Results for “edge”

You're a developer who has shipped dozens of Firebase projects. You've seen the "easy" path lead to security breaches, runaway costs, and impossible migrations. You know Firebase is powerful, but you also know its sharp edges.

Breaks a plan, spec, or conversation into tracer-bullet tickets that declare their blocking edges, published to the configured tracker. Use when the user says "break this into tickets" or wants a plan decomposed into vertical slices.

Fixes common PyTorch bugs including percentile calculations, LayerNorm for Conv1d, and buffer edge cases in reinforcement learning and neural network code.

Record and analyze post-trade outcomes for trading signals, tracking false positives, missed opportunities, and regime mismatches to generate feedback for signal aggregation and skill improvement.

You're a developer who has shipped dozens of Firebase projects. You've seen the "easy" path lead to security breaches, runaway costs, and impossible migrations. You know Firebase is powerful, but you also know its sharp edges.

Master Go 1.21+ with modern patterns, advanced concurrency, performance optimization, and production-ready microservices. Expert in the latest Go ecosystem including generics, workspaces, and cutting-edge frameworks. Use PROACTIVELY for Go development, architecture design, or performance optimization.

Master Go 1.21+ with modern patterns, advanced concurrency, performance optimization, and production-ready microservices. Expert in the latest Go ecosystem including generics, workspaces, and cutting-edge frameworks. Use PROACTIVELY for Go development, architecture design, or performance optimization.

Design user journeys and task flows for a product concept or MVP. Use when the team needs primary flows, branch conditions, edge cases, onboarding paths, and end-to-end user movement before screen planning or UI implementation.

Write and debug spreadsheet formulas (Excel/Google Sheets), pivot tables, and array formulas; translate between dialects; use when users need working formulas with examples and edge-case checks.

Master Rust 1.75+ with modern async patterns, advanced type system features, and production-ready systems programming. Expert in the latest Rust ecosystem including Tokio, axum, and cutting-edge crates. Use PROACTIVELY for Rust development, performance optimization, or systems programming.

Create a layered CSS progressive blur (top or bottom) using multiple backdrop-filter masks for depth and softness. Use when asked for “progressive blur”, “gradient blur overlay”, or stepped blur masks that fade from an edge of the viewport.

Review whether a product, MVP, or feature is actually ready to launch. Use when the team needs a production-minded check across product quality, UX edge cases, auth and security, observability, support readiness, pricing readiness, and post-launch measurement.

Master Rust 1.75+ with modern async patterns, advanced type system features, and production-ready systems programming. Expert in the latest Rust ecosystem including Tokio, axum, and cutting-edge crates. Use PROACTIVELY for Rust development, performance optimization, or systems programming.

Recommends an inference runtime and memory-related launch flags for LLM/VLM workloads on NVIDIA Jetson devices, based on a live memory audit snapshot.

Code review is time-consuming and inconsistent. Reviewers miss security issues, overlook edge cases, or focus on style over substance. Meanwhile, AI agents catch these mechanical issues instantly but can't judge business logic correctness, UX decisions, or architectural fit.

Apply diagonal-cut corners and chamfered edges to buttons, cards, panels, and container shells. Use when a design needs precise geometric framing, sci-fi UI surfaces, clipped-corner controls, or engineered sharp containers instead of rounded pills or plain rectangles.

Assists with building applications using Neon serverless PostgreSQL. Use when setting up serverless databases, creating branches for development workflows, integrating with edge runtimes, or configuring Prisma/Drizzle with Neon. Trigger words: neon, serverless postgres, database branching, neon serverless driver, pgvector, neon database.

Generates structured Given/When/Then acceptance criteria for a user story or feature slice. Use when translating product requirements into testable scenarios that cover the happy path, edge cases, error states, and non-functional expectations for engineering handoff and QA.

Pass/fail health check across every Mushi Mushi pipeline component — CLI credentials, API reachability, edge functions, BYOK key pool, QA cron. Use when "is mushi working", "mushi health check", "check mushi pipeline", "mushi deploy check", "pipeline not responding", or right after setup.

Prediction market arbitrage & alpha scout. Searches Polymarket and Kalshi for the same event, runs cross-platform arb math (including fees), and ranks opportunities by profitability. Use when asked to "find arb", "scout markets", "find edge", or scan a specific topic across prediction markets.

Relate GitHub issues and PRs — pick the right edge type (blocked-by via GraphQL, parent/sub-issue, Closes/Fixes in PR body, plain mention) and apply it. Use when asked to link, block, depend on, make a subtask of, or close-on-merge.

Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.

Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.

Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.

Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.

Performs a meticulous final pass on frontend work, checking visual alignment, typography, color contrast, interaction states, micro-interactions, content, icons, forms, edge cases, responsiveness, performance, and code quality to elevate work from shipped to polished.

Wire ETL, ingestion, cron, edge-function, and queue jobs correctly. Use for "build a pipeline", "sync X into Y", "nightly aggregation", "cron double-counts", "dedupe", "backfill", "the numbers are wrong after a retry". Bakes in idempotency, atomic writes, data contracts, dead-letter, and observability.

Read-only audit of hosting, database, storage, egress, and serverless spend (Supabase, Vercel, S3/R2, edge). Use when "hosting bill is high", "cut infra costs", or a bill jumps. CI minutes → audit-cicd. Model tokens → plan-llm-cost-guardrails. Consumes test-load numbers.

Patterns for running AI models locally in browsers using WebGPU, Transformers.js, WebLLM, and ONNX Runtime. Zero API costs, full privacy. Use when "on-device AI, browser AI, WebLLM, Transformers.js, WebGPU, edge inference, offline AI, client-side ML, ONNX web, " mentioned.

Design and plan end-to-end Azure IoT and Smart City solutions: requirements, architecture, security, operations, cost, and a phased delivery plan with concrete implementation artifacts.

Runs a comprehensive mobile QA audit covering permissions, deep links, push notifications, offline mode, background/foreground transitions, memory leaks, network conditions, accessibility, and platform edge cases for Flutter, React Native, and native iOS/Android apps.

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

Generates comprehensive unit tests with edge cases, error paths, and boundary values. Auto-detects the test framework, scans for untested functions, adopts existing conventions, runs tests, and self-heals failures in up to 3 iterations while measuring coverage.

Supabase administration, RLS policies, migrations, and schema design. Use for database architecture, Row Level Security, performance tuning, auth integration. Activate on "Supabase", "RLS", "migration", "policy", "schema", "auth.uid()". NOT for Supabase Auth UI configuration (use dashboard), edge functions (use cloudflare-worker-dev), or general SQL without Supabase context.