow-compact 解决上下文漂移问题,通过定期压缩保持推理质量不退化。适用于长时任务或多步推理场景,防止 stale/contradictory 信息降低响应准确性。使用时需识别低信号内容并主动移除,维持高价值信息的注意力窗口占比。

安装前请确认仓库权限和维护状态,避免因 token 限制而忽略漂移问题,应优先处理信号衰减。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/evanflow-compact

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/evanklem/evanflow --skill evanflow-compact;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

evanflow-compact 怎么安装?

在终端执行 npx skills add https://github.com/evanklem/evanflow --skill evanflow-compact。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

evanflow-compact 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

evanflow-compact 需要什么权限?

权限级别是只读。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。

evanflow-compact 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

evanflow-compact 用的人多吗?

公开数据是累计安装 196 次,近一周安装 8 次,下载 63 次,GitHub 337 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。

evanflow-compact 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 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
evanflow-compact

Evanflow 紧凑型 · 本页

196337只读未明确通过

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

EvanFlow: Compact

Why this exists: Context drift kills agents before context limits do. Industry research found ~65% of enterprise AI failures in 2025 traced to context drift or memory loss in multi-step reasoning, NOT raw token exhaustion. The agent's ability to reason degrades as context fills with stale, redundant, or contradictory information — long before the hard token limit.

Vocabulary

See evanflow meta-skill. Specific to this skill:

  • Drift — agent reasoning degrades as context fills with stale/contradictory/low-signal content. Manifests as: slower responses, more clarifying questions about already-established facts, forgetting recent decisions, inconsistent application of rules.
  • Anchor — a high-signal artifact (CONTEXT.md, ADR, current plan, current diff) that survives compaction.
  • Compaction — replacing a long stretch of conversation with a shorter summary that preserves the anchors plus key decisions.
  • Branch isolation — moving a side-quest into a separate session so the main session stays focused.

When to Use

Trigger this skill when ANY of these are true:

  • A major phase boundary just completed (e.g., Phase A done, about to start Phase B). Proactive compaction at clean boundaries is much higher quality than reactive compaction mid-flow.
  • The session has accumulated 30+ turns of dense back-and-forth.
  • You notice drift symptoms: agent re-asking already-answered questions, contradicting earlier decisions, restating context the user already established, slower or vaguer responses.
  • Context display warns of high utilization (rough threshold: ~70%+).
  • About to fork into an unrelated side-quest that could be its own session.

SKIP when: the session is short, focused, and progressing without drift symptoms. Compaction has cost (loses fidelity); only invoke when the cost of NOT compacting is higher.

The Strategies (pick one based on situation)

Strategy 1 — Proactive Summarize-and-Continue (preferred at clean boundaries)

When a major phase just finished cleanly:

  1. Identify anchors: what files, decisions, and open questions must survive? List them explicitly.
  2. Author a compaction summary in the conversation as a markdown block titled ## Phase Summary (compaction anchor). Include:

- What was decided - What was built (file list + one-line each) - What's verified - What's deferred / open for next phase - Pointers to the persistent artifacts (memory files, CONTEXT.md sections, ADRs, plan file)

  1. Tell the user: "I'm at a clean boundary. Want me to compact context here? You can /clear and re-orient from this summary + the saved memory."
  2. If user says yes → they /clear, then start the next phase fresh with the summary in front of them.

Strategy 2 — Save Anchors to Memory, Then /clear

When the session is heavy and the next move is unclear:

  1. Audit what's been learned that ISN'T already in memory or files. Anything important?
  2. Save those things to memory (~/.claude/projects/<project>/memory/*.md) as appropriate types (user / feedback / project / reference).
  3. Save any in-progress thinking to a plan file or a scratch doc.
  4. Tell the user: "Important state is now persisted in memory + <files>. Recommend /clear and re-engage fresh."

Strategy 3 — Branch Isolation

When a side-quest emerges that's unrelated to the main thread:

  1. Don't pull it into this session. Tell the user: "This is a separate concern from <main thread>. Recommend handling it in a fresh session — your context will stay clean and the side-quest gets full attention."
  2. If the user wants to do it now anyway, save the main-thread state per Strategy 2 first, then proceed.

Strategy 4 — Reactive Mid-Flow Summarization

When drift symptoms appear and you can't reach a clean boundary:

  1. Stop the current task.
  2. Audit what's been established that's still relevant. Discard the rest mentally.
  3. Author an explicit "current state" block in the conversation summarizing only the live concerns.
  4. Continue from there, referencing the new summary as the truth.

This is less effective than proactive compaction — use only when needed.

Drift Symptoms Checklist

Watch for these. Each is a signal to consider compaction:

  • Agent re-asks a question the user already answered
  • Agent restates the user's instructions back ("just to confirm, you want X" when X has been confirmed multiple times)
  • Agent contradicts its own earlier decision without acknowledging the change
  • Agent forgets a constraint that was set in the same session (e.g., "don't auto-commit" being violated)
  • Agent's responses become noticeably slower or vaguer
  • Agent struggles to find files it referenced minutes ago
  • Tool calls become less efficient (more reads of the same file, more redundant checks)

If 2+ symptoms in the last few turns, invoke this skill.

Hard Rules

  • Never compact without an explicit checkpoint. Always write down what's being preserved before any /clear or summary swap. Lost context that was assumed-saved is the worst failure mode.
  • Anchors first, then compaction. Save to memory / files BEFORE asking the user to /clear.
  • User initiates /clear. Like commits, this is a user-controlled action. The skill recommends; the user does.
  • Compaction is lossy by design. Be explicit with the user about what's being summarized away. They might catch something you'd drop that they want kept.
  • Never auto-commit, never auto-stage. Same rule as everywhere else.

Hand-offs

  • After Strategy 1 or 2, before /clear → the user runs /clear, then re-engages from the saved anchor + memory
  • Drift symptoms persist after compaction → there may be a deeper architectural issue with how state is being managed; consider evanflow-improve-architecture
  • A new feature/change is about to start with fresh context → resume evanflow-brainstorming or evanflow-writing-plans as appropriate

平台分布

Codex

33.9%
按下载量换算21

Claude

32.19%
按下载量换算20

Cursor

19.87%
按下载量换算13

Gemini CLI

9.42%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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。目前只有一个公开来源,建议同时核对源仓库。

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