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Agent Skill · 研究检索

quality-verify-implementation-complete(质量验证实施完成)

quality-verify-implementation-complete 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-verify-implementation-complete 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;许可证信息还不明确,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 72,323。

总安装 188周安装 8GitHub Stars 1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:quality-verify-implementation-complete(质量验证实施完成)
来源仓库:https://github.com/dawiddutoit/custom-claude
仓库路径:skills/quality-verify-implementation-complete
安装命令:
npx skills add https://github.com/dawiddutoit/custom-claude --skill quality-verify-implementation-complete
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-verify-implementation-complete

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。适用于质量验证实施完成相关的进度跟踪与完整性检查场景,支持基于来源线索的状态确认。

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-verify-implementation-complete 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-verify-implementation-complete

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-veri…;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

quality-verify-implementation-complete 怎么安装?

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

quality-verify-implementation-complete 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-verify-implementation-complete 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/dawiddutoit/custom-claude --skill quality-verify-implementation-complete 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。

quality-verify-implementation-complete 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-verify-implementation-complete 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 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-verify-implementation-complete 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。

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。
  • 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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
quality-verify-implementation-complete

质量验证实施完成 · 本页

1881执行命令未明确有提醒

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

Verify Implementation Complete

Quick Start

Before claiming any implementation is "done", run this verification:

# 1. Check if your new module is imported
grep -r "from.*your_module import\|import.*your_module" src/

# 2. Check if your functions are called
grep -r "your_function_name" src/ --include="*.py" | grep -v test | grep -v "def "

# 3. Run integration verification
./scripts/verify_integration.sh  # If available

# 4. Answer the Four Questions (see below)

If any check fails or you cannot answer all four questions, the implementation is NOT complete.

Table of Contents

  1. When to Use This Skill
  2. What This Skill Does
  3. The CCV Principle
  4. The Four Questions Test
  5. Step-by-Step Verification Process
  6. Verification by Artifact Type
  7. Common Failure Patterns
  8. Supporting Files
  9. Expected Outcomes
  10. Requirements
  11. Red Flags to Avoid

When to Use This Skill

Explicit Triggers

  • "Verify this implementation is complete"
  • "Is this code integrated?"
  • "Check if my code is wired into the system"
  • "Prove this feature runs"
  • "Verify integration for [feature/module]"
  • "Run implementation completeness check"

Implicit Triggers

  • Before marking any task as "done" or "complete"
  • Before moving an ADR from in_progress/ to completed/
  • After creating new Python modules, classes, or functions
  • After implementing LangGraph nodes, CLI commands, or API endpoints
  • Before claiming "feature works" or "implementation successful"
  • When self-reviewing code changes

Debugging Triggers

  • "Why isn't my feature working?" (often it's not integrated)
  • "Tests pass but feature doesn't run" (classic integration gap)
  • "Code exists but nothing happens" (orphaned code)
  • "Feature used to work" (regression - connection removed)

What This Skill Does

This skill enforces the Creation-Connection-Verification (CCV) Principle by:

  1. Verifying Creation - Confirms artifacts exist (files, tests, types)
  2. Verifying Connection - Confirms artifacts are wired into the system
  3. Verifying Execution - Confirms artifacts execute at runtime
  4. Providing Evidence - Generates proof that all three phases are complete

This skill prevents the ADR-013 failure mode where code was created and tested but never integrated into builder.py, causing the feature to never execute despite passing all quality gates.

The CCV Principle

The Formula

COMPLETE = CREATION + CONNECTION + VERIFICATION

The Three Phases

PhaseProvesEvidence Required
CREATIONArtifact existsFile written, tests pass, types check, linting clean
CONNECTIONArtifact is wired inImport statement, registration call, configuration entry
VERIFICATIONArtifact executesLogs showing execution, output observed, state changes confirmed

Missing any phase = Implementation is INCOMPLETE

Why All Three Are Required

  • Creation without Connection → Dead code that never runs
  • Connection without Verification → Unknown if it actually works
  • Creation + Connection without Verification → Might work, might not, no proof

The Four Questions Test

Before claiming "done", you MUST answer ALL FOUR questions:

Question 1: How do I trigger this?

What it means: What user action, CLI command, API call, or system event causes this code to execute?

