Explorar skills

Cross-project pattern layer for the Peec AI growth loop. After any Peec skill completes (or after peec-report closes a cycle), extract 1–3 concrete patterns from the output and persist them to SkillMind via mcp__skillmind__add_pattern / remember. On the next orchestrator run, recall matching patterns and pass them in as priors — so lessons learned on project A inform decisions on project B. Use wh

Workflow for authoring or revising any Claude Code configuration — skills (SKILL.md), CLAUDE.md guardrails, settings.json (permissions, hooks, env, model), sub-agents, slash commands, MCP server entries, plugins, and the install or sync scripts around them. Operates from working knowledge of LLMs, agent harnesses, and context engineering so the configuration it produces leverages how Claude Code a

Turns Peec AI's get_actions recommendations + forum/UGC discovery into a prioritized outreach pipeline with pitch templates, contact extraction, status tracking, and success measurement. Goes beyond "here is a list of domains" — produces ready-to-send pitches, covers Reddit / Gutefrage / editorial / owned quadrants, and tracks citation gains week over week. Use when a Peec project has active compe

Audit a government or public services system for compliance, fraud risk, and security — chains benefits processing review, fraud detection analysis, regulatory compliance check (ADA, Section 508, FISMA, Privacy Act), and PII-focused security audit. Use for benefits platforms, eligibility engines, enrollment portals, or inter-agency data systems.

Use continuously during long tasks. Teaches how to read less, output less, and keep the LLM context window lean and fast.

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show

Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

Helps marketers optimize content and brand presence for AI-powered search engines, LLM citations, and answer engine visibility (AEO). Trigger when a user asks about ranking in ChatGPT, Perplexity, or Google AI Overviews; optimizing content for LLM citation; tracking brand mentions in AI search; or adapting SEO strategy for an AI-driven search landscape.

Delegate coding tasks to Blackbox AI CLI agent. Multi-model agent with built-in judge that runs tasks through multiple LLMs and picks the best result. Requires the blackbox CLI and a Blackbox AI API key.

Stop your AI from making things up. Use when your AI hallucinates, fabricates facts, is not grounded in real data, does not cite sources, makes unsupported claims, or you need to verify AI responses against source material. Also use when your LLM makes up facts, responses are disconnected from the input, or outputs are not grounded in source documents. Covers citation enforcement, faithfulness ver

Tier-1 strategy-consultant analysis with parallel sub-agents — one per framework (MECE, Issue Trees, Hypothesis-Driven, Pareto, So What?) — synthesized into a single whiteboard-style response. Use for high-stakes or complex problems. Requires Task/Agent tool.

Tier-1 strategy-consultant analysis tailored for SaaS / B2B software problems — ARR, churn, pipeline, NRR, product-led growth. Same five frameworks as the generic master, but with SaaS-aware MECE defaults, industry vocabulary, and common root-cause patterns. Use for SaaS-specific problems; for cross-industry framing use `strategy-consultant`.