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Agent Skill · 研究检索

quality-reflective-questions(质量反思性问题)

quality-reflective-questions 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。

quality-reflective-questions 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,权限为需要联网;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 72,863。

总安装 186周安装 8GitHub Stars 1下载 65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:quality-reflective-questions(质量反思性问题)
来源仓库:https://github.com/dawiddutoit/custom-claude
仓库路径:skills/quality-reflective-questions
安装命令:
npx skills add https://github.com/dawiddutoit/custom-claude --skill quality-reflective-questions
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/dawiddutoit/custom-claude --skill quality-reflective-questions

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。

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词或任务场景快速定位候选结果。适用于质量反思性问题相关的思维引导与评估场景,支持基于来源线索的问答生成。

通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 确认具体用法。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操作。quality-reflective-questions 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/quality-reflective-questions

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/dawiddutoit/custom-claude --skill quality-refl…;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

    它需要需要联网,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

quality-reflective-questions 怎么安装?

在终端执行 npx skills add https://github.com/dawiddutoit/custom-claude --skill quality-reflective-que…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

quality-reflective-questions 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。

quality-reflective-questions 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

quality-reflective-questions 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。

quality-reflective-questions 用的人多吗?

公开数据是累计安装 186 次,近一周安装 8 次,下载 65 次,GitHub 1 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。

quality-reflective-questions 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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。

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

  • 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
quality-reflective-questions

质量反思性问题 · 本页

1861需要联网未明确未通过

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

Reflective Questions for Work Completeness

Quick Start

Before marking ANYTHING as "done", ask yourself these questions and provide HONEST answers:

The Four Mandatory Questions

  1. How do I trigger this? (What's the entry point?)
  2. What connects it to the system? (Where's the wiring?)
  3. What evidence proves it runs? (Show me the logs)
  4. What shows it works correctly? (What's the outcome?)

If you cannot answer ALL FOUR with specific, concrete details, the work is NOT complete.

The Honesty Test

Replace vague answers with specific evidence:

❌ Bad (vague): "It's integrated" → ✅ Good (specific): "Imported in builder.py line 45" ❌ Bad (vague): "It works" → ✅ Good (specific): "Logs show execution at 10:30:45" ❌ Bad (vague): "Tests pass" → ✅ Good (specific): "46 unit tests + 2 integration tests pass"

Table of Contents

  1. When to Use This Skill
  2. What This Skill Does
  3. The Four Mandatory Questions (Deep Dive)
  4. Category-Specific Questions
  5. Red Flag Questions
  6. The Honesty Checklist
  7. Common Self-Deception Patterns
  8. Supporting Files
  9. Expected Outcomes
  10. Requirements
  11. Red Flags to Avoid

When to Use This Skill

Explicit Triggers

  • "Challenge my assumptions about completeness"
  • "Ask me reflective questions about my work"
  • "Self-review my implementation"
  • "Is this really done?"
  • "Verify my work is complete"
  • "Question my completion claims"

Implicit Triggers (PROACTIVE)

  • Before marking any task complete (every single time)
  • Before moving ADR from in_progress to completed
  • Before claiming "feature works"
  • Before self-approving work
  • After implementing any feature
  • When about to say "all tests passing ✅"

Debugging Triggers

  • "Why do I feel uncertain about this?"
  • "Something seems incomplete but I can't pinpoint it"
  • "I want to mark this done but have doubts"
  • "Am I missing something?"

What This Skill Does

This skill provides a structured framework of reflective questions that:

  1. Challenges assumptions about what "done" means
  2. Exposes gaps between claimed completion and actual completion
  3. Forces specificity instead of vague assurances
  4. Prevents premature completion by requiring evidence
  5. Catches integration failures before they become incidents

This skill complements quality-verify-implementation-complete by providing the mental framework for self-questioning BEFORE running technical verification.

The Four Mandatory Questions (Deep Dive)

These questions MUST be answered for EVERY piece of work before claiming "done".

Question 1: How do I trigger this?

Purpose: Verify the feature has a reachable entry point

What it really asks:

  • Can a user/system actually invoke this code?
  • Is there a documented way to make this execute?
  • Could someone else trigger this without asking me?

Good Answers (Specific):

  • ✅ "Run: uv run temet-run -a talky -p 'analyze code'"
  • ✅ "Call: curl -X POST /api/endpoint -d '{...}'"
  • ✅ "Import: from myapp import MyService; MyService().method()"
  • ✅ "Event: Coordinator triggers when should_review_architecture() returns True"

Bad Answers (Vague):

  • ❌ "Run the system"
  • ❌ "It's automatic"
  • ❌ "The coordinator calls it"
  • ❌ "When needed"

Follow-up Questions:

  • "Can you show me the EXACT command right now?"
  • "What arguments/parameters are required?"
  • "Under what conditions does this trigger?"
  • "Could you trigger this in the next 30 seconds if asked?"

