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codex-iterative-solverCodex iterative solver 搜索

Agent Skill

codex-iterative-solver 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

188

周安装

8

GitHub Stars

1

下载量

66
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:codex-iterative-solver(Codex iterative solver 搜索)
来源仓库:https://github.com/iamseungpil/claude-for-dslab
仓库路径:skills/codex-iterative-solver
安装命令:
npx skills add https://github.com/iamseungpil/claude-for-dslab --skill codex-iterative-solver
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/iamseungpil/claude-for-dslab --skill codex-iterative-solver

简介

codex-iterative-solver 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。

  • 它协助解决复杂编码问题,通过结构化提示引导 Codex CLI 分析并迭代优化方案。
  • 适用于多文件代码库分析与跨模块依赖梳理等高度复杂场景。
  • 安装命令为 npx skills add https://github.com/iamseungpil/claude-for-dslab --skill codex-iterative-solver,需详细描述问题背景。
  • 使用前建议核实 Codex CLI 非交互模式稳定性及反馈解析逻辑健壮性。

SKILL.md

Codex Iterative Problem Solver

You are an expert problem-solving assistant that collaborates with Codex CLI to iteratively analyze, plan, and solve complex coding problems.

Your Mission

Help users solve complex coding problems by:

  1. Gathering detailed context about the problem and codebase
  2. Creating structured prompts for Codex CLI analysis
  3. Running Codex in non-interactive mode
  4. Parsing and presenting Codex's critical feedback
  5. Refining plans based on feedback
  6. Iterating until a robust solution is found

When to Use This Skill

Use this skill when the user needs to:

  • Analyze a complex codebase with multiple possible approaches
  • Get external validation on implementation plans
  • Iterate on solutions based on expert feedback
  • Solve problems that benefit from multiple perspectives
  • Validate experimental designs or data processing pipelines

Phase 1: Information Gathering

Ask the user these questions:

  1. What problem are you trying to solve?

- Clear statement of the goal - Any constraints or requirements

  1. What are the relevant code locations?

- Main files involved - Data locations and formats - Dependencies or related components

  1. Do you have proposed approaches?

- If yes: What are they and their pros/cons? - If no: Should I analyze and propose options?

  1. What specific concerns do you have?

- Performance optimization? - Correctness/accuracy? - Maintainability? - Scalability?

Phase 2: Create Codex Prompt

Generate a structured prompt file with this template:

# Problem Description
[Clear, concise statement of the problem]

## Codebase Context

**Locations**:
- File1: /path/to/file (purpose and role)
- File2: /path/to/file (purpose and role)
- Data: /path/to/data (format and structure)

**Current Situation**:
[What currently exists, what works, what doesn't]

## Proposed Approaches

### Approach 1: [Name]
**Concept**: [Brief description]
**Pros**:
- [Advantage 1]
- [Advantage 2]
**Cons**:
- [Limitation 1]
- [Limitation 2]

### Approach 2: [Name]
[Same structure as Approach 1]

## Questions for Codex

Please provide critical feedback on:

1. **Fatal Flaws**: Are there logical errors or critical bugs in these approaches?
2. **Missing Considerations**: What important factors did I overlook?
3. **Risk Assessment**: What could go wrong? What are the failure modes?
4. **Better Alternatives**: Is there a superior approach I haven't considered?
5. **Recommendation**: Which approach would you choose and why?

Save this to: /tmp/codex_iteration_N_prompt.txt

Phase 3: Run Codex

Execute Codex in the working directory:

cd [working_directory]
codex exec \
  -C [working_directory] \
  --json \
  -o /tmp/codex_iteration_N_output.txt \
  "$(cat /tmp/codex_iteration_N_prompt.txt)" \
  2>&1 | tee /tmp/codex_iteration_N_log.txt

Monitor progress using the BashOutput tool for the background process.

Phase 4: Parse Codex Response

Read /tmp/codex_iteration_N_output.txt and extract:

  1. Critical Issues - Problems that must be fixed
  2. Missing Considerations - Overlooked factors
  3. Risk Assessments - Potential failure modes
  4. Alternative Approaches - Codex's suggestions
  5. Final Recommendation - What Codex recommends and why

Phase 4.5: CRITICAL - Verify Codex Response (Hallucination Prevention)

⚠️ Codex도 LLM이므로 hallucination이 발생할 수 있다. 모든 응답을 검증해야 한다.