Examples:

  • uv run temet-run -a talky -p "analyze code"
  • curl -X POST /api/endpoint
  • from myapp.service import MyService; MyService().method()

If you cannot answer: Feature has no entry point → NOT COMPLETE

Question 2: What connects it to the system?

What it means: Where is the import, registration, or wiring that makes this code reachable?

Examples:

  • 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)

If you cannot answer: Code is orphaned → NOT COMPLETE

Question 3: What evidence proves it runs?

What it means: What logs, traces, or observable output demonstrates execution?

Examples:

  • [2025-12-07 10:30:45] INFO architecture_review_triggered
  • ✓ Task completed successfully (in CLI output)
  • HTTP 200 OK (in curl response)

If you cannot answer: No execution proof → NOT COMPLETE

Question 4: What shows it works correctly?

What it means: What output, state change, or behavior proves correct function?

Examples:

  • state.architecture_review = ArchitectureReviewResult(status=APPROVED)
  • Database row inserted with expected values
  • File created with correct contents

If you cannot answer: No outcome proof → NOT COMPLETE

Step-by-Step Verification Process

Phase 1: Verify Creation (Artifacts Exist)

Run standard quality gates:

# Type checking
uv run mypy src/

# Linting
uv run ruff check .

# Unit tests
uv run pytest tests/unit/ --no-cov -v

Checklist:

  • Files exist with complete implementations (no stubs/TODOs)
  • Unit tests pass (isolated behavior verified)
  • Type checking passes (no type errors)
  • Linting passes (code style clean)

If any fail: Fix before proceeding to Phase 2

Phase 2: Verify Connection (Wiring Exists)

2.1 Check Import Statements

For a new module your_module.py:

# Find all imports of your module
grep -r "from.*your_module import\|import.*your_module" src/ --include="*.py" | grep -v test

Expected: At least one import in production code (not just tests)

If empty: Module is NOT imported → NOT CONNECTED

2.2 Check Call-Sites

For a new function your_function:

# Find all call-sites
grep -r "your_function" src/ --include="*.py" | grep -v test | grep -v "def your_function"

Expected: At least one call-site in production code

If empty: Function is never called → NOT CONNECTED

2.3 Check Registration (if applicable)

For components that require registration:

# LangGraph nodes
grep -E "add_node.*your_node|from.*your_module" src/coordination/graph/builder.py

# CLI commands
grep -r "add_command\|@.*\.command\|@click\.command" src/ | grep your_command

# DI container
grep -r "container\.register\|container\.provide" src/ | grep YourService

# API routes
grep -r "router\.add\|@.*\.route\|@app\." src/ | grep your_endpoint

Expected: Registration code exists

If empty: Component is NOT registered → NOT CONNECTED

Checklist:

  • Module imported in at least one production file
  • Functions/classes called in at least one production path
  • (If applicable) Component registered in system
  • Can trace path from entry point to your code

If any fail: Wire the code before proceeding to Phase 3

Phase 3: Verify Execution (Runtime Proof)

3.1 Trigger the Feature

Execute the actual entry point:

# Example: CLI command
uv run temet-run -a talky -p "test command"

# Example: API endpoint
curl -X POST http://localhost:8000/api/endpoint -d '{"test": "data"}'

# Example: Direct invocation
uv run python -c "from myapp.service import MyService; MyService().method()"

Observe: Command completes without errors

3.2 Capture Execution Evidence

Look for evidence that YOUR code executed:

# Check logs for your module
grep "your_module\|your_function" logs/daemon.log

# Check structured logs
grep "your_module" logs/*.jsonl | jq .

# Check database for expected changes
sqlite3 app.db "SELECT * FROM your_table WHERE created_at > datetime('now', '-1 minute')"

Expected: Evidence that your code path was executed

If no evidence: Code path was not reached → NOT VERIFIED

3.3 Confirm Correct Behavior

Verify the outcome matches expectations:

# Check state changes
# Example: LangGraph state field populated
assert result.your_field is not None

# Example: File created
ls -la expected_output_file.txt

# Example: Database row inserted
# (query from 3.2)

Expected: Behavior matches specification

If incorrect: Implementation has bugs → NOT VERIFIED

Checklist:

  • Feature can be triggered via documented entry point
  • Logs/traces show code execution
  • Expected outcome observed (state change, output, etc.)
  • Can answer all Four Questions

If any fail: Debug and fix before claiming "done"

Verification by Artifact Type

LangGraph Nodes

Creation:

  • Node file exists (e.g., architecture_nodes.py)
  • Factory function implemented (e.g., create_architecture_review_node())
  • Routing function implemented (e.g., should_review_architecture())
  • Unit tests pass

Connection:

# Verify import
grep "architecture_nodes" src/temet_run/coordination/graph/builder.py

# Expected output:
# from .architecture_nodes import create_architecture_review_node, should_review_architecture
  • Imported in builder.py
  • Node added to graph: graph.add_node("your_node", node)
  • Edges wired: graph.add_conditional_edges(...) or graph.add_edge(...)

Verification:

# Add to test_graph_completeness.py
EXPECTED_NODES = [
    "your_node_name",  # Add this
    # ... other nodes
]
  • Node in EXPECTED_NODES list
  • Integration test passes
  • Coordinator execution shows node in logs
  • State field populated after execution

CLI Commands

Creation:

  • Command function exists
  • Decorated with @click.command() or @app.command()
  • Tests pass

Connection:

# Verify registration
grep -r "add_command\|@.*\.command" src/your_app/cli/ | grep your_command_name
  • Command registered in CLI group
  • Shows in --help output

Verification:

# Test command
uv run your-app your-command --help
uv run your-app your-command [args]
  • Command appears in --help
  • Command executes without errors
  • Expected output observed

Service Classes (with DI)

Creation:

  • Service class exists
  • Methods implemented
  • Unit tests pass

Connection:

# Verify DI registration
grep -r "container\.register\|register_singleton\|provide" src/ | grep YourServiceName
  • Service registered in container
  • Dependencies injected correctly

Verification:

# Test injection
uv run python -c "
from your_app.container import container
service = container.resolve('YourService')
service.method()
"
  • Service can be resolved from container
  • Method executes correctly
  • Dependencies are provided

API Endpoints

Creation:

  • Endpoint function exists
  • Route handler implemented
  • Tests pass

Connection:

# Verify route registration
grep -r "router\.add\|@.*\.route\|@app\." src/ | grep your_endpoint
  • Route added to router
  • Endpoint shows in OpenAPI docs (if applicable)

Verification:

# Test endpoint
curl -X POST http://localhost:8000/api/your-endpoint -d '{"test": "data"}'
  • Endpoint responds (not 404)
  • Expected status code (200, 201, etc.)
  • Response body correct

Common Failure Patterns

Pattern 1: Created But Never Imported

Symptom: File exists, tests pass, but nothing uses it

Detection:

grep -r "from.*your_module import" src/ --include="*.py" | grep -v test
# Returns: (empty)

Fix: Add import where the module should be used

Pattern 2: Imported But Never Called

Symptom: Import exists, but functions never invoked

Detection:

grep -r "your_function_name" src/ --include="*.py" | grep -v test | grep -v "def "
# Returns: (empty - only the definition exists)

Fix: Add call-site where the function should be used

Pattern 3: Registered But Wrong Configuration

Symptom: Component registered, but with wrong parameters/order

Detection: Integration tests fail, runtime errors

Fix: Review registration code, compare with working examples

Pattern 4: Unit Tests Pass, Integration Fails

Symptom: Isolated tests work, but feature fails in real system

Detection: Integration tests fail, or manual testing shows errors

Fix: Add integration tests that exercise real dependencies

Pattern 5: Code Reachable But Conditional Never Triggers

Symptom: Code is wired, but conditional logic prevents execution

Detection: Code never appears in logs despite feature being triggered

Fix: Review conditional logic, adjust trigger conditions

Supporting Files

References

  • references/ccv-principle.md - Deep dive into Creation-Connection-Verification
  • references/four-questions-framework.md - Detailed explanation of the Four Questions
  • references/adr-013-case-study.md - Real-world failure that motivated this skill

Examples

  • examples/langgraph-node-verification.md - Complete LangGraph node verification example
  • examples/cli-command-verification.md - CLI command verification walkthrough
  • examples/service-di-verification.md - Service with DI verification example

Scripts

  • scripts/verify_integration.sh - Automated orphan detection script
  • scripts/check_imports.sh - Check if module is imported
  • scripts/check_callsites.sh - Check if functions are called

Templates

  • templates/verification-checklist.md - Copy-paste checklist for feature verification
  • templates/integration-test-template.py - Template for integration tests

