quality-capture-baseline

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Capture Quality Baseline

捕获质量基线

Purpose - Baseline establishment for regression detection
Quick Start - When to invoke and basic usage
Instructions - Complete implementation guide
- Step 1: Prepare Context - Feature name, git commit, timestamp
- Step 2: Run Quality Checks - Primary and fallback methods
- Step 3: Parse Metrics - Extract tests, coverage, type errors, linting, dead code
- Step 4: Create Memory Entity - Store baseline in memory system
- Step 5: Return Baseline Reference - Success output format
Agent Integration - @planner, @implementer, @statuser workflows
Edge Cases - Quality check failures, no git, missing scripts, memory unavailable
Anti-Patterns - What to avoid and what to do instead
Integration with Other Skills - run-quality-gates, validate-refactor-adr, manage-refactor-markers, manage-todo
Examples - Comprehensive scenarios reference
Reference - Parsing, lifecycle, quality gates documentation
Success Criteria - Validation checklist
Troubleshooting - Common problems and solutions

目的 - 建立基线以进行回归检测
快速开始 - 触发时机和基本用法
操作指南 - 完整实现指南
- 步骤1：准备上下文 - 功能名称、Git提交记录、时间戳
- 步骤2：运行质量检查 - 主要方法和备选方法
- 步骤3：解析指标 - 提取测试、覆盖率、类型错误、代码检查、死代码数据
- 步骤4：创建内存实体 - 在内存系统中存储基线
- 步骤5：返回基线引用 - 成功输出格式
Agent集成 - @planner、@implementer、@statuser工作流
边缘情况 - 质量检查失败、无Git仓库、脚本缺失、内存服务不可用
反模式 - 应避免的做法及替代方案
与其他技能集成 - run-quality-gates、validate-refactor-adr、manage-refactor-markers、manage-todo
示例 - 综合场景参考
参考资料 - 解析、生命周期、质量门文档
成功标准 - 验证清单
故障排除 - 常见问题及解决方案

Purpose

目的

Establish a quality metrics baseline at the start of a feature or refactor so that subsequent quality gate runs can detect regressions. This skill is a critical component of the project's regression detection strategy.

在功能开发或重构开始时建立质量指标基线，以便后续运行质量门时能够检测回归。该技能是项目回归检测策略的关键组成部分。

When to Use

使用时机

Use this skill when:

Starting any new feature (before writing code)
Beginning refactor work (to document pre-state)
After major changes (dependency upgrades, architecture shifts)
When @planner creates a new todo.md
When @implementer starts task 0 of a feature
User asks to "capture baseline" or "establish baseline metrics"
Before major architectural changes to track impact

在以下场景使用该技能：

启动任何新功能（编写代码前）
开始重构工作（记录初始状态）
重大变更后（依赖升级、架构调整）
@planner创建新的todo.md时
@implementer启动功能的任务0时
用户要求“capture baseline”或“establish baseline metrics”时
重大架构变更前（跟踪影响）

Quick Start

快速开始

When to invoke this skill:

✅ At the START of any new feature (before writing code)
✅ Before beginning refactor work (to document pre-state)
✅ After major changes (dependency upgrades, architecture shifts)
❌ NOT after you've already started coding (too late)

Basic usage:

1. Run quality checks: ./scripts/check_all.sh
2. Parse metrics (tests, coverage, type errors, linting, dead code)
3. Create memory entity: baseline_{feature}_{date}
4. Return baseline reference for future comparison

触发该技能的时机：

✅ 任何新功能开发启动前（编写代码前）
✅ 重构工作开始前（记录初始状态）
✅ 重大变更后（依赖升级、架构调整）
❌ 不要在已经开始编码后使用（为时已晚）

基本用法：

1. 运行质量检查：./scripts/check_all.sh
2. 解析指标（测试、覆盖率、类型错误、代码检查、死代码）
3. 创建内存实体：baseline_{feature}_{date}
4. 返回基线引用，用于后续对比

Instructions

操作指南

Step 1: Prepare Context

步骤1：准备上下文

Before running quality checks, gather:

Feature name or refactor identifier (e.g., "auth", "service-result-migration")
Current git commit hash:
```
git rev-parse HEAD
```
Current timestamp:
```
date +"%Y-%m-%d %H:%M:%S"
```

Example:

bash

FEATURE="user_profile"
GIT_COMMIT=$(git rev-parse HEAD)
TIMESTAMP=$(date +"%Y-%m-%d %H:%M:%S")
DATE=$(date +"%Y-%m-%d")

