Skill UI

This skill parses raw, verbose Simplicité log files from TXT format, filtering out noise and extracting only essential fields (e.g., timestamp, level, body). By converting logs into structured JSON, it significantly reduces the token size required for AI context optimization, making troubleshooting efficient and reliable.

This skill demonstrates OpenCode's compatibility with MDA-compiled SKILL.md files. It verifies that the loader correctly parses crucial frontmatter fields (like name, description, and metadata) after the source has gone through the MDA compilation process, ensuring robust skill integration and data integrity.

This utility reads structured JSON outputs from multi-search-engine APIs (e.g., Google Scholar, arXiv) provided via standard input. It converts these normalized records into a clean, minimal BibTeX format, suitable for academic papers. It intelligently preserves crucial metadata like DOI, authors, and years while automatically handling data sanitization, deduplication of DOIs, and protecting complex fields like corporate names.

A robust toolkit for deterministic, structural PDF manipulation. This skill allows users to perform programmatic tasks such as extracting text and tables from reports, merging multiple PDF files, splitting documents by page ranges, filling out form fields, or generating entirely new PDFs from structured data (e.g., JSON). It is designed for explicit, byte-level operations, distinguishing it from LLM-based natural language rewriting tools.

This skill serves as a comprehensive pre-flight check to ensure a brand profile is complete and all linked credentials/connectors are reachable. It validates required identity fields, voice/audience completeness, and live connector connectivity, without exposing any sensitive credential values. Use it after onboarding, key rotation, or profile edits to guarantee production readiness.

This guide teaches researchers how to rigorously assess a topic's geographic significance and determine its optimal fit within the Annals of the American Association of Geographers (AAG). It implements a two-part fit test—ensuring the argument is fundamentally geographic (space/place/scale are load-bearing) and possesses sufficient reach to interest neighboring subfields. It guides authors in choosing the correct area (e.g., Human, Physical, Methods) and appropriate journal venue (Annals vs. specialty). Use this when defining a project scope, overcoming reviewer critiques, or deciding wher...

This guide provides comprehensive strategies for writing the Related Work section for submissions to AAAI. It teaches authors how to robustly position their paper's novelty by distinguishing contributions from both established archival literature and contemporaneous non-archival work (like arXiv papers). It also addresses specific reviewer expectations across various AI subfields (NeurIPS, ICML, ICLR) and outlines clear structural templates for maximum impact.

This comprehensive skill guides authors through evaluating whether their chemistry manuscript meets the high standards for ACS Central Science. It assesses the work's central importance, broad scientific reach, and technical rigor across chemical subfields and adjacent disciplines (e.g., biology, materials, energy). It covers journal positioning, required evidence standards, structural guidelines, and reframing the narrative for maximum impact.

This tool guides authors on whether their manuscript fits the scope of Advanced Functional Materials. It emphasizes the critical structure-function-application chain, covering highly advanced fields such as optoelectronics, biomedical devices, and soft robotics. It provides criteria for methodology, required evidence, and journal-specific house style.

A comprehensive guide on positioning academic manuscripts within the broader field conversation, specifically for general education-research journals. It teaches researchers how to move beyond niche citation clusters to establish a field-wide gap, articulate the stakes, and connect the study to adjacent subfields, ensuring the contribution is broadly relevant.

This guide provides comprehensive best practices for ensuring scientific reproducibility in technical papers, particularly for electronics and information processing fields. It covers the critical pillars of documentation: environment setup, data sourcing and preprocessing, full algorithmic disclosure (including pseudo-code and hyperparameters), and robust statistical evaluation. By adopting these standards, authors can transform their work from potentially unverifiable results into credible, verifiable scientific contributions suitable for top-tier journals.

This skill is designed to polish and refine academic manuscripts intended for the American Historical Review (AHR). It focuses on enhancing the clarity, narrative flow, and authoritative voice of the prose, ensuring the work is accessible and engaging for historians across all subfields. It tightens the overall readability, minimizes unnecessary jargon, and helps frame evidence without altering the core arguments or academic content.