Lab Report Writer

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

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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.

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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:

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:

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:

# 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)

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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 |

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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.

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Error Analysis Formulas

Direct Measurement Uncertainty

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

Type B (instrument): $$uB = \frac{\Delta{instr}}{\sqrt{3}}$$ (uniform distribution)

Combined standard uncertainty: $$uc = \sqrt{uA^2 + u_B^2}$$

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

Indirect Measurement (Propagation)

If $y = f(x1, x2, ..., xn)$: $$uc(y) = \sqrt{\sum{i=1}^{n}\left(\frac{\partial f}{\partial xi}\right)^2 uc^2(xi)}$$

Linear Least-Squares Fit

For $y = ax + b$: $$a = \frac{n\sum xiyi - \sum xi\sum yi}{n\sum xi^2 - (\sum xi)^2}$$ $$b = \frac{\sum yi - a\sum xi}{n}$$ $$R^2 = \frac{(n\sum xiyi - \sum xi\sum yi)^2}{[n\sum xi^2 - (\sum xi)^2][n\sum yi^2 - (\sum yi)^2]}$$

Relative Error

$$\delta = \frac{|x{measured} - x{true}|}{x_{true}} \times 100\%$$

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Chart Generation Guidelines

Using Python + matplotlib

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() |

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Troubleshooting

Issue: docx npm module not found

Solution: Find the global install path and set NODE_PATH:

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:

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.

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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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: dxkjuanjuan
• Source: dxkjuanjuan/lab-report-writer
• License: MIT

Install and usage instructions live in the source repository linked above.