Desktop analysis and reporting tools. Includes music analysis with popularity scoring and file statistics (count files, folders, and calculate total size).
npx skills add https://github.com/MassLab-SII/open-agent-skills --skill desktop-analysis
This skill provides data analysis and reporting tools:
./folder/file.txt or folder/file.txt).Analyzes music data from multiple artists, calculates popularity scores using a weighted formula, and generates a detailed analysis report.
popularity_score = (rating × W1) + (play_count_normalized × W2) + (year_factor × W3)| Parameter | Default | Description |
|-----------|---------|-------------|
| --output | music_analysis_report.txt | Output report filename |
| --rating-weight | 0.4 | Weight for rating score |
| --play-count-weight | 0.4 | Weight for normalized play count |
| --year-weight | 0.2 | Weight for year factor |
# Generate music analysis report with default weights (0.4, 0.4, 0.2)
python music_report.py ./music
# Use a custom output filename
python music_report.py ./music --output my_report.txt
# Use custom weights for the popularity formula
python music_report.py ./music --rating-weight 0.5 --play-count-weight 0.3 --year-weight 0.2
Generate file statistics for a directory: total files, folders, and size.
python file_statistics.py .
Recursively list all files under a given directory path. Useful for quickly understanding project directory structure.
# List all files (excluding hidden)
python list_all_files.py .
# Include hidden files
python list_all_files.py ./data --include-hidden
Below are the basic tool functions. These are atomic operations for flexible combination.
Prefer Skills over Basic Tools: When a task matches one of the Skills above, use the corresponding Skill instead of Basic Tools. Skills are more efficient because they can perform batch operations in a single call.
Prefer List All Files over list_directory/list_files: When you need to list files in a directory, prefer using the list_all_files.py skill instead of list_directory or list_files basic tools. The skill provides recursive listing with better output formatting.
Note: Code should be written without line breaks.
# Standard format
python run_fs_ops.py -c "await fs.read_text_file('./file.txt')"
read_text_file(path, head=None, tail=None)Use Cases:
Example:
python run_fs_ops.py -c "await fs.read_text_file('./data/file.txt')"
read_multiple_files(paths)Use Cases:
Example:
python run_fs_ops.py -c "await fs.read_multiple_files(['./a.txt', './b.txt'])"
write_file(path, content)Use Cases:
⚠️ Warning: Do NOT include triple backticks ( `) in the content, as this will break command parsing.
Example:
python run_fs_ops.py -c "await fs.write_file('./new.txt', 'Hello World')"
edit_file(path, edits)Use Cases:
Example:
python run_fs_ops.py -c "await fs.edit_file('./file.txt', [{'oldText': 'foo', 'newText': 'bar'}])"
create_directory(path)Use Cases:
Example:
python run_fs_ops.py -c "await fs.create_directory('./new/nested/dir')"
list_directory(path)Use Cases:
Example:
python run_fs_ops.py -c "await fs.list_directory('.')"
list_files(path=None, exclude_hidden=True)Use Cases:
Example:
python run_fs_ops.py -c "await fs.list_files('./data')"
move_file(source, destination)Use Cases:
Example:
python run_fs_ops.py -c "await fs.move_file('./old.txt', './new.txt')"
search_files(pattern, base_path=None)Use Cases:
Example:
python run_fs_ops.py -c "await fs.search_files('*.txt')"
get_file_info(path)Use Cases:
Example:
python run_fs_ops.py -c "await fs.get_file_info('./file.txt')"
get_file_size(path)Use Cases:
Example:
python run_fs_ops.py -c "await fs.get_file_size('./file.txt')"
get_file_ctime(path) / get_file_mtime(path)Use Cases:
Example:
python run_fs_ops.py -c "await fs.get_file_mtime('./file.txt')"
get_files_info_batch(filenames, base_path=None)Use Cases:
Example:
python run_fs_ops.py -c "await fs.get_files_info_batch(['a.txt', 'b.txt'], './data')"
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Take masslab-sii/desktop-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.