Results for “pe”

Sync files (skills, packages/core/patterns, agents, specs) from EN canonical repo to PT mirror, inserting "Tradução pendente" blockquote after YAML frontmatter, then open PR.

Write, append, or read desk journal entries. The journal is persistent memory — what survives session boundaries. A good entry has: what was done, current state, next step.

`analysis-agent`/`task-agent`/`review-agent`: use when API/DTO, domain, persistence, event, generated, null/default, or mapper boundaries may leak; skip when models stay isolated.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases.

Autonomous memory system for persistent learning across sessions. Automatically saves architectural decisions, bugfixes, patterns, and insights. Use to recall context from previous work and build institutional knowledge.

Master React Native styling, navigation, and Reanimated animations for cross-platform mobile development. Use when building React Native apps, implementing navigation patterns, or creating performant animations.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Audit and redesign AI-generated feedback for pedagogical quality, timing, and learning impact. Use when building or reviewing automated feedback in digital learning tools.

Implement Python database access with parameterized SQL, transaction scope, connection helpers, and repository seams. Use when editing Postgres queries, repositories, transactions, pooling, or persistence boundaries in Python.

Trigger: web security, XSS mitigation, secure cookies, CSRF, CSP headers, CORS policies. Scope: Frontend and browser-level security boundaries. Boundary: Excludes operating system file storage permissions.

Trigger: database schema, indexing, cache asidePattern, Redis caching, ORM models, Drizzle schema. Scope: Database query tuning, indexing, and data cache designs. Boundary: Excludes user identity validation.

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Cross-stack source code asset audit — classifies every file, detects embedded third-party libraries, and delivers actionable four-level verdicts per module with interactive HTML reports.

Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Master enterprise-grade Scala development with functional programming, distributed systems, and big data processing. Expert in Apache Pekko, Akka, Spark, ZIO/Cats Effect, and reactive architectures.

Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Generating and auditing personal dev environment configs (zsh/tmux/neovim/ghostty) and automating the macOS desktop via AppleScript/JXA (Finder, Mail, Safari). Use for dotfiles or Apple Events.

Analyzes email sequence performance metrics. Evaluates open rates, click rates, reply rates, and conversion by step. Identifies drop-off points, benchmarks against industry averages, and recommends optimizations.

Takes raw leads and enriches them to outreach-ready status. Chains company-researcher, cro-auditor, decision-maker-finder, apollo-lead-finder, email-validator, and personalization-enricher.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Brainstorm 5 unique, memorable product names with rationale aligned to brand values and target audience. Use when naming a new product, rebranding, or exploring product name ideas.

Generate 5 creative, cost-effective marketing ideas with channels, messaging, and engagement rationale. Use when brainstorming marketing campaigns, planning product promotion, or looking for creative marketing tactics.

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Manages direct communication to affected data subjects following a personal data breach under GDPR Article 34 when the breach is likely to result in a high risk to their rights and freedoms. Covers the high risk threshold, required notification content per Art. 34(2), exemptions under Art. 34(3), and breach notification letter templates for five scenarios. Keywords: data subject notification, Article 34, high risk, breach communication, GDPR.

Codify the most recent successful /scrape flow into a permanent browser-skill on disk. Future /scrape calls with the same intent run the codified script in ~200ms instead of re-driving the page. Walks back through the conversation, synthesizes script.ts + script.test.ts + fixture, runs the test in a temp dir, and asks before committing. Use when asked to "skillify", "codify", "save this scrape", or "make this permanent". (gstack)

ByteDance's open-source super agent harness — spawns parallel sub-agents, Docker sandbox execution, persistent long-term memory, modular skills (research/report/slides). Built on LangChain + LangGraph. Triggers on: 'deerflow', 'deer-flow', 'bytedance agent', 'super agent harness', 'LangGraph agent orchestration', 'multi-hour agent tasks', 'spawn sub-agents', 'agent sandbox docker', 'persistent agent memory', 'agent skills system', 'AI research orchestrator', 'long-running agent tasks', 'agent with memory', 'langgraph orchestrator', 'IM channel agent integration'.

Persistent cross-session memory for AI agents. Mem0 stores user preferences, past decisions, domain knowledge, and agent learnings across all sessions, all tools, and all users. Complements planning-with-files (task-level memory) with long-term agent intelligence (CRM + personal knowledge base layer). Use when asked to "remember this", "store preference", "mem0", "long-term memory", "user memory", "agent memory", or when building multi-session agents that need to recall past interactions.