If you cannot answer specifically: The feature has no entry point → NOT COMPLETE

Question 2: What connects it to the system?

Purpose: Verify the artifact is actually wired into the codebase

What it really asks:

  • Where is the import statement?
  • Where is the registration/initialization?
  • Where is the configuration that enables this?
  • Can you show me the LINE NUMBER where this is connected?

Good Answers (Specific):

  • ✅ "builder.py line 45: from.architecture_nodes import create_review_node"
  • ✅ "main.py line 12: app.add_command(my_command)"
  • ✅ "container.py line 67: container.register(MyService, scope=Scope.SINGLETON)"
  • ✅ "routes.py line 23: router.add_route('/endpoint', handler)"

Bad Answers (Vague):

  • ❌ "It's imported"
  • ❌ "It's in the builder"
  • ❌ "It's registered"
  • ❌ "It's wired up"

Follow-up Questions:

  • "Can you paste the EXACT import line?"
  • "What FILE and LINE NUMBER has the registration?"
  • "Can you show me with grep output?"
  • "Could I find this connection in 60 seconds if I looked?"

If you cannot answer specifically: The artifact is orphaned → NOT COMPLETE

Question 3: What evidence proves it runs?

Purpose: Verify the code actually executes at runtime

What it really asks:

  • Have you ACTUALLY triggered this and observed execution?
  • What logs/traces show this code path was hit?
  • Can you show me timestamped evidence of execution?
  • Did you observe this with your own eyes (or grep)?

Good Answers (Specific):

  • ✅ "Logs: [2025-12-07 10:30:45] INFO architecture_review_triggered agent=talky"
  • ✅ "Output: ✓ Task completed successfully (from CLI run at 10:30)"
  • ✅ "Trace: OpenTelemetry span architecture_review with duration 1.2s"
  • ✅ "Debug: Added print statement, saw output 'Node executed'"

Bad Answers (Vague):

  • ❌ "It should run"
  • ❌ "Tests pass"
  • ❌ "No errors when I ran it"
  • ❌ "The system works"

Follow-up Questions:

  • "Can you paste the ACTUAL log line showing execution?"
  • "What TIMESTAMP did this execute?"
  • "Did you observe this directly or are you assuming?"
  • "Could you trigger this RIGHT NOW and show me the logs?"

If you cannot answer specifically: No execution proof → NOT COMPLETE

Question 4: What shows it works correctly?

Purpose: Verify the code produces the expected outcome

What it really asks:

  • What observable outcome proves correct behavior?
  • What state changed as a result of execution?
  • What output/artifact was created?
  • How do you KNOW it did the right thing?

Good Answers (Specific):

  • ✅ "State: result.architecture_review = ArchitectureReviewResult(status=APPROVED, violations=[])"
  • ✅ "Database: Row inserted with ID 123, verified with query"
  • ✅ "File: Created output.txt with expected contents (see: cat output.txt)"
  • ✅ "API: Returned HTTP 200 with JSON body containing expected fields"

Bad Answers (Vague):

  • ❌ "It works"
  • ❌ "No errors"
  • ❌ "Tests pass"
  • ❌ "Everything looks good"

Follow-up Questions:

  • "Can you show me the EXACT output/state change?"
  • "What VALUE did this produce?"
  • "How do you KNOW this is correct vs just 'no errors'?"
  • "Could you demonstrate correct behavior RIGHT NOW?"

If you cannot answer specifically: No outcome proof → NOT COMPLETE

Category-Specific Questions

Apply the Four Questions framework to specific implementation types. For detailed questions by category, see references/category-specific-questions.md.

Categories covered:

  • Modules/Files: Import verification, call-site validation
  • LangGraph Nodes: Graph registration, edge connectivity
  • CLI Commands: Registration, --help visibility, execution
  • Service Classes (DI): Container registration, injection points
  • API Endpoints: Route registration, response validation

Red Flag Questions

These questions expose common self-deception patterns. If you answer "yes" to any, stop and investigate.

Integration Red Flags

  1. "Did I only test this in isolation?"

- If YES: You might have orphaned code - Action: Add integration test, verify in real system

  1. "Am I assuming something is connected without verifying?"

- If YES: Assumption might be wrong - Action: Grep for imports, verify connection exists

  1. "Did I only run unit tests, not integration tests?"

- If YES: Integration might be broken - Action: Create/run integration tests

  1. "Am I relying on 'should' or 'probably' language?"

- If YES: You're guessing, not verifying - Action: Replace guesses with evidence

  1. "Could this code exist and never execute?"

- If YES: It might be orphaned - Action: Verify call-sites exist in production code

Execution Red Flags

  1. "Have I not actually triggered this feature?"

- If YES: You don't know if it works - Action: Trigger it, observe execution

  1. "Am I claiming it works based on 'no errors' vs positive proof?"

- If YES: Absence of errors ≠ presence of success - Action: Show positive evidence of correct behavior

  1. "Did I forget to check logs after running?"

- If YES: No execution proof - Action: Run again, capture logs

  1. "Am I trusting tests alone without manual verification?"

- If YES: Tests might be mocked/isolated - Action: Manual E2E test, verify in real environment

  1. "Could this feature be wired but the conditional never triggers?"

- If YES: Dead code path - Action: Verify the condition is reachable

Completion Red Flags

  1. "Am I rushing to mark this complete?"

- If YES: Slow down, verify properly - Action: Run through Four Questions again

  1. "Do I have doubts I'm ignoring?"

- If YES: Your instinct is usually right - Action: Investigate the doubt before proceeding

  1. "Would I bet $1000 this works end-to-end?"

- If NO: You're not confident - Action: Find out why, verify until confident

  1. "Could someone else verify this works without asking me?"

- If NO: Insufficient documentation/evidence - Action: Document entry point, provide evidence

  1. "Am I self-approving without external review?"

- If YES: You might miss blind spots - Action: Request reviewer agent or peer review

The Honesty Checklist

Before marking ANYTHING complete, answer these honestly:

Evidence Requirements

  • I can paste the exact command to trigger this feature (Not "run the system" - the EXACT command with args)
  • I can show the file and line number where this is imported/registered (Not "it's in builder.py" - the EXACT line number)
  • I have actual logs showing this code executed (Not "it should log" - actual timestamped log lines)
  • I can show the specific output/state change this produced (Not "it works" - the EXACT output/data)
  • I triggered this manually and observed it work (Not "tests pass" - I personally ran it)

Integration Requirements

  • This code is imported in at least one production file (grep output shows import, not just tests)
  • This code has call-sites in production paths (grep output shows calls, not just definitions)
  • This code is registered/wired where it needs to be (container, graph, router, CLI - verified)
  • Integration tests verify this component is in the system (Not just unit tests - integration/E2E tests exist)

Outcome Requirements

  • I can demonstrate this works to someone else right now (Could walk someone through triggering and observing)
  • The behavior matches the specification (Not just "no errors" - correct behavior observed)
  • I would bet money this works end-to-end (Confident enough to stake reputation on it)
  • I have answered all Four Questions with specific details (No vague answers, all concrete)

If ANY checkbox is unchecked: NOT COMPLETE

Common Self-Deception Patterns

Be aware of these patterns that lead to premature completion claims. For detailed analysis and fixes, see references/self-deception-patterns.md.

Common Patterns:

  1. "Tests Pass" Syndrome - Unit tests pass but integration untested
  2. "Should Work" Fallacy - Using assumptions instead of evidence
  3. "No Errors" Confusion - Equating silence with correctness
  4. "File Exists" Completion - Code written but not integrated
  5. "Looks Good" Approval - Vague approval without specifics
  6. "I Remember Doing It" - Trusting memory over verification
  7. "Later Will Be Fine" - Deferring critical verification steps

Usage

  1. Before marking work complete, run through the Four Questions
  2. Check the Honesty Checklist - all boxes must be checked
  3. Verify no Red Flags are present
  4. If uncertain, review references/category-specific-questions.md for your implementation type

Supporting Files:

Expected Outcomes

Successful Self-Review

Reflective Questions Self-Review
Feature: ArchitectureReview Node
Date: 2025-12-07

FOUR MANDATORY QUESTIONS:

1. How do I trigger this?
   ✅ SPECIFIC: uv run temet-run -a talky -p "Write a function"
   When should_review_architecture() returns True (when code_changes detected)

2. What connects it to the system?
   ✅ SPECIFIC: builder.py line 12: from .architecture_nodes import create_architecture_review_node
   builder.py line 146: graph.add_node("architecture_review", review_node)
   builder.py line 189: Conditional edge from "query_claude"

3. What evidence proves it runs?
   ✅ SPECIFIC: Logs from execution at 2025-12-07 10:30:45:
   [INFO] architecture_review_triggered agent=talky session=abc123
   [INFO] architecture_review_complete status=approved violations=0

