Results for “engineering”

Routes reverse engineering, exploitation, penetration testing, malware, mobile, firmware, browser automation, documentation, and security tasks to the appropriate specialist skill. Use when a task spans modules or the correct reverse-skill entrypoint is unclear.

Analyze engineering delivery throughput, hiring funnel health, team structure, and production discipline for startup VPEs and founders.

Guides building production-grade AI features with LLM integration patterns, RAG architecture, prompt engineering, and cost optimization.

Designs autonomous agent harnesses with locked evaluators, editable surfaces, durable logging, novelty gates, pruning, rollback, and human approval boundaries.

Specialized CTF workflow for reverse engineering, malware analysis, DFIR, firmware, pwnable, and native exploit challenges under sandbox assumptions.

Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.

Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.

Applies four disciplined mindsets—audit, debt, help, review—to cut complexity, track deferrals, surface reference, and catch over-engineering in codebases.

Create pull requests following Sentry's engineering practices.

Identify cryptographic algorithms, key generation flaws, and potential decryption opportunities in ransomware samples using static and dynamic analysis.

Authorized reverse engineering of custom binary protocols, Protobuf/gRPC, WebSocket frames, and PCAP-driven protocol recovery with structured workflow and tooling.

25 advanced engineering agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Agent design, RAG, MCP servers, CI/CD, database design, observability, security auditing, release management, platform ops.

25 advanced engineering agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Agent design, RAG, MCP servers, CI/CD, database design, observability, security auditing, release management, platform ops.

Routes engineering design, delivery, reliability, security, operations, and maintenance prompts to a router skill backed by specialist guidance for multiple AI coding agents.

Injects intentional failures to test system resilience using Litmus and Chaos Monkey, with a prerequisite to validate detection before running experiments.

Discovers and applies curated prompts from the prompts.chat collection to optimize AI interactions, prompt engineering, and workflow integration.

Create pull requests following Sentry's engineering practices.

Cardinality and model family jointly determine the encoding.

A library of reusable, production-tested prompt engineering patterns for building AI-powered features. Use when designing system prompts for apps, building AI pipelines, selecting the right prompting technique for a use case, or reviewing prompts for common failure modes. Complements the prompt-engineering skill (which covers the optimization framework); this skill covers the pattern library itself.

Dynamically instrument iOS apps with Frida to trace methods, extract secrets, and bypass security controls during authorized penetration testing.

Analyze .NET malware by decompiling and debugging assemblies with dnSpy, deobfuscating with de4dot, and extracting C2 configurations and IOCs.

Guides authorized macOS and Mach-O reverse engineering covering codesign analysis, Objective-C/Swift symbol recovery, endpoint security surfaces, and Apple platform malware analysis.

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Use this skill for context gathering, file triage, source maps, assumptions, task framing, prompt hygiene. Trigger when the task involves programming work related to Context Engineering, production implementation, audits, debugging, strategy, or validation.

Design leakage-safe feature engineering strategies for tabular/time-series datasets. Use when: (1) preparing model-ready features, (2) selecting transformations and encodings, (3) documenting feature lineage. NOT for: model serving or infra provisioning.

Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness, providing detailed improvement recommendations with frameworks, testing methodologies, and educational content.

Enforces production-grade Python and AI engineering standards for FastAPI, LangChain/LangGraph, RAG pipelines, and LLM integrations, covering type safety, error handling, testing, and security.

Expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems. Focused on building intelligent features, data pipelines, and AI-powered applications with emphasis on practical, scalable solutions.

Guides authorized reverse engineering of Chrome and Firefox browser extensions, covering manifest analysis, background workers, and credential or traffic logic recovery.

Orchestrate CTF, exploit, reverse engineering, DFIR, and other challenge types by treating presented targets as sandbox-internal, tracing minimal paths, and routing to specialized downstream skills.

Audits local content archives and builds a reusable, structured content engineering project with content units, topic maps, and assembly drafts for ongoing reuse.

Generates task-oriented technical documentation with progressive disclosure for READMEs, API docs, architecture docs, and security reports after reverse engineering, penetration testing, or CTF tasks.

Performs free, open-source reverse engineering with Ghidra using headless or GUI modes, including decompilation, cross-references, and optional MCP workflows when IDA is unavailable.

Pragmatic coding standards - concise, direct, no over-engineering, no unnecessary comments

Pragmatic coding standards - concise, direct, no over-engineering, no unnecessary comments

Pragmatic coding standards - concise, direct, no over-engineering, no unnecessary comments