By sickn33
Run structured A/B testing, benchmark LLM agents, manage product analytics, and evaluate AI models with Hugging Face integration. Provides production patterns for LLM apps, context window management, Langfuse observability, KPI dashboards, and product management frameworks.
Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks
Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc. ) into focused tools people will pay for. Not just "ChatGPT but different" - products that solve specific problems with AI.
Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot
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Local, deterministic skill-stack composition for coding agents—from an explicit project profile to a reviewable plan before any target change.
Current release: V15.0.0. This release includes AAS Core under the Agent-First Preview claim for local search, inspection, recommendation, manifest validation, planning, and diagnosis. Apply and recovery remain experimental and outside the supported preview path.
Codex or Claude inspects your project using its own capabilities; AAS does not scan it. The agent sends the local, read-only AAS MCP an explicit project profile. AAS Core evaluates that profile and your policy against a verified local catalog, returns an explainable recommendation, and lets the agent propose aas-stack.json. The aas CLI validates that desired state and creates an immutable per-target plan before any skill changes are made.
Read the AAS Core preview guide →
Project
-> inspected by Codex or Claude (not by AAS)
-> explicit, allowlisted project profile
-> AAS MCP (local stdio, read-only)
-> deterministic AAS Core + verified local catalog
-> recommendation with evidence, exclusions, coverage, and unknowns
-> agent proposes aas-stack.json
-> AAS CLI validate + immutable plan preview
-> human review (optionally in Workbench)
The 1,967+ reusable SKILL.md playbooks, specialized plugins, bundles, workflows, and direct installers remain important. They are the content, curation, distribution, and compatibility layers around AAS Core—not competing primary products.
This is an independent community project. It is not affiliated with, sponsored by, endorsed by, or authorized by Google. Google, Antigravity, Gemini, and related product names are referenced only to describe compatibility and install targets. The GitHub repository is canonical; the hosted catalog and browser-local Workbench are companion discovery and review surfaces, not a hosted control plane.
The agent composes. You control. AAS keeps the stack reproducible.
AAS Core gives the repository one product model:
search_skills, get_skill, recommend_stack, inspect_stack, and diff_stack; it does not install skills, scan source files, call a remote model, or write to the project.aas-stack.json. The manifest pins catalog identity, targets, goals, policy, and exact skill IDs without storing repository source or model reasoning.aas stack validate checks the proposal, while aas stack plan produces an immutable, per-target plan without applying it.npx claudepluginhub sickn33/agentic-awesome-skills --plugin agentic-bundle-aas-ai-product-evaluation-opsEditorial "AAS Agent & MCP Builder" bundle for Claude Code from Agentic Awesome Skills.
Editorial "AAS API Platform Builder" bundle for Claude Code from Agentic Awesome Skills.
Editorial "Security Developer" bundle for Claude Code from Agentic Awesome Skills.
Editorial "DDD & Evented Architecture" bundle for Claude Code from Agentic Awesome Skills.
Editorial "Mobile Developer" bundle for Claude Code from Agentic Awesome Skills.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
26 Agent Skills (several with runnable, unit-tested scripts) for building, evaluating, securing, and monitoring reliable LLM & AI-agent apps.
Editorial "LLM Application Developer" bundle for Claude Code from Agentic Awesome Skills.
Benchmark, evaluate, and optimize skills to ensure reliable performance across all LLMs
OSS Claude Code config: agents, skills, and hooks for professional AI-assisted development workflows
This skill should be used when the user asks to "evaluate agent performance", "build test framework", "measure agent quality", "create evaluation rubrics", or mentions LLM-as-judge, multi-dimensional evaluation, agent testing, or quality gates for agent pipelines.