4. What shows it works correctly?
   ✅ SPECIFIC: state.architecture_review = ArchitectureReviewResult(
       status=ReviewStatus.APPROVED,
       violations=[],
       recommendations=["Code follows Clean Architecture"]
   )

HONESTY CHECKLIST:
✅ All evidence specific, not vague
✅ All connections verified with grep
✅ Execution observed directly
✅ Outcome matches specification

SELF-DECEPTION CHECK:
✅ Not relying on "tests pass" alone
✅ Not using "should" or "probably"
✅ Not assuming - all verified
✅ Would bet $1000 this works

DECISION: ✅ WORK IS COMPLETE
Ready to mark as done.

Failed Self-Review (Catches Incompleteness)

Reflective Questions Self-Review
Feature: ArchitectureReview Node
Date: 2025-12-05 (BEFORE FIX)

FOUR MANDATORY QUESTIONS:

1. How do I trigger this?
   ⚠️  VAGUE: "Run the coordinator"
   FOLLOW-UP: What's the EXACT command?
   RE-ANSWER: uv run temet-run -a talky -p "..."
   ⚠️  STILL VAGUE: What prompts trigger the node?

2. What connects it to the system?
   ❌ VAGUE: "It should be in builder.py"
   FOLLOW-UP: Can you show me the line number?
   RE-CHECK: grep "architecture_nodes" builder.py
   RESULT: (empty) ❌
   CRITICAL: MODULE IS NOT IMPORTED

3. What evidence proves it runs?
   ❌ ASSUMPTION: "Tests pass so it should run"
   FOLLOW-UP: Have you actually run it and seen logs?
   RE-ANSWER: "No, just ran unit tests"
   CRITICAL: NO EXECUTION PROOF

4. What shows it works correctly?
   ❌ ASSUMPTION: "Tests verify behavior"
   FOLLOW-UP: What actual output did you observe?
   RE-ANSWER: "Just the test assertions passing"
   CRITICAL: NO RUNTIME OUTCOME PROOF

HONESTY CHECKLIST:
❌ Using vague language ("should", "I think")
❌ No specific line numbers or imports shown
❌ No execution logs captured
❌ Relying on tests, not runtime verification

SELF-DECEPTION CHECK:
❌ Relying on "tests pass" only
❌ Using "should" repeatedly
❌ Assuming instead of verifying
❌ Would NOT bet $1000 (honest answer: no)

DECISION: ❌ WORK IS NOT COMPLETE
Critical issues found:
1. Module not imported in builder.py
2. No runtime execution proof
3. No integration test

DO NOT mark as done. Fix integration first.

Requirements

Tools Required

  • None (this is a mental framework)

Knowledge Required

  • Understanding of what "done" means in your domain
  • Willingness to be honest with yourself
  • Ability to distinguish vague from specific answers

Mindset Required

  • Intellectual honesty - Admit when you don't know
  • Rigor - Don't accept vague answers from yourself
  • Patience - Take time to verify properly
  • Courage - Admit incompleteness vs rushing to "done"

Red Flags to Avoid

Do Not

  • ❌ Accept vague answers from yourself
  • ❌ Use "should", "probably", "I think" language
  • ❌ Rush through questions to mark done faster
  • ❌ Skip questions that feel uncomfortable
  • ❌ Trust memory instead of current verification
  • ❌ Assume connection without grep proof
  • ❌ Claim execution without logs
  • ❌ Rely on unit tests alone for integration work

Do

  • ✅ Answer all Four Questions with specific details
  • ✅ Replace assumptions with evidence
  • ✅ Be honest about gaps and uncertainties
  • ✅ Verify current state, don't trust memory
  • ✅ Show concrete proof (line numbers, logs, output)
  • ✅ Admit incompleteness when found
  • ✅ Fix gaps before marking complete
  • ✅ Use this framework for EVERY completion claim

Notes

  • This skill was created in response to ADR-013 (2025-12-07)
  • The pattern: Self-deception about completeness led to orphaned code
  • This skill provides the mental framework BEFORE technical verification
  • Pair this with quality-verify-implementation-complete for full coverage
  • The Four Questions are the MINIMUM bar, not the complete verification
  • Honesty with yourself is the foundation of quality work

Remember: The person you're most likely to deceive is yourself. These questions force honesty.

平台分布

Codex

37.37%
按下载量换算24

Claude

30.07%
按下载量换算20

Cursor

19.77%
按下载量换算13

Gemini CLI

9.69%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

未通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。目前只有一个公开来源,建议同时核对源仓库。

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