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Engineering operating model for teams where AI agents generate a large share of implementation output.
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Deep research what people actually say about any topic across social media. Pulls posts and engagement from Reddit, X, YouTube, TikTok, Hacker News, Polymarket, GitHub, and the web.
Manus-style persistent file-based planning for AI coding agents: keeps task_plan.md, findings.md, and progress.md on disk so work survives context loss and /clear. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring 5+ tool calls. Supports automatic session recovery after /clear.
Comprehensive CTF and security testing skill covering web exploitation (SQLi, XSS, SSTI, SSRF, JWT, prototype pollution, file upload RCE), binary exploitation (buffer overflow, ROP, heap, format string, kernel, seccomp bypass), cryptography (RSA, AES, ECC, PRNG, ZKP, lattice), reverse engineering (ELF/PE, VMs, obfuscation, WASM, game clients), forensics (disk images, memory dumps, PCAP, stego, event logs, side-channel), OSINT (social media, geolocation, DNS, public records), malware analysis (C2 traffic, packers, .NET, shellcode), AI/ML security (adversarial examples, prompt injection, model extraction), and misc challenges (jails, encodings, RF/SDR, esoteric languages, game theory). Use when the user presents a CTF challenge, security assessment, penetration test, or needs offensive security techniques. Routes to specialized sub-skills per category.
Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA standards. Use this skill to generate semantic ARIA for Web and accessibility traits for Web and Native platforms (iOS/Android).
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature.
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
| name | ai-first-engineering |
| description | Engineering operating model for teams where AI agents generate a large share of implementation output. |
| origin | Multiversal |
Use this skill when designing process, reviews, and architecture for teams shipping with AI-assisted code generation.
Prefer architectures that are agent-friendly:
Avoid implicit behavior spread across hidden conventions.
Review for:
Minimize time spent on style issues already covered by automation.
Strong AI-first engineers:
Raise testing bar for generated code:
