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Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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 | benchmark |
| description | Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives. |
| origin | Multiversal |
Measures real browser metrics via browser MCP:
1. Navigate to each target URL
2. Measure Core Web Vitals:
- LCP (Largest Contentful Paint) — target < 2.5s
- CLS (Cumulative Layout Shift) — target < 0.1
- INP (Interaction to Next Paint) — target < 200ms
- FCP (First Contentful Paint) — target < 1.8s
- TTFB (Time to First Byte) — target < 800ms
3. Measure resource sizes:
- Total page weight (target < 1MB)
- JS bundle size (target < 200KB gzipped)
- CSS size
- Image weight
- Third-party script weight
4. Count network requests
5. Check for render-blocking resources
Benchmarks API endpoints:
1. Hit each endpoint 100 times
2. Measure: p50, p95, p99 latency
3. Track: response size, status codes
4. Test under load: 10 concurrent requests
5. Compare against SLA targets
Measures development feedback loop:
1. Cold build time
2. Hot reload time (HMR)
3. Test suite duration
4. TypeScript check time
5. Lint time
6. Docker build time
Run before and after a change to measure impact:
/benchmark baseline # saves current metrics
# ... make changes ...
/benchmark compare # compares against baseline
Output:
| Metric | Before | After | Delta | Verdict |
|--------|--------|-------|-------|---------|
| LCP | 1.2s | 1.4s | +200ms | WARNING: WARN |
| Bundle | 180KB | 175KB | -5KB | ✓ BETTER |
| Build | 12s | 14s | +2s | WARNING: WARN |
Stores baselines in .multiversal/benchmarks/ as JSON. Git-tracked so the team shares baselines.
/benchmark compare on every PR/canary-watch for post-deploy monitoring/browser-qa for full pre-ship checklist