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technical-report

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Chinese

Technical Report

技术报告

Purpose: Document technical work in detail — experiments, systems, code, and internal research.
When to use:
  • Writing experiment reports (detailed results)
  • Documenting system architecture
  • Writing code documentation
  • Creating internal research memos
  • Preparing supplementary materials
Links to:
  • experiment-tracking
    — experiment data
  • research-design
    — methodology
  • thesis-writing
    — thesis integration
  • paper-writing
    — paper format
  • docker-containerization
    — deployment docs
  • kubernetes-orchestration
    — infrastructure docs
用途： 详细记录技术工作——包括实验、系统、代码和内部研究。
适用场景：
  • 撰写实验报告（包含详细结果）
  • 记录系统架构
  • 编写代码文档
  • 创建内部研究备忘录
  • 准备补充材料
关联内容：
  • experiment-tracking
    — 实验数据
  • research-design
    — 方法论
  • thesis-writing
    — 论文整合
  • paper-writing
    — 论文格式
  • docker-containerization
    — 部署文档
  • kubernetes-orchestration
    — 基础设施文档

Report Types

报告类型

1. Experiment Report

1. 实验报告

markdown
undefined
markdown
undefined

Experiment Report: [Experiment Name]

Experiment Report: [Experiment Name]

Date: [YYYY-MM-DD] Author: [Name] Version: [v1.0]
Date: [YYYY-MM-DD] Author: [Name] Version: [v1.0]

1. Objective

1. Objective

[What question does this experiment answer?]
[What question does this experiment answer?]

2. Setup

2. Setup

2.1 Hardware

2.1 Hardware

  • GPU: [type] × [count]
  • RAM: [size]
  • Storage: [type/size]
  • GPU: [type] × [count]
  • RAM: [size]
  • Storage: [type/size]

2.2 Software

2.2 Software

  • Python: [version]
  • PyTorch: [version]
  • CUDA: [version]
  • Python: [version]
  • PyTorch: [version]
  • CUDA: [version]

2.3 Dataset

2.3 Dataset

PropertyValue
Name[dataset]
Size[N samples]
Train/Val/Test[split ratios]
Preprocessing[steps]
PropertyValue
Name[dataset]
Size[N samples]
Train/Val/Test[split ratios]
Preprocessing[steps]

2.4 Hyperparameters

2.4 Hyperparameters

ParameterSearch RangeFinal ValueSelection
learning_rate[1e-4, 1e-2]1e-3grid search
batch_size[16, 32, 64]64validation loss
ParameterSearch RangeFinal ValueSelection
learning_rate[1e-4, 1e-2]1e-3grid search
batch_size[16, 32, 64]64validation loss

3. Method

3. Method

[Brief description of what was tested]
[Brief description of what was tested]

4. Results

4. Results

4.1 Main Results

4.1 Main Results

MethodAccuracyF1Latency
Baseline85.2±0.384.1±0.412ms
Ours87.1±0.286.3±0.315ms
MethodAccuracyF1Latency
Baseline85.2±0.384.1±0.412ms
Ours87.1±0.286.3±0.315ms

4.2 Ablation

4.2 Ablation

ConfigurationAccuracyΔ
Full87.1±0.2—
w/o Module A85.8±0.3-1.3
w/o Module B86.2±0.2-0.9
ConfigurationAccuracyΔ
Full87.1±0.2—
w/o Module A85.8±0.3-1.3
w/o Module B86.2±0.2-0.9

4.3 Statistical Analysis

4.3 Statistical Analysis

  • Test: paired t-test
  • p-value: 0.002 (significant at α=0.05)
  • Seeds: 5 (42, 123, 456, 789, 1024)
  • Test: paired t-test
  • p-value: 0.002 (significant at α=0.05)
  • Seeds: 5 (42, 123, 456, 789, 1024)

5. Analysis

5. Analysis

[Interpretation of results, patterns observed]
[Interpretation of results, patterns observed]

6. Conclusions

6. Conclusions

[Key takeaways, next steps]
[Key takeaways, next steps]

7. Reproducibility

7. Reproducibility

  • Code: [URL or "available upon request"]
  • Data: [URL or "see Section 2.3"]
  • Random seeds: [list]
undefined
  • Code: [URL or "available upon request"]
  • Data: [URL or "see Section 2.3"]
  • Random seeds: [list]
undefined

2. System Design Document

2. 系统设计文档

markdown
undefined
markdown
undefined

System Design: [System Name]

System Design: [System Name]

Version: [v1.0] Status: [Draft/Review/Final] Last Updated: [Date]
Version: [v1.0] Status: [Draft/Review/Final] Last Updated: [Date]

1. Overview

1. Overview

[1 paragraph: what the system does, why it exists]
[1 paragraph: what the system does, why it exists]

2. Requirements

2. Requirements

2.1 Functional Requirements

2.1 Functional Requirements

  • FR1: [requirement]
  • FR2: [requirement]
  • FR1: [requirement]
  • FR2: [requirement]

2.2 Non-Functional Requirements

2.2 Non-Functional Requirements

  • NFR1: Performance — [specification]
  • NFR2: Scalability — [specification]
  • NFR3: Reliability — [specification]
  • NFR1: Performance — [specification]
  • NFR2: Scalability — [specification]
  • NFR3: Reliability — [specification]

3. Architecture

3. Architecture

3.1 High-Level Design

3.1 High-Level Design

[Architecture diagram]
[Architecture diagram]

3.2 Component Design

3.2 Component Design

ComponentResponsibilityInterface
[comp1][what it does][API/events]
[comp2][what it does][API/events]
ComponentResponsibilityInterface
[comp1][what it does][API/events]
[comp2][what it does][API/events]

3.3 Data Flow

3.3 Data Flow

[Sequence diagram or flowchart]
[Sequence diagram or flowchart]

4. API Design

4. API Design

4.1 Endpoints

4.1 Endpoints

MethodPathDescription
GET/api/v1/[resource][description]
POST/api/v1/[resource][description]
MethodPathDescription
GET/api/v1/[resource][description]
POST/api/v1/[resource][description]

4.2 Data Models

4.2 Data Models

typescript
interface [Model] {
  id: string;
  field: type;
}
typescript
interface [Model] {
  id: string;
  field: type;
}

5. Database Design

5. Database Design

[ER diagram, schema]
[ER diagram, schema]

6. Deployment

6. Deployment

[Infrastructure, Docker, K8s config]
[Infrastructure, Docker, K8s config]

7. Testing Strategy

7. Testing Strategy

[Unit, integration, e2e tests]
[Unit, integration, e2e tests]

8. Monitoring

8. Monitoring

[Metrics, alerts, dashboards]
[Metrics, alerts, dashboards]

9. Security

9. Security

[Authentication, authorization, data protection]
[Authentication, authorization, data protection]

10. Future Work

10. Future Work

[Planned improvements]
undefined
[Planned improvements]
undefined

3. Code Documentation

3. 代码文档

markdown
undefined
markdown
undefined

[Module Name] Documentation

[Module Name] Documentation

Overview

Overview

[What this module does]
[What this module does]

Installation

Installation

bash
pip install [package]
bash
pip install [package]

Quick Start

Quick Start

python
from module import Class
python
from module import Class

Initialize

Initialize

obj = Class(param1=value1)
obj = Class(param1=value1)

Use

Use

result = obj.method(input)
undefined
result = obj.method(input)
undefined

API Reference

API Reference

Class: 
ClassName

Class: 
ClassName

__init__(self, param1, param2)

__init__(self, param1, param2)

Description: Initialize the class.
Parameters:
NameTypeDefaultDescription
param1strrequired[description]
param2int10[description]
Example:
python
obj = Class(param1="value", param2=20)
Description: Initialize the class.
Parameters:
NameTypeDefaultDescription
param1strrequired[description]
param2int10[description]
Example:
python
obj = Class(param1="value", param2=20)

method(self, input)

method(self, input)

