Bundle prompts with selected files and directories for AI analysis.
npx skills add https://github.com/cogpy/clawcog --skill oracle-cogpy
Please help me install this Agent Skill. Skill: oracle Source: https://github.com/cogpy/clawcog/tree/main/skills/oracle Command: npx skills add https://github.com/cogpy/clawcog --skill oracle-cogpy
This Skill streamlines the process of getting AI-powered insights and assistance by bundling prompts with relevant code and documentation, ensuring the AI has the necessary context to provide accurate and actionable advice.
Use the oracle skill to preview the token count for a task by running oracle --dry-run summary -p "<task>" --file "src/**".
High-intent search queries and answers about installing and using this skill.
You bundle source files and directories with your prompt for AI code debugging by passing file paths like "src/**" to ensure the AI receives the full context needed to analyze problems and suggest fixes.
Yes, you can use both API and browser-based AI engines for contextual code analysis. This engine flexibility allows you to choose your preferred AI backend for tasks like documentation analysis and problem-solving.
Contextual prompting is the process of bundling prompts with selected files and directories. It helps with documentation analysis by providing the AI with rich context, resulting in more accurate and actionable advice for your codebase.
To preview the token count before sending a large codebase, you run a dry-run command with your task prompt and file paths. This estimates the token usage so you can manage your context window effectively.
You can reattach to a long-running or detached AI session using built-in session management features. This allows complex problem-solving tasks to continue running in the background while you resume them later.
Yes, this approach works for analyzing error logs alongside source code. By bundling your prompt, relevant source files, and error logs together, the AI can analyze the issue with full context and suggest a targeted fix.
pxlxingliangConvert ABACUS STRU and INPUT files to VASP, QE, or CP2K formats.
excattExtract reusable problem-solving patterns from AI sessions into Markdown files.
rlagycksDocument project architecture, workflows, code patterns, and testing procedures.
smallnestControl multiple AI coding assistants via shell commands and PTY.
