| #!/usr/bin/env python3 | |
| """ | |
| Claude Code token usage analyzer. | |
| Analyzes ~/.claude/projects/ JSONL files for token usage patterns. | |
| """ | |
| import json | |
| import os | |
| import sys | |
| from pathlib import Path |
| #!/usr/bin/env python3 | |
| """Scan for Antic/OneWheel BLE devices and dump all characteristics.""" | |
| import asyncio | |
| from bleak import BleakScanner, BleakClient | |
| OW_SERVICE = "e659f300-ea98-11e3-ac10-0800200c9a66" | |
| async def scan(): | |
| print("Scanning for Antic/OneWheel devices (10 seconds)...") | |
| print("Make sure your Antic is powered on and nearby.\n") |
| name | orchestrating-swarms |
|---|---|
| description | Master multi-agent orchestration using Claude Code's TeammateTool and Task system. Use when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns. |
Master multi-agent orchestration using Claude Code's TeammateTool and Task system.
This is not a proposal. This documents existing but hidden functionality found in Claude Code v2.1.19 binary, plus speculation on how it could be used.
TeammateTool already exists in Claude Code. We extracted this from the compiled binary at ~/.local/share/claude/versions/2.1.19 using strings analysis. The feature is fully implemented but gated behind feature flags (I9() && qFB()).
| name | trmnl-image-generator | ||||
|---|---|---|---|---|---|
| description | Generates TRMNL-compatible e-ink display images. Use when creating images for TRMNL devices, converting images to 1-bit format, or uploading content to e-ink displays. Triggers on "TRMNL", "e-ink", "e-paper", "terminal display". | ||||
| allowed-tools |
|
I am Claude Code, an AI assistant with a comprehensive suite of tools designed for software engineering tasks. My capabilities span from basic file operations to complex web automation, API integrations, and intelligent task management. This report provides an exhaustive analysis of my available tools and how they enable me to assist with development workflows.
| class PromptReader | |
| PROMPTS_DIR = Rails.root.join("app", "prompts") | |
| # Reads the content of a prompt file or Leva::Prompt object and renders it with Liquid templating | |
| # | |
| # @example Read a prompt file without Liquid templating | |
| # PromptReader.read("welcome_message") | |
| # | |
| # @example Read a prompt file with Liquid templating | |
| # PromptReader.read("greeting", name: "John") |
You are an experienced software developer tasked with addressing a GitHub issue. Your goal is to analyze the issue, understand the codebase, and create a comprehensive plan to tackle the task. Follow these steps carefully:
<github_issue> #$ARGUMENTS </github_issue>
Analyze the code thoroughly until you feel you have a solid understanding of the context and requirements.
You are an AI assistant tasked with creating well-structured GitHub issues for feature requests, bug reports, or improvement ideas. Your goal is to turn the provided feature description into a comprehensive GitHub issue that follows best practices and project conventions.
First, you will be given a feature description and a repository URL. Here they are:
<feature_description> #$ARGUMENTS </feature_description>
Follow these steps to complete the task, make a todo list and think ultrahard:
You are an AI assistant helping with the Featurebase post triaging process. Your task is to analyze in-review posts, summarize the issue or request, present options for action, and prepare a brief response based on the decision made. Follow these instructions carefully:
First, review the post data from featurebase-mcp, get the last 20 posts with status "In Review" Like featurebase:list_posts (MCP)(limit: 20, select: "id,title,content,postStatus(name),inReview,date", sortBy: "date:desc")
Next, go one post at a time, create a todo list with all 50 posts to keep track of the triage process.
For each post, provide: