# Ai Skill > Agent skill for AI Systems — build model-powered behavior: agents, retrieval, evals, and guardrails - **Type:** MCP server - **Install:** `agentstack add mcp-gabros20-ai-skill` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [gabros20](https://agentstack.voostack.com/s/gabros20) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [gabros20](https://github.com/gabros20) - **Source:** https://github.com/gabros20/ai-skill - **Website:** https://aiengskill.vercel.app ## Install ```sh agentstack add mcp-gabros20-ai-skill ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # ai — AI Systems **Visual guide:** [aiengskill.vercel.app](https://aiengskill.vercel.app) **Family hub:** [digital-product-skill.vercel.app](https://digital-product-skill.vercel.app) — the 15-skill Digital Product family and its composer. Build **the model-powered behavior of a product** — model selection and routing, the prompt/context harness, structured outputs, tool and MCP consumption, the agent loop, retrieval/RAG, memory, evals, guardrails, and AI cost/observability — across chat, autonomous-agent, RAG, batch, and multi-agent systems, in any provider or language. `ai` is the **intelligence layer of the Build stage** of the [Digital Product Skills](https://github.com/gabros20) family, peer of `frontend` and `backend`: an agent skill for building the part of a product whose core uncertainty or capability comes from a model. It sits between `architecture` (which decides system boundaries, the contract shape, NFR budgets, and the threat model) and `operate`/`quality` (which deploy, monitor, and independently verify what gets built). It owns the **cognition** — the running model-powered behavior and the decisions behind it — not the substrate it runs on. It is a **provider-, model-, and language-agnostic router grounded in the current stack, not a single-vendor SDK tutorial and not a system-design tool.** ## What it does A **faceted router**: pick one primary job × at most one base deployment-surface (the multi-agent overlay stacks additively on top). **Primary jobs** — model selection & routing · prompt & context engineering · structured outputs · tool & MCP integration · agent construction · retrieval & RAG · memory · **evaluation ⭐** · guardrails & safety · observability & cost. **Surface overlays** — chat assistant (default) · autonomous agent · RAG app · batch & pipeline · **multi-agent** (additive). The base surface reshapes how every job applies for that deployment shape; `surface-multi-agent` stacks on top of whichever base surface applies rather than replacing it. **The differentiator:** three things most AI material skips. **Evaluation is the flagship** — not an afterthought: error-analysis-first, LLM-as-judge via critique-shadowing, jury and role-binding, offline/online/regression/safety suites, and `pass^k` for reliability. The through-line is **verify-and-subtract, not just assemble** — prune the harness, budget tokens, decompose the judge, distrust benchmarks, prove the retrieval, and decide when *not* to multi-agent (the field is loud on adding and near-silent on pruning). And a distinctive value-add: **token/cost economics + model and tooling licensing** — the per-model cost math (routing cascade → cache → batch → effort), plus "open ≠ open-source" (Llama's restricted Community License vs MIT/Apache families) and the source-available tooling cluster (BSL/SSPL/ELv2/open-core: Restate, Inngest, Phoenix, Langfuse, litellm). Running through all of it is the flagship invariant — **retrieval-first / anti-staleness**: the model/SDK/price/spec layer changes monthly, so every model name, price, version, and spec revision is date-stamped and re-verified against the live source, never frozen. The `handoff.yaml` even carries a `volatile_facts` block so a stale value can't leak downstream. Full attribution in [SOURCES.md](skills/ai/SOURCES.md). ## Install ```bash npx skills add gabros20/ai-skill -g -y ``` Or use it in Codex with `$ai`. ## Examples ```text $ai build an agent that answers questions over our docs, then prove it works with evals $ai our token bill is too high — set up model routing with a cheap tier and honest fallback $ai add retrieval with chunking, hybrid search, and reranking, grounded with citations $ai write an LLM-as-a-judge and a regression suite for our support assistant $ai our context is bloated and slow — prune the harness before we add more ``` ## Route by job | User intent | Reads | Contribution | |---|---|---| | Pick/route a model, set a cost/latency/effort budget, add fallback, decide open-vs-closed / self-host | [model-selection-and-routing](skills/ai/references/model-selection-and-routing.md) | Price-per-intelligence pick, routing cascade (#1 cost lever), degrade-honestly fallback, "open ≠ open-source" license read | | Engineer the prompt/context harness, or prune an over-grown one | [prompt-and-context-engineering](skills/ai/references/prompt-and-context-engineering.md) | Whole-token-budget curation, Model/Harness/Agent framing, long-horizon levers, harness-hygiene / prune-before-add | | Get typed/structured output or conformant tool arguments | [structured-outputs](skills/ai/references/structured-outputs.md) | Schema-first contract, constrained-decode vs reask/retry vs provider-native, tool-arg conformance | | Design/consume tools or integrate MCP; decide direct-API vs CLI vs MCP | [tool-and-mcp-integration](skills/ai/references/tool-and-mcp-integration.md) | Tool contract (typed + validate-retry), MCP anatomy + build chain, direct/CLI/MCP decision | | Build an agent: the loop, termination, multi-agent shape, durability, framework pick | [agent-construction](skills/ai/references/agent-construction.md) | Workflow-vs-agent shape, four multi-agent shapes + when-NOT, durable-execution