Lab Report Writer

---
name: "Lab Report Writer"
description: "Generate professional lab reports for university courses, scientific research, engineering tests, and medical/material experiments. Supports three input modes (topic/raw data/draft improvement), auto-research with WebSearch, data tables & chart generation, error analysis, and output as docx/markdown. Use when writing experiment reports, lab reports, test reports, or any structured scientific/technical document. Triggers: lab report, experiment report, test report, experimental report, write report, generate report."
---

# Lab Report Writer

Generate professional, low-plagiarism lab reports with proper structure, data analysis, and references.

## What This Skill Does

1. **Three input modes**: From topic/description → full report; From raw data → analysis + report; From draft → improvement & formatting
2. **Covers all report types**: University course labs, scientific research, engineering tests, medical/material experiments
3. **Auto-research**: WebSearch for theoretical background, related work, and real citable references
4. **Data processing**: Tables, charts (line/scatter/bar with error bars), uncertainty propagation, least-squares fitting
5. **Low plagiarism**: Original writing throughout, proper paraphrasing, no copy-paste
6. **Multiple output formats**: docx (with tables/charts/formulas) or markdown, user's choice

## Prerequisites

- Node.js 18+ (for docx generation via npm `docx` package)
- If docx output: `docx` npm package must be available (`npm list -g docx` or project-local)
- For charts: Python 3 with matplotlib, OR use ASCII/markdown tables as fallback

---

## Level 2: Quick Start

### Interactive Launch

When the user mentions writing a lab report, experiment report, or test report, start the interactive flow:

```
/lab-report
```

The skill will ask clarifying questions via AskUserQuestion before generating anything.

---

## Level 3: Detailed Instructions

### Step 1: Interactive Questionnaire

Use AskUserQuestion to collect the following information. Ask in 1-2 rounds (max 4 questions per round).

#### Round 1 — Core Parameters

| Question | Header | Options | Notes |
|----------|--------|---------|-------|
| Report type? | 报告类型 | 高校课程实验, 科研实验报告, 工程测试报告, 医学/材料实验 | Multi-select OK |
| Input mode? | 输入模式 | 从题目/描述生成, 从实验数据生成, 从草稿改进 | Determines workflow |
| Output format? | 输出格式 | Word文档(docx), Markdown(md), 同时输出docx+md | User picks |
| Target word count? | 字数要求 | 2000-5000字, 5000-8000字, 8000-15000字, 不限 | Guides depth |

#### Round 2 — Content Details (adapt based on Round 1 answers)

| Question | Header | When to Ask |
|----------|--------|-------------|
| What is the experiment topic/title? | 实验题目 | Input mode = topic |
| What course/subject is this for? | 课程/学科 | University course type |
| Do you have raw data files? (provide paths) | 数据文件 | Input mode = data |
| Do you have a draft file? (provide path) | 草稿文件 | Input mode = draft |
| What sections do you need? | 报告章节 | If non-standard structure needed |
| Any specific formatting requirements? | 格式要求 | If user mentions rubric/grading criteria |

### Step 2: Load Grading Criteria (If Provided)

If the user provides a grading rubric file path (e.g., `成绩评定表.docx`):

1. Read the file using `textutil -convert txt -stdout` (macOS) or Python
2. Extract scoring dimensions and weights
3. Align the report structure and content depth to maximize score on each dimension
4. Store extracted criteria in memory for self-check at the end

### Step 3: Research Phase

Execute WebSearch queries to gather:

1. **Theoretical background**: Core principles, equations, mechanisms
2. **Related work**: Key papers, review articles, textbook references
3. **Reference literature**: Real, verifiable sources with complete bibliographic info

Search strategy:
- Start broad: `"[topic] 实验原理 综述"` or `"[topic] experiment principle review"`
- Then specific: `"[topic] 数据处理 误差分析"` or `"[topic] data analysis uncertainty"`
- For references: `"[author] [key term]" IEEE/Sensors/Review` to find specific papers
- Run 10-20 searches for thorough coverage; 5-8 for shorter reports

**Critical**: Record every search result that yields a usable reference. Do NOT fabricate references.

### Step 4: Section Template Selection

Based on report type, select the appropriate section template from [Section Templates](#section-templates) below.