Expected Outcomes

Successful Verification

✅ Implementation Verification Complete

Feature: ArchitectureReview Node
Location: src/temet_run/coordination/graph/architecture_nodes.py

Phase 1: CREATION ✅
  - File exists (175 lines)
  - Unit tests pass (24/24)
  - Type checking clean
  - Linting clean

Phase 2: CONNECTION ✅
  - Imported in: builder.py line 12
  - Registered: graph.add_node("architecture_review", node)
  - Edges: Conditional edge from "query_claude"
  - Evidence:
    $ grep "architecture_nodes" src/coordination/graph/builder.py
    from .architecture_nodes import create_architecture_review_node

Phase 3: VERIFICATION ✅
  - Integration test: EXPECTED_NODES includes "architecture_review"
  - Execution proof:
    [2025-12-07 10:30:45] INFO architecture_review_triggered
    [2025-12-07 10:30:46] INFO architecture_review_complete status=approved
  - Outcome proof: state.architecture_review = ArchitectureReviewResult(...)

Four Questions Test ✅
  1. Trigger: uv run temet-run -a talky -p "Write code"
  2. Connection: builder.py line 12 (import), line 146 (add_node)
  3. Execution: Logs above show node executed
  4. Outcome: state.architecture_review contains approval data

✅ IMPLEMENTATION IS COMPLETE

Verification Failure

❌ Implementation Verification Failed

Feature: ArchitectureReview Node
Location: src/temet_run/coordination/graph/architecture_nodes.py

Phase 1: CREATION ✅
  - File exists (175 lines)
  - Unit tests pass (24/24)
  - Type checking clean
  - Linting clean

Phase 2: CONNECTION ❌
  - Imported in: NOT FOUND
  - Evidence:
    $ grep "architecture_nodes" src/coordination/graph/builder.py
    (empty - no imports found)

CRITICAL FAILURE: Module is not imported in builder.py

❌ IMPLEMENTATION IS INCOMPLETE

Required Actions:
1. Add import in builder.py: from .architecture_nodes import create_architecture_review_node
2. Register node: graph.add_node("architecture_review", review_node)
3. Add edges to wire into workflow
4. Re-run verification after fixes

DO NOT claim this feature is "done" until verification passes.

Requirements

Tools Required

  • Read (to examine source files)
  • Grep (to search for imports/calls)
  • Bash (to run verification commands)

Knowledge Required

  • Understanding of the project's architecture
  • Knowledge of where entry points are (main.py, builder.py, etc.)
  • Familiarity with import paths and module structure

Environment Required

  • Working directory is project root
  • Source code in src/ directory
  • Tests in tests/ directory

Red Flags to Avoid

Do Not

  • ❌ Claim "feature complete" without running this verification
  • ❌ Skip Phase 2 (connection) verification
  • ❌ Skip Phase 3 (runtime) verification
  • ❌ Accept "tests pass" as proof of integration
  • ❌ Move ADRs to completed/ without this verification
  • ❌ Self-approve without answering Four Questions
  • ❌ Assume code is integrated just because it compiles
  • ❌ Trust unit tests alone (they can hide orphaned code)

Do

  • ✅ Run all three phases of verification
  • ✅ Paste actual command output as evidence
  • ✅ Answer all Four Questions before claiming "done"
  • ✅ Add integration tests for new components
  • ✅ Update EXPECTED_NODES or equivalent tracking lists
  • ✅ Show execution logs as proof
  • ✅ Verify correct behavior, not just "no errors"
  • ✅ Document evidence in task/ADR completion notes

Notes

  • This skill was created in response to ADR-013 failure (2025-12-07)
  • The failure: 313 lines of code, 46 passing tests, but code never integrated
  • This skill prevents that exact failure mode from recurring
  • Integrate this skill with util-manage-todo (three-phase pattern)
  • Integrate with create-adr-spike (Phase 6: Integration Verification)
  • Integrate with quality-run-quality-gates (add as Gate 6)

Remember: Code is not "done" when it compiles and tests pass. Code is "done" when it executes at runtime and produces correct outcomes.

平台分布

Codex

36.22%
按下载量换算24

Claude

31.25%
按下载量换算21

Cursor

19.02%
按下载量换算13

Gemini CLI

8.06%
按下载量换算5

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

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/dawiddutoit/custom-claude --skill quality-verify-implementation-complete 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。目前只有一个公开来源,建议同时核对源仓库。

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