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evanflow-compactEvanflow 紧凑型

Agent Skill

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。

总安装

196

周安装

8

GitHub Stars

337

下载量

63
CodexClaudeCursorGemini CLI

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

unknown

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: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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

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

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