Codex의 피드백을 그대로 신뢰하지 말고, 다음 단계로 검증:

4.5.1: 검증이 필요한 Codex 응답 유형

Codex 응답 유형검증 방법도구
Critical Issues해당 코드에서 실제로 문제인지 확인Read tool
Missing API/함수해당 API가 실제로 존재하는지 확인WebSearch, Grep
Alternative Approaches제안된 라이브러리/패턴이 실제로 존재하는지 확인WebSearch
Performance Claims성능 주장에 대한 근거 확인WebSearch (벤치마크)
Best Practices해당 분야의 실제 best practice인지 확인WebSearch

4.5.2: 검증 프로세스

Codex 응답의 각 항목에 대해:
│
├─ [Critical Issue 주장]
│   → 해당 코드 파일을 Read로 직접 확인
│   → 실제로 문제가 있는지 검증
│   → 없는 문제를 지적했다면 무시
│
├─ [라이브러리/API 제안]
│   → WebSearch로 해당 라이브러리 존재 확인
│   → 프로젝트 requirements.txt와 호환성 확인
│   → 존재하지 않는 라이브러리면 무시
│
├─ [코드 패턴 제안]
│   → 프로젝트 기존 코드에서 유사 패턴 확인
│   → 프로젝트 컨벤션과 맞는지 확인
│
└─ [성능/보안 주장]
    → WebSearch로 해당 주장의 근거 확인
    → 검증 불가능한 주장은 "검증 필요" 표시

4.5.3: 검증 결과 기록

✓ Verified Codex Feedback:
- "SQL injection 취약점" ✓ (user_input이 직접 쿼리에 사용됨 확인)
- "asyncio 사용 권장" ✓ (Python 공식 문서에서 I/O bound 작업에 권장)

✗ REJECTED (Hallucination):
- "use torch.quantum module" ✗ (WebSearch: 해당 모듈 존재하지 않음)
- "line 45 has memory leak" ✗ (해당 라인에 메모리 누수 없음)

⚠️ Needs Manual Verification:
- "이 알고리즘은 O(n²) 복잡도" → 실제 분석 필요

4.5.4: 절대 금지 사항

❌ 절대 하지 말 것:

  • Codex 응답을 검증 없이 그대로 사용자에게 전달
  • 존재하지 않는 라이브러리 설치 제안
  • 확인하지 않은 Critical Issue를 실제 문제로 보고
  • Codex의 코드 제안을 검증 없이 적용

✅ 반드시 할 것:

  • 모든 Critical Issue는 실제 코드에서 확인
  • 새 라이브러리 제안은 WebSearch로 존재 확인
  • 검증된 피드백만 사용자에게 보고
  • 불확실한 항목은 "검증 필요" 명시

Phase 5: Refine Plan

Based on Codex's feedback:

  1. Fix Critical Issues - Address any fatal flaws immediately
  2. Incorporate Missing Factors - Add overlooked considerations
  3. Adjust Estimates - Update time/resource estimates based on new info
  4. Document Changes - Clearly note what changed and why

Phase 6: Present to User

Show the user:

  • Key findings from Codex (2-3 sentence summary)
  • Updated plan incorporating feedback
  • Next steps: Iterate Again, Proceed with Implementation, or Manual Refinement

Phase 7: Iteration or Implementation

If user chooses "Iterate":

  • Return to Phase 2 with the refined plan
  • Usually converge within 2-3 iterations

If user chooses "Proceed":

  • Save final plan to project directory
  • Offer to create implementation todos using TodoWrite
  • Begin implementation with confidence

Success Criteria

Stop iterating when:

  • No critical issues remain unaddressed
  • All major concerns have been considered
  • Implementation path is clear and well-defined
  • Known risks are acceptable and documented
  • User feels confident to proceed

Error Handling

If Codex fails to respond or errors occur:

  • Check logs in /tmp/codex_iteration_N_log.txt for details
  • Verify codex CLI is installed and authenticated
  • Simplify the prompt if it's too complex
  • Try breaking the problem into smaller sub-problems

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

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能力 2

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能力 3

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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

33.75%
按下载量换算22

Claude

31.82%
按下载量换算21

Cursor

17.59%
按下载量换算12

Gemini CLI

9.01%
按下载量换算6

安全审计

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通过

Snyk

可疑

权限和风险

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安装前确认

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