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raman-fitting拉曼拟合

Agent Skill

raman-fitting 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。

总安装

366

周安装

34

GitHub Stars

93

下载量

280
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

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:raman-fitting(拉曼拟合)
来源仓库:https://github.com/letta-ai/skills
仓库路径:skills/raman-fitting
安装命令:
npx skills add https://github.com/letta-ai/skills --skill raman-fitting
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/letta-ai/skills --skill raman-fitting

简介

用于查找、检索与拉曼光谱拟合相关的文献、方法或工具。

  • 适合根据关键词快速定位算法、参数设置或实验数据。
  • 可结合物理模型或化学特征筛选高相关度结果。
  • 需确认数据来源是否权威,避免误导性结论。
  • 建议与实际测量数据对比验证拟合效果。raman-fitting 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Raman Fitting

Overview

This skill guides the analysis and curve fitting of Raman spectroscopy data, particularly for materials like graphene where characteristic peaks (G, D, 2D bands) must be accurately extracted. The primary challenge in these tasks is ensuring correct data ingestion before any analysis begins.

Critical First Step: Data Parsing Verification

Before any fitting or analysis, verify data is parsed correctly. This is the most common source of failure in spectroscopic data analysis.

Data File Inspection Protocol

  1. Read raw file content first - Examine the first 5-10 lines of the raw data file to understand the actual format
  2. Identify potential format issues:

- Line number prefixes (e.g., "1→", "2→" before actual data) - Decimal separators (comma vs period - European vs US format) - Column delimiters (tab, comma, semicolon, whitespace) - Header lines that need to be skipped - Encoding issues (UTF-8, ISO-8859-1, etc.)

  1. Parse and print first few values - After parsing, print the first 3-5 data points to verify they match expectations: # Always verify parsing immediately print(f"First 5 wavenumbers: {wavenumbers[:5]}") print(f"First 5 intensities: {intensities[:5]}") print(f"Wavenumber range: {min(wavenumbers)} to {max(wavenumbers)} cm⁻¹")
  2. Sanity check against physical expectations:

- Raman wavenumbers typically range from 100-4000 cm⁻¹ - If values fall outside this range, parsing is likely incorrect - Graphene G peak should appear near 1580 cm⁻¹ - Graphene 2D peak should appear near 2700 cm⁻¹

Common Parsing Pitfalls

IssueExample Raw DataCorrect Handling
Line number prefix1→1580.5,12345Strip prefix before delimiter split
Comma decimal1580,5\t12345,7Replace comma with period
Mixed delimiters1580.5, 12345Handle both tab and comma
BOM character\ufeff1580.5Strip BOM from file start

Physical Constraints for Raman Spectroscopy

Graphene/Carbon Materials

Apply these domain-specific constraints when fitting:

PeakExpected Position (cm⁻¹)Typical Width (FWHM)Physical Meaning
D band~135020-50Defects/disorder
G band~158010-20sp² carbon vibration
2D band~270025-60Second-order D band
D+G~294030-60Combination mode

Fitting Parameter Bounds

Set physically reasonable bounds to avoid nonsensical fits:

# Example bounds for graphene G peak
bounds_lower = [1560, 5, 0]      # [x0, gamma, amplitude]
bounds_upper = [1600, 50, 1e6]

# Example bounds for graphene 2D peak
bounds_lower = [2650, 10, 0]
bounds_upper = [2750, 100, 1e6]

Fitting Approach

Step 1: Visual Identification of Peaks

Before fitting, identify peaks visually:

  • Plot the full spectrum to see all features
  • Note approximate peak positions and relative intensities
  • Identify baseline behavior (flat, sloped, curved)

Step 2: Baseline Correction

Consider baseline subtraction before peak fitting:

  • Linear baseline for simple cases
  • Polynomial baseline for curved backgrounds
  • Iterative methods for complex baselines

Step 3: Peak Function Selection

Common peak shapes for Raman:

  • Lorentzian: Natural line shape, use when peaks are sharp
  • Gaussian: Use when instrumental broadening dominates
  • Voigt: Convolution of both, most physically accurate
  • Pseudo-Voigt: Computationally efficient approximation

Lorentzian function:

I(x) = amplitude * (gamma²) / ((x - x0)² + gamma²)

Step 4: Initial Parameter Estimation

Use visual inspection or automatic peak finding to set initial guesses:

from scipy.signal import find_peaks

# Find peaks in the data
peaks, properties = find_peaks(intensity, height=threshold, distance=min_distance)

# Use found peak positions as initial guesses
for peak_idx in peaks:
    x0_guess = wavenumber[peak_idx]
    amplitude_guess = intensity[peak_idx]

Step 5: Fitting and Validation

After fitting, validate results:

  1. Check R² value - Should be > 0.95 for good fits
  2. Check parameter boundaries - Parameters hitting bounds indicate problems
  3. Visual inspection - Plot data with fitted curve overlay
  4. Physical plausibility - Do fitted parameters make physical sense?

Verification Strategies

Red Flags That Indicate Problems

  • R² < 0.9: Poor fit quality, investigate cause
  • Parameters at bounds: Fit is constrained, not converged
  • Peak positions far from expected: Data parsing or assignment error
  • Negative amplitudes: Physically impossible, check data/model
  • Very large/small widths: Outside physical expectations

Systematic Debugging Checklist

  1. Raw data file examined line by line
  2. Parsing verified by printing first few values
  3. Wavenumber range is physically reasonable (100-4000 cm⁻¹)
  4. Peaks visible in plotted data at expected positions
  5. Initial guesses based on actual data, not assumptions
  6. Fit quality metrics (R², residuals) calculated and checked
  7. Fitted parameters compared to literature values

When Fits Fail

If fitting produces poor results:

  1. Go back to data parsing - Most common source of error
  2. Narrow the fitting window - Fit one peak at a time
  3. Improve initial guesses - Use values closer to peak maximum
  4. Check for overlapping peaks - May need multi-peak model
  5. Examine residuals - Systematic patterns indicate model issues

Output Format

When reporting fitted parameters, include:

  • Peak position (x0) with uncertainty
  • Width parameter (gamma or FWHM)
  • Amplitude or integrated intensity
  • Goodness of fit metric (R², chi-squared)
  • Comparison to expected/literature values if available

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

30.22%
按下载量换算85

Gemini CLI

26.36%
按下载量换算74

Antigravity

17.97%
按下载量换算50

windsurf

11.69%
按下载量换算33

OpenCode

7.37%
按下载量换算21

Codex

3.36%
按下载量换算9

安全审计

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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来源信息

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