Explorar skills

End-to-end Peec AI project setup — competitor discovery from real AI chats, customer-journey prompt design across Awareness → Consideration → Decision → Retention, topic/tag taxonomy, GSC-based keyword mapping, forum pain-point mining (Reddit, Gutefrage, t3n, OMR), and a categorized executable backlog. Use when the user wants to set up, restructure, or audit a Peec AI project for their own brand o

Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abl

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

Autonomous SEO + AI-search optimization agent for 2026. Audits and fixes technical SEO, on-page metadata, structured data, Core Web Vitals (LCP/INP/CLS), llms.txt, Generative Engine Optimization (GEO) for ChatGPT/Perplexity/AI Overviews, E-E-A-T author signals, programmatic SEO for catalog sites, and AI-crawler config. Reports findings + applies fixes.

Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost controls.

Provision free LLM API keys from 20+ providers. Health-checks existing keys, opens signup pages, validates new keys, and saves them to your project.

Content-intelligence workflow that turns a Peec AI visibility gap into a publish-ready content brief. Combines Peec (prompt visibility, source URLs, scraped chat responses), Visibly AI (backlinks, onpage, keywords, GSC), and Reddit / forum mining. Uses Query Fan-Out to expand one prompt into 5–8 sub-queries and scores competitor URLs for attackability. Use when the user wants to find content oppor

Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost controls.

Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost controls.

Jailbreak API-served LLMs using G0DM0D3 techniques — Parseltongue input obfuscation (33 techniques), GODMODE CLASSIC system prompt templates, ULTRAPLINIAN multi-model racing, encoding escalation, and Zorro-native prefill/system prompt integration. Use when a user wants to bypass safety filters, jailbreak a model, or red-team model robustness.

Generative Engine Optimization (GEO) / Answer Engine Optimization (AEO) — make a site citable by ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews. Generates llms.txt and llms-full.txt, fills schema.org coverage gaps (Organization, Person/Author, Article, Product, FAQPage, HowTo, BreadcrumbList), audits entity density (target 15+ recognized entities per pillar page → 4.8x AI Overview se

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.