### Step 5: Data Processing (If Applicable)

If the user provides experimental data:

1. **Read data**: CSV via `Read`, Excel via Python, or JSON directly
2. **Generate tables**: Format as markdown tables in the report; for docx, use Table objects
3. **Compute statistics**: Mean, standard deviation, relative error
4. **Uncertainty propagation**: Apply formula Δy = √(Σ(∂y/∂xi · Δxi)²)
5. **Curve fitting**: Linear least-squares y = ax + b with R², or polynomial if needed
6. **Charts**: Generate using matplotlib (save as PNG) or describe as ASCII for markdown

For data processing code, use Python via Bash:
```python
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
# ... analysis code ...
```

### Step 6: Write the Report

Write section by section, following these principles:

#### Low-Plagiarism Writing Rules

1. **Original phrasing**: Never copy sentences from sources. Read, understand, rephrase in your own words.
2. **Structure variation**: Don't follow the exact paragraph structure of any single source.
3. **Specific details**: Include concrete numbers, formulas, and technical terms — generic text is more likely to match existing content.
4. **Personal analysis**: The "discussion/analysis" section should reflect independent reasoning, not paraphrased conclusions.
5. **Chinese-English mixing**: For Chinese reports, mixing technical terms in English (with Chinese explanation) naturally lowers similarity scores.

#### Section Writing Order

Recommended order for efficiency:
1. Write the body sections first (principle, procedure, data, analysis)
2. Then introduction and conclusion (these frame the body)
3. Then abstract (summarizes everything)
4. Finally compile references

### Step 7: Format and Generate Output

#### For docx output:

Use Node.js with the `docx` npm package. Generate using a script:

```javascript
const docx = require("docx");
const { Document, Packer, Paragraph, TextRun, AlignmentType, Table, ... } = docx;
// Build document with proper formatting
// Font: SimSun (宋体) body, SimHei (黑体) headings, KaiTi (楷体) abstract
// Line spacing: 1.5x (360 twips)
// Margins: 2.54cm top/bottom, 3.17cm left/right
// First-line indent: 2 characters (~480 twips)
```

Find the `docx` module path:
```bash
# Check global install
npm list -g docx 2>/dev/null
# Use NODE_PATH when running
NODE_PATH=$(npm root -g) node generate-report.js
```

#### For markdown output:

Write directly with `Write` tool. Include:
- YAML frontmatter with title, author, date
- Proper heading hierarchy
- Markdown tables for data
- Math notation with `$...$` or `$$...$$`
- Image references for charts

### Step 8: Self-Check Against Grading Criteria

If grading criteria were loaded, verify:

| Dimension | Self-Check |
|-----------|------------|
| Format & language | Correct font, spacing, margins? No typos? |
| Abstract quality | Covers all sections? Concise and readable? |
| Introduction depth | Sufficient background research? Clear motivation? |
| Logic & evidence | Arguments supported by data? Smooth transitions? |
| Conclusion validity | Follows from analysis? No overclaims? |
| Personal insight | Unique observations? Own perspective? |

### Step 9: Deliver

1. Save output file to the user's specified location (default: `~/Desktop/`)
2. Report word count (Chinese characters + English terms)
3. List any manual edits needed (e.g., author name, school info)
4. Clean up temporary files (generation scripts, intermediate data)

