Skill UI

Context engineering optimizes AI agent performance by curating information hierarchy. It involves managing rules files, specs, source code, and conversation history to prevent hallucinations and maintain focus. Use this skill when starting new sessions, switching tasks, or improving agent output quality in development projects.

This tool opens the dedicated Context Mode Insight dashboard in your default browser. Insight serves as the central analytics layer for AI-assisted engineering teams, providing crucial metrics such as per-engineer productive rates, identifying retry waste, detecting blockers, and offering role-narrowed performance views. It is designed to be the single source of truth for comprehensive team performance monitoring and sign-in details.

A specialized prompt engineering assistant that optimizes and generates production-ready prompts for various AI tools, including LLMs, Midjourney, and coding agents. It enforces strict rules, extracts detailed intent (Task, Context, Audience, etc.), and outputs optimized, single-block prompts, ensuring zero wasted tokens and professional quality output.

This skill generates a formal Architecture Decision Record (ADR) following industry-standard formats. It guides users through documenting the full rationale behind significant technical choices, ensuring that the context, alternatives considered, chosen option, and resulting trade-offs are all captured. Ideal for maintaining architectural integrity and knowledge transfer within engineering teams.

This skill scaffolds the necessary configurations for advanced engineering workflows. It guides the user through setting up the issue tracking system (GitHub, GitLab, or local markdown), defining the canonical triage label vocabulary, and establishing the domain documentation context (single or multi-context). It must be run once to initialize the repository for subsequent agent-driven development skills.

This skill automatically rewrites raw commit messages to adhere to professional engineering standards. It strips out common 'AI/marketing slop' phrases (like 'robust implementation' or 'leverage') while strictly maintaining the Conventional Commits format. It ensures subjects are concise, bodies provide necessary context (why, breaking changes), and the tone is direct and actionable, making your project history clean and readable.

Turns a one-line goal into a step-by-step build plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief, plus review gates, dependency graphs, parallel step detection, and a plan-change protocol.

This skill helps transform vague product or engineering ideas into structured, verifiable acceptance criteria (AC). It is crucial for high-impact changes involving security, data migration, or external APIs. By inspecting existing context and asking minimal, targeted questions, it produces clear, observable criteria, significantly de-risking the development process and ensuring handoff readiness.

This skill helps optimize and verify latency-sensitive systems such as real-time dashboards, market data feeds, and streaming agents. It involves tracking p50/p95/p99 latency, freshness age, and queue depth. You will map hot paths and run live readbacks to ensure performance. Use this when throughput or data freshness matters in engineering contexts.

A comprehensive guide for authors targeting the International Conference on Knowledge Capture (K-CAP). It assists in positioning the manuscript, re-framing the contribution, and verifying the evidence bar. The tool emphasizes that the work must focus on knowledge acquisition, engineering, and operationalizing structured knowledge systems, rather than merely using them in a general context.

This skill provides a CLI-based workflow for Android APK reverse engineering. It covers APK unpacking, Java decompilation, smali modification, repackaging, Frida dynamic hooking, and optional native .so analysis. It leverages local tools such as jadx, apktool, frida, adb, ida-reverse, and radare2. Useful for analyzing login, signature, security, root detection, or certificate verification logic in Android apps, always in an authorized context.

This toolkit provides six production-ready AI engineering workflows including prompt evaluation, context budget planning, RAG pipeline design, agent security audits, evaluation harness building, and product sense coaching. It transforms AI coding assistants into senior engineering partners with structured methodologies, quantitative scoring, and checklists for consistent results in LLM system optimization and security.