parallel▌

Skill by ara.so — Daily 2026 Skills collection.

Three-layer parallel analysis of Rust questions across language mechanics, design patterns, and domain constraints. \n \n Executes in parallel agent mode (when layer analyzer files exist) or sequential inline mode, synthesizing results into domain-correct architectural solutions \n Analyzes ownership, borrowing, lifetimes, and error codes at the language layer; evaluates smart pointers, interior mutability, and design trade-offs at the design layer; and applies domain-specific requirements (FinT

Bulk enrichment of company, people, or product data with web-sourced fields like CEO names, funding, and contact info. \n \n Accepts inline JSON data or CSV files; outputs enriched results to CSV \n Runs asynchronously with progress tracking via monitoring URL and polling commands \n Requires parallel-cli tool and internet access; handles large datasets with configurable timeouts \n Supports flexible field requests through natural language intent descriptions (e.g., \"CEO name and founding year\

Extract content from multiple URLs in parallel, token-efficiently. \n \n Handles webpages, articles, PDFs, and JavaScript-heavy sites with a single command \n Runs in a forked context to minimize token overhead compared to built-in WebFetch \n Supports batch extraction of multiple URLs with optional focus objectives \n Requires parallel-cli installation and authentication; outputs extracted content as markdown to a local file for follow-up queries \n

Fast web search for current information, research, and fact-finding across the internet. \n \n Executes single objective-based queries or multiple keyword searches in parallel, returning up to 10 results with excerpts and metadata \n Supports time-sensitive filtering via --after-date and domain-specific searches with --include-domains \n Outputs structured JSON with titles, URLs, publish dates, and excerpts for easy parsing and follow-up queries \n Requires inline citations for every claim using

Exhaustive research with configurable depth, latency, and cost trade-offs for complex topics. \n \n Three processor tiers (pro-fast, ultra-fast, ultra) ranging from 30 seconds to 25 minutes, with cost scaling from 1x to 3x baseline \n Asynchronous execution with polling: kick off research instantly, monitor progress via URL, retrieve results when ready without blocking \n Outputs formatted markdown report and JSON metadata; executive summary printed to stdout for quick overview \n Designed for e

Orchestrates parallel execution of dependent tasks across multiple subagents using markdown plan files. \n \n Parses task dependencies from plan markdown and launches unblocked tasks in parallel waves, ensuring correct execution order \n Provides subagents with structured task context including descriptions, acceptance criteria, validation steps, and dependency information \n Enforces test-driven development (RED phase first) with fallback to documented non-testable verification for tasks that d

Orchestrate parallel development tasks with dependency management and test-driven validation. \n \n Parses markdown plan files to extract task definitions, dependencies, and acceptance criteria, then launches unblocked tasks in parallel waves using Sparky subagents \n Enforces test-driven development (RED phase first) for testable tasks, with fallback to documented non-testable verification (manual, static, or runtime checks) \n Manages task dependencies automatically, blocking tasks until their

Delegate independent tasks to specialized agents working concurrently with isolated context. \n \n Dispatch one agent per problem domain when facing 3+ unrelated failures across different test files or subsystems \n Each agent receives focused scope, clear goal, and constraints—no inherited session context or history \n Agents work in parallel on independent investigations, then you review summaries and integrate non-conflicting fixes \n Best for timing issues, subsystem bugs, and exploratory fi

Systematic debugging framework using competing hypotheses to identify root causes across multiple failure categories. \n \n Generates hypotheses across six failure mode categories: logic errors, data issues, state problems, integration failures, resource issues, and environment mismatches \n Establishes evidence standards with citation requirements (file:line references) and confidence levels (high/medium/low) to avoid confirmation bias \n Supports parallel agent investigation with structured re