---

## Section Templates

### Template A: University Course Lab Report

| # | Section | Content | Font Style |
|---|---------|---------|------------|
| - | Title | Experiment name + descriptive subtitle | SimHei, 小二号, bold, centered |
| - | Author info | Name, student ID, department | KaiTi, 小四号, centered |
| 1 | Abstract | 150-300 words summarizing purpose, method, results, conclusion | KaiTi, 小四号 |
| 2 | Introduction | Background, motivation, objectives | SimSun, 小四号 |
| 3 | Theoretical Principles | Core theory, equations (numbered), diagrams | SimSun, 小四号 |
| 4 | Apparatus & Materials | Equipment list with model/specs, materials | SimSun, 小四号 |
| 5 | Experimental Procedure | Step-by-step, parameter settings, precautions | SimSun, 小四号 |
| 6 | Data & Results | Raw data tables, processed results, charts | SimSun, 小四号 |
| 7 | Analysis & Discussion | Error analysis, comparison with theory, uncertainty | SimSun, 小四号 |
| 8 | Conclusion | Summary of findings, answers to objectives | SimSun, 小四号 |
| 9 | Reflections | Personal insights, suggestions for improvement | SimSun, 小四号 |
| - | References | GB/T 7714 format, [1][2]... numbered | SimSun, 五号 |

### Template B: Scientific Research Report

| # | Section | Content |
|---|---------|---------|
| - | Title | Descriptive, concise |
| - | Authors & Affiliations | Name, institution, email |
| 1 | Abstract | Background, method, key results, significance |
| 2 | Introduction | Literature review, gap identification, research questions |
| 3 | Materials & Methods | Detailed methodology, reproducibility focus |
| 4 | Results | Data presentation, statistical analysis, figures |
| 5 | Discussion | Interpretation, limitations, comparison with literature |
| 6 | Conclusion | Key findings, implications, future directions |
| - | Acknowledgments | Funding, assistance |
| - | References | GB/T 7714 or APA format |

### Template C: Engineering Test Report

| # | Section | Content |
|---|---------|---------|
| - | Title | Test item + test type |
| - | Test Info | Date, location, personnel, equipment |
| 1 | Test Objective | Purpose, acceptance criteria |
| 2 | Test Method | Standard followed, procedure, parameters |
| 3 | Test Environment | Conditions, instrumentation, calibration |
| 4 | Test Data | Raw measurements, calculated results |
| 5 | Data Analysis | Statistical treatment, comparison with spec |
| 6 | Conclusions | Pass/fail, compliance assessment |
| - | Appendices | Raw data, calibration certificates |

### Template D: Medical/Material Experiment Report

| # | Section | Content |
|---|---------|---------|
| 1 | Abstract | Structured: background, methods, results, conclusion |
| 2 | Introduction | Clinical/scientific context, research gap |
| 3 | Materials | Sample prep, reagents, instruments |
| 4 | Methods | Experimental protocol, controls, ethical approval |
| 5 | Results | Quantitative data, statistical tests, figures |
| 6 | Discussion | Clinical/scientific significance, limitations |
| 7 | Conclusion | Key findings, translational implications |

---

## Reference Format Guide (GB/T 7714)

### Journal Article
```
[1] Author1, Author2, Author3. Title[J]. Journal Name, Year, Volume(Issue): Pages.
```
Example:
```
[1] Smith C S. Piezoresistance effect in germanium and silicon[J]. Physical Review, 1954, 94(1): 42-49.
```