Description: [what it does]
Parameters:
NameTypeDescription
inputnp.ndarray[description]
Returns:
TypeDescription
dict[description]
Raises:
ExceptionCondition
ValueError[when]
Example:
python
result = obj.method(np.array([1, 2, 3]))
Description: [what it does]
Parameters:
NameTypeDescription
inputnp.ndarray[description]
Returns:
TypeDescription
dict[description]
Raises:
ExceptionCondition
ValueError[when]
Example:
python
result = obj.method(np.array([1, 2, 3]))

Configuration

Configuration

VariableDefaultDescription
DEBUGFalseEnable debug mode
VariableDefaultDescription
DEBUGFalseEnable debug mode

Changelog

Changelog

v1.0.0 (YYYY-MM-DD)

v1.0.0 (YYYY-MM-DD)

  • Initial release
undefined
  • Initial release
undefined

4. Research Memo

4. 研究备忘录

markdown
undefined
markdown
undefined

Research Memo: [Topic]

Research Memo: [Topic]

Date: [YYYY-MM-DD] Author: [Name] Status: [Draft/Reviewed]
Date: [YYYY-MM-DD] Author: [Name] Status: [Draft/Reviewed]

Context

Context

[Why this memo exists, what triggered it]
[Why this memo exists, what triggered it]

Key Observations

Key Observations

  1. [Observation 1]
  2. [Observation 2]
  3. [Observation 3]
  1. [Observation 1]
  2. [Observation 2]
  3. [Observation 3]

Analysis

Analysis

[Detailed analysis of observations]
[Detailed analysis of observations]

Implications

Implications

[What this means for the research]
[What this means for the research]

Action Items

Action Items

  • [Action 1]
  • [Action 2]
  • [Action 1]
  • [Action 2]

References

References

[Related papers, experiments, discussions]

---
[Related papers, experiments, discussions]

---

Report Quality Checklist

报告质量检查表

Content

内容

  • Clear objective statement
  • Sufficient detail for reproduction
  • Results with mean ± std (min 3 seeds)
  • Statistical significance tests
  • Honest limitations acknowledged
  • 清晰的目标陈述
  • 足够的细节以支持复现
  • 结果包含均值±标准差（至少3个随机种子）
  • 统计显著性检验
  • 坦诚说明局限性

Format

格式

  • Consistent notation
  • Figures with captions
  • Tables with captions
  • References formatted
  • Version number
  • Date
  • 符号一致
  • 图表带有标题
  • 表格带有标题
  • 参考文献格式规范
  • 版本号
  • 日期

Technical

技术层面

  • Code snippets tested
  • Commands verified
  • Links working
  • Version numbers accurate
  • 代码片段已测试
  • 命令已验证
  • 链接可用
  • 版本号准确

Integration Flow

整合流程

experiment-tracking (data)
    ↓
technical-report (this skill)
    ├── Experiment Report → advisor/committee
    ├── System Design → team/collaborators
    ├── Code Documentation → users/developers
    └── Research Memo → self/team
    ↓
thesis-writing (integrate into thesis)
    ↓
paper-writing (extract for paper)
    ↓
conference-paper (present findings)
    ↓
progress-report (summarize for advisor)
experiment-tracking (data)
    ↓
technical-report (this skill)
    ├── Experiment Report → advisor/committee
    ├── System Design → team/collaborators
    ├── Code Documentation → users/developers
    └── Research Memo → self/team
    ↓
thesis-writing (integrate into thesis)
    ↓
paper-writing (extract for paper)
    ↓
conference-paper (present findings)
    ↓
progress-report (summarize for advisor)