运行质量检查前，收集以下信息：

功能名称或重构标识（例如："auth"、"service-result-migration"）
当前Git提交哈希：
```
git rev-parse HEAD
```
当前时间戳：
```
date +"%Y-%m-%d %H:%M:%S"
```

示例：

bash

FEATURE="user_profile"
GIT_COMMIT=$(git rev-parse HEAD)
TIMESTAMP=$(date +"%Y-%m-%d %H:%M:%S")
DATE=$(date +"%Y-%m-%d")

Step 2: Run Quality Checks

步骤2：运行质量检查

Primary method (recommended):

bash

./scripts/check_all.sh

Fallback (if check_all.sh fails):

bash

undefined

主要方法（推荐）：

bash

./scripts/check_all.sh

备选方案（如果check_all.sh执行失败）：

bash

undefined

Run individual checks

运行单独的检查

uv run pytest tests/ -v --cov=src --cov-report=term-missing uv run pyright src/ uv run ruff check src/ uv run vulture src/


**Capture output:**
- Store stdout and stderr
- Measure execution time
- Note any failures (document them, don't skip)

uv run pytest tests/ -v --cov=src --cov-report=term-missing uv run pyright src/ uv run ruff check src/ uv run vulture src/


**捕获输出：**
- 存储标准输出和标准错误
- 测量执行时间
- 记录任何失败情况（文档化，不要跳过）

Step 3: Parse Metrics

步骤3：解析指标

Use the parsing patterns from

references/parsing.md

. Quick reference:

Tests:

bash

undefined

使用

references/parsing.md

中的解析规则。快速参考：

测试：

bash

undefined

Pattern: "X passed" or "X failed" or "X skipped"

规则："X passed" 或 "X failed" 或 "X skipped"

PASSED=$(grep -oE "[0-9]+ passed" output.txt | grep -oE "[0-9]+") FAILED=$(grep -oE "[0-9]+ failed" output.txt | grep -oE "[0-9]+") SKIPPED=$(grep -oE "[0-9]+ skipped" output.txt | grep -oE "[0-9]+")


**Coverage:**
```bash

PASSED=$(grep -oE "[0-9]+ passed" output.txt | grep -oE "[0-9]+") FAILED=$(grep -oE "[0-9]+ failed" output.txt | grep -oE "[0-9]+") SKIPPED=$(grep -oE "[0-9]+ skipped" output.txt | grep -oE "[0-9]+")


**覆盖率：**
```bash

Pattern: "TOTAL ... X%"

规则："TOTAL ... X%"

COVERAGE=$(grep "TOTAL" output.txt | grep -oE "[0-9]+%" | tail -1)


**Type Errors:**
```bash

COVERAGE=$(grep "TOTAL" output.txt | grep -oE "[0-9]+%" | tail -1)


**类型错误：**
```bash

Pattern: "X errors, Y warnings" or "0 errors"

规则："X errors, Y warnings" 或 "0 errors"

TYPE_ERRORS=$(grep -oE "[0-9]+ error" output.txt | grep -oE "[0-9]+" | head -1)


**Linting:**
```bash

TYPE_ERRORS=$(grep -oE "[0-9]+ error" output.txt | grep -oE "[0-9]+" | head -1)


**代码检查：**
```bash

Pattern: "Found X errors"

规则："Found X errors"

LINTING_ERRORS=$(grep -oE "Found [0-9]+ error" output.txt | grep -oE "[0-9]+")


**Dead Code:**
```bash

LINTING_ERRORS=$(grep -oE "Found [0-9]+ error" output.txt | grep -oE "[0-9]+")


**死代码：**
```bash

Pattern: Count vulture output lines or "X% unused"

规则：统计vulture输出行数或 "X% unused"

DEAD_CODE=$(vulture src/ 2>&1 | grep -v "^$" | wc -l | xargs)


**Execution Time:**
```bash

DEAD_CODE=$(vulture src/ 2>&1 | grep -v "^$" | wc -l | xargs)


**执行时间：**
```bash

Measure with time command or calculate from timestamps

使用time命令测量或通过时间戳计算

EXEC_TIME=$(echo "scale=1; $END_TIME - $START_TIME" | bc)

undefined

EXEC_TIME=$(echo "scale=1; $END_TIME - $START_TIME" | bc)

undefined

Step 4: Create Memory Entity

步骤4：创建内存实体

Entity structure:

yaml

name: quality-capture-baseline
type: quality_baseline
observations:
  - "Tests: {passed} passed, {failed} failed, {skipped} skipped"
  - "Coverage: {coverage}%"
  - "Type errors: {type_errors}"
  - "Linting errors: {linting_errors}"
  - "Dead code: {dead_code} items"
  - "Execution time: {exec_time}s"
  - "Git commit: {git_hash}"
  - "Timestamp: {timestamp}"
  - "Feature: {feature_name}"