### Book/Monograph
```
[2] Author. Title[M]. Edition. Place: Publisher, Year: Pages.
```

### Conference Paper
```
[3] Author1, Author2. Title[C]//Conference Name. Place: Publisher, Year: Pages.
```

### Technical Report
```
[4] Author/Organization. Title[R]. Place: Institution, Year.
```

### Online Resource
```
[5] Author. Title[EB/OL]. (Date)[Cite Date]. URL.
```

### Data Sheet / Standard
```
[6] Organization. Title[Z]. Place: Organization, Year.
```

---

## Error Analysis Formulas

### Direct Measurement Uncertainty

**Type A (statistical)**:
$$u_A = \sqrt{\frac{\sum_{i=1}^{n}(x_i - \bar{x})^2}{n(n-1)}}$$

**Type B (instrument)**:
$$u_B = \frac{\Delta_{instr}}{\sqrt{3}}$$ (uniform distribution)

**Combined standard uncertainty**:
$$u_c = \sqrt{u_A^2 + u_B^2}$$

**Expanded uncertainty** (k=2 for 95% confidence):
$$U = k \cdot u_c = 2u_c$$

### Indirect Measurement (Propagation)

If $y = f(x_1, x_2, ..., x_n)$:
$$u_c(y) = \sqrt{\sum_{i=1}^{n}\left(\frac{\partial f}{\partial x_i}\right)^2 u_c^2(x_i)}$$

### Linear Least-Squares Fit

For $y = ax + b$:
$$a = \frac{n\sum x_iy_i - \sum x_i\sum y_i}{n\sum x_i^2 - (\sum x_i)^2}$$
$$b = \frac{\sum y_i - a\sum x_i}{n}$$
$$R^2 = \frac{(n\sum x_iy_i - \sum x_i\sum y_i)^2}{[n\sum x_i^2 - (\sum x_i)^2][n\sum y_i^2 - (\sum y_i)^2]}$$

### Relative Error
$$\delta = \frac{|x_{measured} - x_{true}|}{x_{true}} \times 100\%$$

---

## Chart Generation Guidelines

### Using Python + matplotlib

```python
import matplotlib
matplotlib.use('Agg')  # Non-interactive backend
import matplotlib.pyplot as plt
import numpy as np

# Configure for Chinese labels
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'Heiti TC']
plt.rcParams['axes.unicode_minus'] = False

# Example: Line chart with error bars
fig, ax = plt.subplots(figsize=(8, 5))
x = np.array([1, 2, 3, 4, 5])
y = np.array([2.1, 3.8, 6.2, 7.9, 10.1])
y_err = np.array([0.2, 0.3, 0.2, 0.4, 0.3])

ax.errorbar(x, y, yerr=y_err, fmt='o-', capsize=4, label='Measured')
ax.set_xlabel('Independent Variable')
ax.set_ylabel('Dependent Variable')
ax.set_title('Experiment Results')
ax.legend()
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('chart.png', dpi=200)
print("Chart saved: chart.png")
```

### Chart Types by Data

| Data Type | Chart Type | matplotlib Function |
|-----------|-----------|-------------------|
| Single variable vs parameter | Line chart | `plot()` + `errorbar()` |
| Two variables correlation | Scatter plot | `scatter()` |
| Categorical comparison | Bar chart | `bar()` |
| Distribution | Histogram | `hist()` |
| Time series | Line chart | `plot()` |

---

## Troubleshooting

### Issue: docx npm module not found
**Solution**: Find the global install path and set NODE_PATH:
```bash
npm list -g docx  # Find install location
NODE_PATH=$(npm root -g) node generate-report.js
```

### Issue: Chinese fonts not rendering in docx
**Solution**: Use standard font names: SimSun (宋体), SimHei (黑体), KaiTi (楷体). On macOS, these may render as fallback — the document will display correctly on Windows with Office.

### Issue: matplotlib Chinese characters show as boxes
**Solution**: Add font configuration:
```python
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'Heiti TC']
```

### Issue: WebSearch returns no results
**Solution**: Try English queries, simplify terms, search for specific author+keyword combinations. Fall back to general knowledge if needed (mark references as "[待核实]").

### Issue: Data file format not recognized
**Solution**: For Excel (.xlsx), use Python with openpyxl or pandas. For .csv, use Read tool directly.

---

## Related Skills

- **kimi-webbridge**: For web research when WebSearch is disabled or insufficient
- **ui-styling**: For chart styling and visual design
- **design**: For report cover page or presentation-quality figures