Skill UI

This tool provides a preferred, end-to-end method for modifying code in real-world repositories. It simulates a professional development workflow: cloning the target repository, running changes on a dedicated task branch, and critically, verifying the implementation through an independent red $\to$ green $\to$ regression test loop. It is designed for fixing GitHub issues, implementing new features, or adding functions to existing codebases, ensuring the structural integrity and correctness of the changes.

A specialized downstream skill for complex CTF challenges, focusing on cryptographic analysis, steganography, and mobile system forensics. It guides the user through recovering complex data chains, such as decoding blobs, tracing cryptographic transforms (keys, IVs, nonces), inspecting hidden media payloads, and analyzing mobile trust boundaries (e.g., APK/IPA signing logic, local storage, SSL pinning). This skill is designed to be used only after a main sandbox orchestrator has established the operational context.

Converts raw OpenMed NER outputs (text spans) into structured, standards-compliant FHIR R4 resources (e.g., Condition, Observation, MedicationStatement). It provides mechanical helpers for building CodeableConcepts using major coding systems like SNOMED, LOINC, and ICD-10, ensuring high interoperability with EHR systems and registries.

This skill facilitates the crucial grounding step for clinical coding by interacting with external FHIR terminology servers. It supports core operations such as `$validate-code`, `$expand` (for ValueSets), `$lookup`, and `$translate` (ConceptMaps). It ensures that raw text spans are converted into validated, coded `CodeableConcept` resources using industry standards like SNOMED CT, LOINC, and ICD-10, maintaining interoperability.

Learn how to implement data-driven design in Godot using custom Resource classes. This guide covers defining typed data structures (like items, stats, or configs) using `class_name` and `@export`, creating editable `.tres`/`.res` assets, referencing them in scripts, and managing runtime saving and loading via `ResourceLoader` and `ResourceSaver`. Essential for building scalable, data-heavy games without hardcoding values.

Hivemind is an advanced orchestration layer designed to manage complex tasks using a parallel swarm of external, low-cost workers. It simulates a structured team workflow (scout, coder, tester) to decompose large problems, execute subtasks in isolated worktrees, synthesize aggregated results, and perform consensus reviews. This dramatically reduces token costs and enhances reliability for large-scale coding, research, and testing tasks.

A guided tool to manage and progress through a structured, multi-module technical learning curriculum. It tracks prerequisites, requires recording concrete evidence artifacts for completion, and implements spaced repetition reviews to ensure long-term knowledge retention in coding practices.

This guide details the modern, action-based approach to handling player input in Unity using the Input System package. It covers setting up Input Actions, Action Maps, utilizing the PlayerInput component, and advanced coding patterns (callbacks, polling). Use this when building robust, device-agnostic gameplay controls for modern Unity projects.

This skill applies Steve McConnell's construction rules to software development tasks. It covers routine design, variable usage, class design, control flow, defensive programming, coding standards, and testing. Use it for disciplined software construction to ensure high-quality code implementation.

This skill allows delegating self-contained coding tasks to assistants like Codex or Claude Code via non-interactive background processes. It supports feature development, code review, and refactoring. Use OpenSquilla's exec_command and process tools to manage sessions, poll output, and send inputs without blocking the main workflow. Ideal for tasks requiring substantial file exploration.

This skill orchestrates coding tasks end-to-end from issue intake to acceptance verification. It operates as a state machine ensuring minimal human intervention by enforcing strict acceptance criteria and verification steps. It handles implementation, review, deployment, and final evidence-backed completion, escalating issues when DoD fails or permissions are missing.

This skill creates, revises, or audits AGENTS.md files based on repository evidence and verified commands. It ensures coding agents understand the workflow without rediscovering it, preserving maintainer intent. It builds evidence maps from manifests and CI, scopes instructions correctly, and validates changes before handoff to prevent mistakes.