Example:

python

mcp__memory__create_entities({
    "entities": [{
        "name": f"baseline_{feature}_{date}",
        "type": "quality_baseline",
        "observations": [
            f"Tests: {passed} passed, {failed} failed, {skipped} skipped",
            f"Coverage: {coverage}%",
            f"Type errors: {type_errors}",
            f"Linting errors: {linting_errors}",
            f"Dead code: {dead_code} items",
            f"Execution time: {exec_time}s",
            f"Git commit: {git_commit}",
            f"Timestamp: {timestamp}",
            f"Feature: {feature}"
        ]
    }]
})

If pre-existing issues exist: Add additional observation:

"Pre-existing issues: 3 type errors in legacy code (documented, will not be fixed)"

实体结构：

yaml

name: quality-capture-baseline
type: quality_baseline
observations:
  - "Tests: {passed} passed, {failed} failed, {skipped} skipped"
  - "Coverage: {coverage}%"
  - "Type errors: {type_errors}"
  - "Linting errors: {linting_errors}"
  - "Dead code: {dead_code} items"
  - "Execution time: {exec_time}s"
  - "Git commit: {git_hash}"
  - "Timestamp: {timestamp}"
  - "Feature: {feature_name}"

示例：

python

mcp__memory__create_entities({
    "entities": [{
        "name": f"baseline_{feature}_{date}",
        "type": "quality_baseline",
        "observations": [
            f"Tests: {passed} passed, {failed} failed, {skipped} skipped",
            f"Coverage: {coverage}%",
            f"Type errors: {type_errors}",
            f"Linting errors: {linting_errors}",
            f"Dead code: {dead_code} items",
            f"Execution time: {exec_time}s",
            f"Git commit: {git_commit}",
            f"Timestamp: {timestamp}",
            f"Feature: {feature}"
        ]
    }]
})

如果存在预先存在的问题： 添加额外的观测项：

"Pre-existing issues: 3 type errors in legacy code (documented, will not be fixed)"

Step 5: Return Baseline Reference

步骤5：返回基线引用

Success output format:

✅ Baseline captured: baseline_{feature}_{date}

Metrics:
- Tests: {passed} passed ({coverage}% coverage)
- Type safety: {status} ({type_errors} errors)
- Code quality: {status} ({linting_errors} linting, {dead_code} dead code items)
- Execution: {exec_time}s
- Git: {git_hash}

Use this baseline for regression detection.

With pre-existing issues:

✅ Baseline captured: baseline_{feature}_{date}

Metrics:
- Tests: 152 passed (89% coverage)
- Type safety: 3 errors (pre-existing, documented)
- Code quality: Clean
- Git: abc123f

⚠️  Note: 3 pre-existing type errors documented, will not cause regression failures.

Use this baseline for regression detection.

成功输出格式：

✅ Baseline captured: baseline_{feature}_{date}

Metrics:
- Tests: {passed} passed ({coverage}% coverage)
- Type safety: {status} ({type_errors} errors)
- Code quality: {status} ({linting_errors} linting, {dead_code} dead code items)
- Execution: {exec_time}s
- Git: {git_hash}

Use this baseline for regression detection.

存在预先存在的问题时：

✅ Baseline captured: baseline_{feature}_{date}

Metrics:
- Tests: 152 passed (89% coverage)
- Type safety: 3 errors (pre-existing, documented)
- Code quality: Clean
- Git: abc123f

⚠️  Note: 3 pre-existing type errors documented, will not cause regression failures.

Use this baseline for regression detection.

