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Agent Skill · 研究检索

evanflow-iterate(埃文流迭代)

evanflow-iterate 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-iterate 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,权限为接触敏感数据;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 72,323。

总安装 188周安装 8GitHub Stars 337下载 66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-iterate(埃文流迭代)
来源仓库:https://github.com/evanklem/evanflow
仓库路径:skills/evanflow-iterate
安装命令:
npx skills add https://github.com/evanklem/evanflow --skill evanflow-iterate
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-iterate

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-iterate 执行质量检查循环,直到满足预定标准才停止迭代过程。适用于代码 polish、clean up 等非功能性改进场景,确保交付物达到生产要求。使用时需运行项目特定检查命令(如 tsc --noEmit、eslint),并根据输出持续优化。

安装前请确认仓库权限和维护状态,跳过 trivially correct 的修改,专注深层质量问题。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-iterate

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-iterate;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

evanflow-iterate 怎么安装?

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

evanflow-iterate 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-iterate 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。

evanflow-iterate 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-iterate 用的人多吗?

公开数据是累计安装 188 次,近一周安装 8 次,下载 66 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-iterate 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。

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。
  • 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。

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-iterate

埃文流迭代 · 本页

188337接触敏感数据未明确通过

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: Iterate

Vocabulary

See evanflow meta-skill. Key terms: deep modules, deletion test, vertical slice.

When to Use

  • After evanflow-executing-plans finishes all tasks
  • After any non-trivial implementation
  • When asked to "polish this" / "review this" / "make sure it's clean"

SKIP when: the change is one line or trivially correct.

The Loop

Repeat until stopping condition met:

1. Run All Quality Checks

Run the project's quality checks — exact commands are project-specific (see CLAUDE.md or the project's README). Typical examples across stacks:

# typecheck — one of:
tsc --noEmit          # TypeScript
pnpm typecheck        # if scripted
cargo check           # Rust
go vet ./...          # Go

# lint — one of:
pnpm lint
eslint .
cargo clippy
ruff check .

# test — one of:
pnpm test
pytest
cargo test
go test ./...

If any check fails: fix and restart the loop. Don't proceed to step 2 with broken checks.

2. Re-Read the Diff With Fresh Eyes

git diff             # working-tree changes
git diff HEAD~N..HEAD  # if reviewing a series of past commits

For each changed file, look critically for:

  • Dead code — leftover console.logs, commented-out blocks, unused imports/vars
  • Naming — does the name match what the code does? (Ubiquitous language matters; see evanflow-glossary.)
  • Deletion test — does each new module earn its existence? Could removing it improve the code?
  • Magic strings/numbers — should be enums or constants per CLAUDE.md
  • Error handling — boundary inputs validated? External calls wrapped? Loading/error/empty states in UI?
  • Type safety — any any, as, @ts-ignore? Justified?
  • Security — authenticatedProcedure where needed? Resource ownership re-derived from ctx.user? Per CLAUDE.md.
  • Test coverage — does the new behavior have a test? Does the test verify behavior, not internals?
  • Test assertion correctness — research shows 62% of LLM-generated assertions are wrong. For each assertion, would a one-character bug in the implementation still let it pass? If yes, the assertion is too weak.
  • Scope creep — anything in the diff that wasn't in the plan?
  • Comments — only WHY notes that explain non-obvious constraints. Delete WHAT comments.

Fix what you find. Then restart from step 1.

2.5. Five Failure Modes Check

Industry research identifies five predictable failure modes in agentic coding. After step 2's diff review, do an explicit pass against each:

  • (a) Hallucinated actions — did the implementation invent file paths, env vars, IDs, function names, library APIs, or other external values that aren't authoritatively confirmed? (Example: a process.env.STRIPE_SECRET_KEY reference when the actual var name is STRIPE_SK.)
  • (b) Scope creep — does the diff touch files or behaviors not in the plan? Bundled refactors or stylistic changes that should be separate PRs?
  • (c) Cascading errors — was a failure suppressed/caught/wrapped in a way that hides root cause from callers? Are there silent fallbacks that mask bugs (try/catch returning empty arrays, default values that paper over missing data)?
  • (d) Context loss — does the diff contradict earlier decisions in the session, the plan, CLAUDE.md, or CONTEXT.md? Names, conventions, invariants?
  • (e) Tool misuse — used the wrong tool (e.g., Bash for file reads, MCP server when CLI was simpler), or used a tool with wrong parameters (e.g., grep without proper escaping, Edit without reading first)?

For each mode flagged, fix and restart from step 1.

3. (UI work only) Visual Verification

If the diff touches frontend page or component files and the change has visible output:

Default approach (no Playwright needed):

# Make sure your dev server is running first (e.g., pnpm dev, npm run dev, etc.)
chromium --headless --no-sandbox \
  --screenshot=/tmp/iter-$(date +%s).png \
  --window-size=1440,900 \
  http://localhost:<port>/<route>

(If your project doesn't have chromium, substitute google-chrome --headless or chrome --headless with the same flags.)

Then read the screenshot:

Read /tmp/iter-*.png

Check against:

  • Any brainstorm mockup or design comp the project maintains
  • The project's design system (colors, spacing, typography, component patterns documented in CLAUDE.md)
  • Responsive behavior — also screenshot at --window-size=390,844 (mobile)

If you need interaction (click, fill, observe modal): use Playwright MCP. If MCP fails with "chrome not found", configure it to use your installed Chromium binary by adding "--executable-path", "/path/to/chromium" to args in the Playwright .mcp.json. Don't fight the MCP — fix it once, then use it.

4. Stopping Condition

Stop the loop when all are true:

  • All quality checks pass
  • Re-read the diff and find no new issues you'd want to fix
  • (UI) Screenshot matches expectation, OR you've confirmed with the user

Hard cap: 5 iterations. If you're still finding issues at iteration 5, the original plan was wrong — stop and ask the user. Don't iterate forever.

Hard Rules

  • Don't iterate just to iterate. If everything is clean on the first pass, stop. Don't invent issues.
  • Fix root causes, not symptoms. A linter warning that you suppress instead of fix is debt.
  • Never auto-commit, never auto-stage, never auto-finish. Iteration produces a clean working tree. After convergence, report what was done and stop. The user decides whether to commit, refactor further, or change direction.
  • Never iterate past the user. If the user says "good enough," stop. Their judgment beats the loop.
  • Visual verification requires a running dev server. If the dev server isn't up, ask the user to start it (don't try to start it yourself unless the project has a documented "start dev" skill).

Hand-offs

  • Loop converged, all clean → report what was done and STOP. Await user direction. No auto-finish, no staging, no commit.
  • Loop hit cap with issues remaining → back to evanflow-writing-plans (plan was wrong)
  • Found architectural issues → evanflow-improve-architecture
  • Found a bug → evanflow-debug

平台分布

Codex

33.59%
按下载量换算22

Claude

32.96%
按下载量换算22

Cursor

17.72%
按下载量换算12

Gemini CLI

9.27%
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

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。

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