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brute
brute contiene 379 skills recopiladas de general-intelligence-systems, con cobertura ocupacional por repositorio y páginas de detalle dentro del sitio.
Skills en este repositorio
Build, audit, and improve harnesses that make AI coding agents reliable: AGENTS.md/CLAUDE.md instruction files, feature/state tracking, verification gates, scope boundaries, session handoff, memory persistence, context budgets, tool-permission safety, and multi-agent coordination. Use this whenever a coding agent is unreliable across sessions — forgets context, drifts out of scope, claims "done" before tests pass, or starts each session inconsistently — or when creating or assessing AGENTS.md, CLAUDE.md, feature_list.json, init.sh, progress.md, or session-handoff files. Reach for it even if the user never says the word "harness."
Generate a consolidated digest from multiple information sources
Track and report on progress toward defined goals
Review the agent's own recent outputs for quality and accuracy
Generate changelogs from recent commits and merged PRs
Implement features on branches from issue descriptions
Search for and discover community-built Aeon skills
Write long-form articles synthesized from research and analysis
Fetch recent tweets from specific accounts via X.AI API
Scan and summarize recent academic papers from Semantic Scholar
Monitor and summarize activity from tracked subreddits
Aggregate and summarize RSS feed updates
Discovers and ranks tasks from GitHub, GitLab, local files, and custom sources
Coordinate multiple enhancement analyzers in parallel and produce unified report
Unified static analysis — git history, AST symbols, project metadata
Sync documentation with code — find outdated refs, update CHANGELOG, flag stale examples
Autonomously validate that a task is complete and ready to ship — tests, build, and requirement checks
Cross-tool AI consultation — get second opinions from Gemini CLI, Codex CLI, Claude Code, OpenCode, or Copilot CLI
Research any topic online and create comprehensive learning guides with RAG-optimized indexes
Synthesize performance findings into evidence-backed recommendations and decisions
Run sequential performance benchmarks with strict duration rules and baseline management
Clean AI slop from code with certainty-based findings and auto-fixes
Analyze prompts for clarity, structure, examples, and output reliability
Milestone progress tracking and review
Scope deviation detection
Creative ideation with MDA analysis
Design document validation
Statistical analysis and balance verification of game data
Architecture Decision Record documentation
Architectural code review
Technical debt tracking and prioritization
Effort breakdown and estimation
Sprint creation and tracking
Standardized bug documentation
Release pipeline checklist
Auto-generate changelog from git commits
Player-facing release notes generation
Writes, refactors, and evaluates prompts for LLMs generating optimized templates, structured output schemas, evaluation rubrics, and test suites.
Designs production-grade RAG systems with document chunking, embedding generation, vector store configuration, hybrid search pipelines, reranking, and retrieval quality evaluation.
Generates Angular 17+ standalone components, configures advanced routing with lazy loading and guards, implements NgRx state management, applies RxJS patterns, and optimizes bundle performance.