Agent Integration

Agent集成

@planner Usage

@planner 使用方法

When: At plan creation, BEFORE creating todo.md

Workflow:

Receive feature request from user
Analyze requirements
→ Invoke capture-quality-baseline
Receive baseline reference
Create todo.md with baseline in header
Create memory entities linking to baseline

Example:

markdown

undefined

时机： 计划创建时，在创建todo.md之前

工作流：

接收用户的功能请求
分析需求
→ 调用capture-quality-baseline
接收基线引用
在todo.md的头部添加基线信息后创建文件
创建关联到基线的内存实体

示例：

markdown

undefined

Todo: User Profile Feature

Baseline: baseline_user_profile_2025-10-16 Created: 2025-10-16

Tasks

Task 1 (references baseline for quality gates) ...

undefined

Task 1 (references baseline for quality gates) ...

undefined

@implementer Usage

@implementer 使用方法

When: At feature start (task 0) or before refactor work

Workflow:

Validate ADR (if refactor) using validate-refactor-adr
→ Invoke capture-quality-baseline
Receive baseline reference
Reference baseline in quality gate runs
Compare final metrics against baseline

Example:

User: "Start implementing authentication"

@implementer:
1. Invoke capture-quality-baseline
2. Receives: baseline_auth_2025-10-16
3. Begin task 1 implementation
4. After each task: Run quality gates, compare to baseline
5. Report: "All metrics maintained or improved from baseline"

时机： 功能启动时（任务0）或重构工作前

工作流：

使用validate-refactor-adr验证ADR（如果是重构）
→ 调用capture-quality-baseline
接收基线引用
在质量门运行时引用基线
将最终指标与基线对比

示例：

User: "Start implementing authentication"

@implementer:
1. 调用capture-quality-baseline
2\. 接收：baseline_auth_2025-10-16
3. 开始任务1的实现
4. 每个任务完成后：运行质量门，与基线对比
5. 报告："所有指标保持或优于基线"

@statuser Usage

@statuser 使用方法

When: User requests baseline recapture or progress check

Workflow:

Receive progress check request
If major changes detected: → Invoke capture-quality-baseline
Compare new baseline to original
Report delta and recommend re-baseline if appropriate

Example:

User: "Check progress after dependency upgrade"

@statuser:
1. Detects major change (dependency upgrade)
2. Invokes capture-quality-baseline
3. Receives: baseline_post_upgrade_2025-10-16
4. Compares to original baseline
5. Reports: "2 tests removed (deprecated APIs), coverage maintained, type errors resolved"

时机： 用户要求重新捕获基线或检查进度时

工作流：

接收进度检查请求
如果检测到重大变更：→ 调用capture-quality-baseline
将新基线与原始基线对比
报告差异并建议是否需要重新建立基线

示例：

User: "Check progress after dependency upgrade"

@statuser:
1. 检测到重大变更（依赖升级）
2. 调用capture-quality-baseline
3. 接收：baseline_post_upgrade_2025-10-16
4. 与原始基线对比
5. 报告："移除2个测试（废弃API），覆盖率保持，类型错误已解决"

Edge Cases

边缘情况

Quality Checks Fail

质量检查失败

Scenario: Some checks fail during baseline capture

Handling:

Still capture the baseline (document failures)
Add observation: "Baseline captured WITH failures"
Flag in output: "⚠️ Baseline has failures - must fix before feature work"
Return baseline reference (still usable for comparison)

Example:

✅ Baseline captured: baseline_auth_2025-10-16

⚠️  WARNING: Baseline has failures

Metrics:
- Tests: 140 passed, 5 FAILED, 2 skipped
- Type safety: 2 errors
- Code quality: 3 linting errors

🛑 FIX these failures before starting feature work.

场景： 基线捕获过程中部分检查失败

处理方式：

仍然捕获基线（记录失败情况）
添加观测项："Baseline captured WITH failures"
在输出中标记："⚠️ Baseline has failures - must fix before feature work"
返回基线引用（仍可用于对比）

示例：

✅ Baseline captured: baseline_auth_2025-10-16

⚠️  WARNING: Baseline has failures

Metrics:
- Tests: 140 passed, 5 FAILED, 2 skipped
- Type safety: 2 errors
- Code quality: 3 linting errors

🛑 FIX these failures before starting feature work.

No Git Repository

无Git仓库

Scenario: Not in a git repository (or git not available)

Handling:

Skip git commit capture
Continue with other metrics
Add observation: "No git commit (not in repository)"
Note in output: "Git: Not available"

场景： 不在Git仓库中（或Git不可用）

处理方式：

跳过Git提交记录捕获
继续收集其他指标
添加观测项："No git commit (not in repository)"
在输出中注明："Git: Not available"

Script Not Found

脚本未找到

Scenario:

./scripts/check_all.sh

doesn't exist

Handling:

Try individual checks (pytest, pyright, ruff, vulture)
If any work: Use those results
If none work: Report error and abort

Error message:

❌ Cannot capture baseline: Quality check scripts not found

Tried:
- ./scripts/check_all.sh (not found)
- uv run pytest (not found)
- uv run pyright (not found)

Cannot establish baseline without quality checks.