decision + license, framework pick with what-NOT | | Add retrieval/RAG behavior — chunking, hybrid, rerank, agentic retrieval, vector-store choice | [retrieval-and-rag](skills/ai/references/retrieval-and-rag.md) | 5-stage pipeline (contextual/late → hybrid+RRF → rerank → agentic decomp → eval separately), pgvector-default decision | | Give the system durable memory across turns/sessions | [memory](skills/ai/references/memory.md) | Short-vs-long-term split, four philosophies as options, benchmark-subordinated recall eval | | Prove the system works — dataset, aligned judge, harness, CI gate ⭐ | [evaluation](skills/ai/references/evaluation.md) | Error-analysis-first taxonomy, Critique-Shadowing judge + jury, three harness shapes, offline/online/regression/safety, pass^k | | Add guardrails/safety — moderation, rails, validators, code-exec guards, approval gates, red-team | [guardrails-and-safety](skills/ai/references/guardrails-and-safety.md) | Uniform 5-way controls table + self-audit, prompt self-verification, validate-the-guardrail-like-a-judge | | Instrument AI cost and behavior — traces, token/cost/latency/cache, online-eval monitors | [observability-and-cost](skills/ai/references/observability-and-cost.md) | OTel-GenAI `gen_ai.*` semconv, cache-as-architecture, cost-lever ordering, license-pattern read | **Surface overlay (add at most one base; the multi-agent overlay stacks additively):** | System type | Reads | Reshapes | |---|---|---| | Interactive assistant — streaming, conversational, human-in-loop (default) | [surface-chat-assistant](skills/ai/references/surface-chat-assistant.md) | Single-call-before-loop default, session memory, online-eval fit, lighter guardrail posture | | Autonomous agent — long-horizon loop, runs unattended | [surface-autonomous-agent](skills/ai/references/surface-autonomous-agent.md) | Compaction/JIT/note-taking as structural, harness-hygiene on model upgrades, approval-gate default, durable-execution border, pass^k | | RAG / knowledge app — retrieval-centric, grounded | [surface-rag-app](skills/ai/references/surface-rag-app.md) | retrieval-and-rag as spine, citations as a schema contract, cite-or-retract guardrail, index-staleness monitored | | Batch / pipeline — offline classify/extract at scale | [surface-batch-and-pipeline](skills/ai/references/surface-batch-and-pipeline.md) | Cheap-tier routing default, cost-lever ordering, schema-heavy output, offline/regression eval only | | **Multi-agent** — orchestrated specialists, stacked additively on the base surface | [surface-multi-agent](skills/ai/references/surface-multi-agent.md) | **Orchestrator + tool-scoped specialists (not swarms), the honest when-NOT-to-multi-agent (~15× tokens), four shapes, per-base composition** | Plus [handoff](skills/ai/references/handoff.md) — the `handoff.yaml` companion (with `volatile_facts`) to `quality`, `operate`, `backend`, `frontend`, and `data` when downstream work is expected. ## Outputs A running model-powered behavior plus the decision record behind it: a model/routing choice, a prompt/context harness, structured-output and tool contracts, an agent loop, a retrieval pipeline, a memory layer, an eval suite (dataset + aligned judge + CI gate), a uniform guardrails controls table, and OTel-GenAI telemetry — plus fillable checklists and a `handoff.yaml` companion (carrying `volatile_facts`) that feeds `quality`/`operate` downstream, grounded in the tool each artifact names and date-stamped for re-verification, never a frozen template dump. ## Reference and asset library 16 references under `skills/ai/references/` (10 primary jobs, 5 surface overlays, 1 handoff contract) and 9 fillable assets under `skills/ai/assets/`: - **Assets:** `model-selection-scorecard.md` · `otel-genai-observability-checklist.md` · `context-budget-worksheet.md` · `agent-loop-and-tool-contract-checklist.md` · `rag-pipeline-checklist.md` · `eval-harness-starter.md` · `llm-judge-rubric-template.md` · `guardrails-controls-checklist.md` · `handoff.yaml` ## Digital Product lifecycle `ai` is the intelligence layer of the **Build** stage, peer of `frontend` and `backend`: it consumes `architecture`'s contracts, NFR budgets, threat model, and `handoff.yaml` upstream, and hands its running behavior, eval suites, and telemetry hooks to `operate` and `quality` downstream. It *references* rather than owns the serving substrate (`backend`: MCP/tool endpoints, vector-store-as- served, gateways, durable-execution infra), the governed dataset/pipeline (`data`), and the chat UI (`frontend`). It works standalone with no upstream artifact required, and never silently invokes a sibling skill. ## Repository layout ```text skills/ai/ runtime skill (SKILL.md + 16 references + 9 assets + agents/openai.yaml) evals/ activation · traversal · output · compression-ablation fixtures research/ 4-channel research corpora + build-gate synthesis docs/ installation and usage .codex-plugin/ plugin manifest ``` ## Documentation & releases - [docs/installation.md](docs/installation.md) · [docs/usage.md](docs/usage.md) · [docs/recipes.md](docs/recipes.md) - [CHANGELOG.md](CHANGELOG.md) — release history (current: v0.1.0) - [SOURCES.md](skills/ai/SOURCES.md) — source attribution and license posture - [CONTRIBUTING.md](CONTRIBUTING.md) — local validation (`scripts/lint-skill`, `scripts/check-sync`) ## License MIT — see [LICENSE](LICENSE). ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [gabros20](https://github.com/gabros20) - **Source:** [gabros20/ai-skill](https://github.com/gabros20/ai-skill) - **License:** MIT - **Homepage:** https://aiengskill.vercel.app Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-gabros20-ai-skill - Seller: https://agentstack.voostack.com/s/gabros20 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.