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Agent Skill · 研究检索

evaluate-presets(评估预设)

evaluate-presets 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。

evaluate-presets 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,权限为执行命令;许可证为 MIT,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 46,467。

总安装 776周安装 33GitHub Stars 2,832下载 272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

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:evaluate-presets(评估预设)
来源仓库:https://github.com/mikeyobrien/ralph-orchestrator
仓库路径:skills/evaluate-presets
安装命令:
npx skills add https://github.com/mikeyobrien/ralph-orchestrator --skill evaluate-presets
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/mikeyobrien/ralph-orchestrator --skill evaluate-presets

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。

evaluate-presets 用于系统化测试 hat collection 预设配置,验证路由逻辑正确性。通过 shell 脚本直接调用,无需复杂编排,支持单个或多个预设评估。适用于 TDD 红绿循环或批量审计,输出结果可用于优化 hat 分配策略。

安装前需确认 bash 环境和脚本权限,注意 preset 命名规范。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/evaluate-presets

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/mikeyobrien/ralph-orchestrator --skill evaluat…;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

    它需要执行命令,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

evaluate-presets 怎么安装?

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

evaluate-presets 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。

evaluate-presets 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/mikeyobrien/ralph-orchestrator --skill evaluate-presets;npx skills add mikeyobrien/ralph-orchestrator --skill "evaluate-presets" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

evaluate-presets 是免费的吗?

来源标注的许可证是 MIT。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。

evaluate-presets 用的人多吗?

公开数据是累计安装 776 次,近一周安装 33 次,下载 272 次,GitHub 2,832 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。

evaluate-presets 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 来源仓库有 2,832 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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 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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
evaluate-presets

评估预设 · 本页

7762,832执行命令MIT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

Evaluate Presets

Overview

Systematically test all hat collection presets using shell scripts. Direct CLI invocation—no meta-orchestration complexity.

When to Use

  • Testing preset configurations after changes
  • Auditing the preset library for quality
  • Validating new presets work correctly
  • After modifying hat routing logic

Quick Start

Evaluate a single preset:

./tools/evaluate-preset.sh tdd-red-green claude

Evaluate all presets:

./tools/evaluate-all-presets.sh claude

Arguments:

  • First arg: preset name (without .yml extension)
  • Second arg: backend (claude or kiro, defaults to claude)

Bash Tool Configuration

IMPORTANT: When invoking these scripts via the Bash tool, use these settings:

  • Single preset evaluation: Use timeout: 600000 (10 minutes max) and run_in_background: true
  • All presets evaluation: Use timeout: 600000 (10 minutes max) and run_in_background: true

Since preset evaluations can run for hours (especially the full suite), always run in background mode and use the TaskOutput tool to check progress periodically.

Example invocation pattern:

Bash tool with:
  command: "./tools/evaluate-preset.sh tdd-red-green claude"
  timeout: 600000
  run_in_background: true

After launching, use TaskOutput with block: false to check status without waiting for completion.

What the Scripts Do

evaluate-preset.sh

  1. Loads test task from tools/preset-test-tasks.yml (if yq available)
  2. Creates merged config with evaluation settings
  3. Runs Ralph with --record-session for metrics capture
  4. Captures output logs, exit codes, and timing
  5. Extracts metrics: iterations, hats activated, events published

Output structure:

.eval/
├── logs/<preset>/<timestamp>/
│   ├── output.log          # Full stdout/stderr
│   ├── session.jsonl       # Recorded session
│   ├── metrics.json        # Extracted metrics
│   ├── environment.json    # Runtime environment
│   └── merged-config.yml   # Config used
└── logs/<preset>/latest -> <timestamp>

evaluate-all-presets.sh

Runs all 12 presets sequentially and generates a summary:

.eval/results/<suite-id>/
├── SUMMARY.md              # Markdown report
├── <preset>.json           # Per-preset metrics
└── latest -> <suite-id>

Presets Under Evaluation

PresetTest Task
tdd-red-greenAdd is_palindrome() function
adversarial-reviewReview user input handler for security
socratic-learningUnderstand HatRegistry
spec-drivenSpecify and implement StringUtils::truncate()
mob-programmingImplement a Stack data structure
scientific-methodDebug failing mock test assertion
code-archaeologyUnderstand history of config.rs
performance-optimizationProfile hat matching
api-designDesign a Cache trait
documentation-firstDocument RateLimiter
incident-responseRespond to "tests failing in CI"
migration-safetyPlan v1 to v2 config migration

Interpreting Results

Exit codes from evaluate-preset.sh:

  • 0 — Success (LOOP_COMPLETE reached)
  • 124 — Timeout (preset hung or took too long)
  • Other — Failure (check output.log)

Metrics in metrics.json:

  • iterations — How many event loop cycles
  • hats_activated — Which hats were triggered
  • events_published — Total events emitted
  • completed — Whether completion promise was reached

Hat Routing Performance

Critical: Validate that hats get fresh context per Tenet #1 ("Fresh Context Is Reliability").