场景：

./scripts/check_all.sh

不存在

处理方式：

尝试单独运行检查（pytest、pyright、ruff、vulture）
如果有可用的检查：使用其结果
如果全部不可用：报告错误并终止

错误信息：

❌ Cannot capture baseline: Quality check scripts not found

Tried:
- ./scripts/check_all.sh (not found)
- uv run pytest (not found)
- uv run pyright (not found)

Cannot establish baseline without quality checks.

Memory Service Unavailable

内存服务不可用

Scenario: Cannot create memory entity (MCP server down)

Handling:

Store baseline in local file:
```
.quality-baseline-{feature}-{date}.json
```
Return file path as reference
Note in output: "Baseline stored locally (memory unavailable)"

Fallback file format:

json

{
  "baseline_name": "baseline_auth_2025-10-16",
  "metrics": {
    "tests": {"passed": 145, "failed": 0, "skipped": 2},
    "coverage": 87,
    "type_errors": 0,
    "linting_errors": 0,
    "dead_code": 3,
    "execution_time": 8.3,
    "git_commit": "abc123f",
    "timestamp": "2025-10-16 10:30:00"
  }
}

场景： 无法创建内存实体（MCP服务器宕机）

处理方式：

将基线存储在本地文件：
```
.quality-baseline-{feature}-{date}.json
```
返回文件路径作为引用
在输出中注明："Baseline stored locally (memory unavailable)"

备选文件格式：

json

{
 "baseline_name": "baseline_auth_2025-10-16",
 "metrics": {
 "tests": {"passed": 145, "failed": 0, "skipped": 2},
 "coverage": 87,
 "type_errors": 0,
 "linting_errors": 0,
 "dead_code": 3,
 "execution_time": 8.3,
 "git_commit": "abc123f",
 "timestamp": "2025-10-16 10:30:00"
 }
}

Anti-Patterns

反模式

❌ DON'T:

Skip baseline capture (regression detection requires it)
Reuse old baselines for new features (each feature needs its own)
Capture baseline after starting work (too late)
Ignore failing checks in baseline (document them)
Forget to reference baseline in todo.md

✅ DO:

Capture baseline BEFORE any feature work
Create unique baseline per feature
Document pre-existing issues in baseline
Reference baseline in todo.md and memory
Re-baseline after major changes (upgrades, migrations)

❌ 不要：

跳过基线捕获（回归检测需要基线）
为新功能复用旧基线（每个功能需要独立基线）
开始工作后才捕获基线（为时已晚）
忽略基线中的检查失败（需记录）
忘记在todo.md中引用基线

✅ 应该：

在任何功能工作开始前捕获基线
为每个功能创建唯一基线
在基线中记录预先存在的问题
在todo.md和内存中引用基线
重大变更后重新建立基线（升级、迁移）

Integration with Other Skills

与其他技能集成

Skill: run-quality-gates

capture-quality-baseline: Establishes the reference point
run-quality-gates: Compares current metrics against baseline

Skill: validate-refactor-adr

validate-refactor-adr: Validates ADR completeness
capture-quality-baseline: Documents pre-refactor state

Skill: manage-refactor-markers

capture-quality-baseline: Establishes pre-refactor metrics
manage-refactor-markers: Tracks refactor progress

Skill: manage-todo

capture-quality-baseline: Provides baseline reference
manage-todo: Stores baseline name in todo.md header

技能：run-quality-gates

capture-quality-baseline：建立参考基准
run-quality-gates：将当前指标与基线对比

技能：validate-refactor-adr

validate-refactor-adr：验证ADR完整性
capture-quality-baseline：记录重构前的状态

技能：manage-refactor-markers

capture-quality-baseline：建立重构前的指标
manage-refactor-markers：跟踪重构进度

技能：manage-todo

capture-quality-baseline：提供基线引用
manage-todo：在todo.md头部存储基线名称

Examples

示例

See

examples.md

for comprehensive scenarios:

Feature start baseline (clean state)
Refactor start baseline (with pre-existing issues)
Re-baseline after major change
Baseline with failures
Agent auto-invocation patterns
Cross-agent baseline sharing
Baseline comparison workflows
Recovery from failed baseline capture

