Inspect the first few lines of a file and its total line count in one shell call. Combine head and wc -l with a visible separator to check generated or modified files with fewer calls.
Build TypeScript data services that separate API calls from display components. Add caching and a circuit breaker, which pauses calls after repeated failures, while keeping user-triggered requests able to report errors directly.
Build dashboard panels in plain TypeScript, with regularly refreshed content, loading indicators, item counts, and error messages. Provides a shared panel class and examples for stocks, news, and weather, including retry handling and small trend charts.
Connect stock data to an application using Finnhub Stock API. The skill covers current quotes, company profiles, historical prices, and market news, with server-side request handling to protect API keys and manage cached responses.
Write large text files when write_file or shell_agent fails because of content size limits. The fallback passes multiline content through run_shell to Python, with guidance on escaping characters and checking the saved file.
Recover failed writes of large files or code containing special characters by placing content in a temporary Python script. Use raw strings or Base64 encoding, then check that the output file exists and has content.
List and count folders in a specified path using list_dir results. Distinguishes directories from files, handles hidden and system entries, and presents an alphabetically sorted inventory with the path and total count.
Build news feeds with GNews, NewsAPI, or HackerNews using headline retrieval, keyword search, and category filters. Includes examples for organizing article data, displaying sources and publication times, and handling failed requests in a frontend service.
Describe the files you want created, updated, or organized, and let shell_agent choose how to perform the work. It selects tools such as Python or Bash, executes the operation, and handles errors with retries.
Analyze agent runs that fail before any work begins. Check iteration counts, tool use, logs, and outputs to identify startup failures, then direct investigation toward environment setup, input formatting, permissions, or resource limits.
Keep report writing focused on the original request when information retrieval fails. This skill guides agents to use existing knowledge, disclose missing information, create a structured document with write_file, and verify that the file exists.
Recover from failed automated file creation with an explicit shell workflow. The skill guides agents through checking folders, writing and running scripts, and inspecting output files to confirm that deliverables were actually created.
Check deliverables before marking a task complete. Review the requested file format, confirm every reference file was used, and compare the result with the original request to catch omissions and work that misses the goal.
Diagnose connection problems with government websites by checking SSL certificates, which verify website identity, and proxy settings. Follow structured troubleshooting steps, record failed attempts, and explore alternative APIs or data sources when access remains blocked.
Create or edit Excel workbooks with checks for sheet names, row counts, formulas, and merged cells. Reopen the saved file and verify each requirement, recording explicit results rather than relying on a completion summary.
Verify saved Excel files directly with Python and openpyxl, a workbook library. Check sheets, headers, row counts, marked rows, and output files to resolve conflicting agent reports and summarize what actually exists on disk.
Process complex Excel files by running Python scripts directly through run_shell. Use pandas for data transformations and openpyxl for workbook editing, including multiple sheets, formulas, and formatting, with explicit saving and error reporting.
Create and process Excel files, PDF checklists, and structured reports by running Python scripts directly. The skill covers multiple sheets, formulas, formatting, data transformations, and error handling for document workflows with several steps.
Validate spreadsheet sources before running Python scripts to transform data, update multiple sheets, or apply formatting. Check required data first, try backup sources when needed, and report errors clearly if processing cannot proceed.
Process Excel files with Python after checking that input files and source URLs are accessible. The workflow covers data transformations, formulas, and formatting, with alternative sources and error logs when access fails.
Update or create Excel workbooks with checks against the requested structure. Identify the correct file before editing, then reread the saved workbook to verify sheet names, populated columns, row counts, and selected sample counts.
Create or modify Excel workbooks, then independently check the saved file with Python. Verify required sheets, columns, populated cells, row counts, and sample selections against the requested structure before reporting the work as complete.
Create medical visit notes in SOAP format, which separates patient reports, observed findings, clinical assessment, and care plans. Organize the available information into a complete Markdown or plain text file written in one operation.
Run Python through run_shell when code execution tools repeatedly fail. This workflow embeds a multiline script directly in a shell command, supporting tasks such as CSV conversion and PDF generation while checking execution output.
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