What Good Looks Like

Each hat should execute in its own iteration:

Iter 1: Ralph → publishes starting event → STOPS
Iter 2: Hat A → does work → publishes next event → STOPS
Iter 3: Hat B → does work → publishes next event → STOPS
Iter 4: Hat C → does work → LOOP_COMPLETE

Red Flags (Same-Iteration Hat Switching)

BAD: Multiple hat personas in one iteration:

Iter 2: Ralph does Blue Team + Red Team + Fixer work
        ^^^ All in one bloated context!

How to Check

1. Count iterations vs events in session.jsonl:

# Count iterations
grep -c "_meta.loop_start\|ITERATION" .eval/logs/<preset>/latest/output.log

# Count events published
grep -c "bus.publish" .eval/logs/<preset>/latest/session.jsonl

Expected: iterations ≈ events published (one event per iteration) Bad sign: 2-3 iterations but 5+ events (all work in single iteration)

2. Check for same-iteration hat switching in output.log:

grep -E "ITERATION|Now I need to perform|Let me put on|I'll switch to" \
    .eval/logs/<preset>/latest/output.log

Red flag: Hat-switching phrases WITHOUT an ITERATION separator between them.

3. Check event timestamps in session.jsonl:

cat .eval/logs/<preset>/latest/session.jsonl | jq -r '.ts'

Red flag: Multiple events with identical timestamps (published in same iteration).

Routing Performance Triage

PatternDiagnosisAction
iterations ≈ events✅ GoodHat routing working
iterations << events⚠️ Same-iteration switchingCheck prompt has STOP instruction
iterations >> events⚠️ Recovery loopsAgent not publishing required events
0 events❌ BrokenEvents not being read from JSONL

Root Cause Checklist

If hat routing is broken:

  1. Check workflow prompt in hatless_ralph.rs:

- Does it say "CRITICAL: STOP after publishing"? - Is the DELEGATE section clear about yielding control?

  1. Check hat instructions propagation:

- Does HatInfo include instructions field? - Are instructions rendered in the ## HATS section?

  1. Check events context:

- Is build_prompt(context) using the context parameter? - Does prompt include ## PENDING EVENTS section?

Autonomous Fix Workflow

After evaluation, delegate fixes to subagents:

Step 1: Triage Results

Read .eval/results/latest/SUMMARY.md and identify:

  • ❌ FAIL → Create code tasks for fixes
  • ⏱️ TIMEOUT → Investigate infinite loops
  • ⚠️ PARTIAL → Check for edge cases

Step 2: Dispatch Task Creation

For each issue, spawn a Task agent:

"Use /code-task-generator to create a task for fixing: [issue from evaluation]
Output to: tasks/preset-fixes/"

Step 3: Dispatch Implementation

For each created task:

"Use /code-assist to implement: tasks/preset-fixes/[task-file].code-task.md
Mode: auto"

Step 4: Re-evaluate

./tools/evaluate-preset.sh <fixed-preset> claude

Prerequisites

  • yq (optional): For loading test tasks from YAML. Install: brew install yq
  • Cargo: Must be able to build Ralph

Related Files

  • tools/evaluate-preset.sh — Single preset evaluation
  • tools/evaluate-all-presets.sh — Full suite evaluation
  • tools/preset-test-tasks.yml — Test task definitions
  • tools/preset-evaluation-findings.md — Manual findings doc
  • presets/ — The preset collection being evaluated

平台分布

Claude Code

28.49%
按下载量换算77

Gemini CLI

22.61%
按下载量换算61

Antigravity

17.59%
按下载量换算48

windsurf

12.99%
按下载量换算35

trae

9.16%
按下载量换算25

Codex

3.35%
按下载量换算9

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/mikeyobrien/ralph-orchestrator --skill evaluate-presets;npx skills add mikeyobrien/ralph-orchestrator --skill "evaluate-presets" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。

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