查看

examples.md

获取综合场景：

功能启动基线（干净状态）
重构启动基线（存在预先问题）
重大变更后重新建立基线
包含失败的基线
Agent自动触发模式
跨Agent基线共享
基线对比工作流
基线捕获失败后的恢复

Reference

参考资料

Metrics parsing: See
```
references/parsing.md
```
for detailed parsing logic
Baseline lifecycle: See
```
references/lifecycle.md
```
for management best practices

Quality gates: See project's

../run-quality-gates/references/shared-quality-gates.md

for gate definitions

指标解析： 查看
```
references/parsing.md
```
获取详细解析逻辑
基线生命周期： 查看
```
references/lifecycle.md
```
获取管理最佳实践

质量门： 查看项目的

../run-quality-gates/references/shared-quality-gates.md

获取质量门定义

Success Criteria

成功标准

Troubleshooting

故障排除

Problem: Metrics parsing fails (empty values)

Solution:

Check quality check output format (may have changed)
Review parsing patterns in
```
references/parsing.md
```
Update regex patterns if needed
Fallback: Manually parse critical metrics

Problem: Baseline creation succeeds but can't be found later

Solution:

Verify memory entity name format:
```
baseline_{feature}_{date}
```

Search memory:

mcp__memory__search_memories(query="baseline")

Check fallback file:
```
.quality-baseline-*.json
```

Problem: Quality checks take too long (> 30s)

Solution:

Check if Neo4j is running (connection timeout)
Skip slow tests:
```
pytest -m "not slow"
```
Run checks in parallel (check_all.sh does this)
Report timing issue to user

Last Updated: 2025-10-16 Priority: High (critical for regression detection) Dependencies: Quality gate scripts, memory MCP server

问题： 指标解析失败（值为空）

解决方案：

检查质量检查输出格式（可能已变更）
查看
```
references/parsing.md
```
中的解析规则
如有需要更新正则表达式
备选方案：手动解析关键指标

问题： 基线创建成功但后续无法找到

解决方案：

验证内存实体名称格式：
```
baseline_{feature}_{date}
```

搜索内存：

mcp__memory__search_memories(query="baseline")

检查备选文件：
```
.quality-baseline-*.json
```

问题： 质量检查耗时过长（>30秒）

解决方案：

检查Neo4j是否在运行（连接超时）
跳过慢测试：
```
pytest -m "not slow"
```
并行运行检查（check_all.sh已支持）
向用户报告耗时问题

最后更新： 2025-10-16 优先级： 高（回归检测的关键） 依赖： 质量门脚本、内存MCP服务器",

quality-capture-baseline

Original

Translation

Capture Quality Baseline

捕获质量基线

Table of Contents

目录

Core Sections

核心章节

Purpose

目的

When to Use

使用时机

Quick Start

快速开始

Instructions

操作指南

Step 1: Prepare Context

步骤1：准备上下文

Step 2: Run Quality Checks

步骤2：运行质量检查

Run individual checks

运行单独的检查

Step 3: Parse Metrics

步骤3：解析指标

Pattern: "X passed" or "X failed" or "X skipped"

规则："X passed" 或 "X failed" 或 "X skipped"

Pattern: "TOTAL ... X%"

规则："TOTAL ... X%"

Pattern: "X errors, Y warnings" or "0 errors"

规则："X errors, Y warnings" 或 "0 errors"

Pattern: "Found X errors"

规则："Found X errors"

Pattern: Count vulture output lines or "X% unused"

规则：统计vulture输出行数或 "X% unused"

Measure with time command or calculate from timestamps

使用time命令测量或通过时间戳计算

Step 4: Create Memory Entity

步骤4：创建内存实体

Step 5: Return Baseline Reference

步骤5：返回基线引用

Agent Integration

Agent集成

@planner Usage

@planner 使用方法

Todo: User Profile Feature

Todo: User Profile Feature

Tasks

Tasks

@implementer Usage

@implementer 使用方法

@statuser Usage

@statuser 使用方法

Edge Cases

边缘情况

Quality Checks Fail

质量检查失败

No Git Repository

无Git仓库

Script Not Found

脚本未找到

Memory Service Unavailable

内存服务不可用

Anti-Patterns

反模式

Integration with Other Skills

与其他技能集成

Examples

示例

Reference

参考资料

Success Criteria

成功标准

Troubleshooting

故障排除