vasilyu1983
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ai-architecture-advisor
Chooses among AI/ML approaches: classical ML, LLM, RAG, fine-tuning, agents, multimodal, embeddings/recsys, dense/MoE/SSM/diffusion. Use when picking or scaling an architecture.
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Chooses among AI/ML approaches: classical ML, LLM, RAG, fine-tuning, agents, multimodal, embeddings/recsys, dense/MoE/SSM/diffusion. Use when picking or scaling an architecture.
Chooses among AI/ML approaches: classical ML, LLM, RAG, fine-tuning, agents, multimodal, embeddings/recsys, dense/MoE/SSM/diffusion. Use when picking or scaling an architecture.
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The front-door decision skill for "I have problem X — what should I build with?" It owns the choice — which approach fits, when to promote complexity (and when not to), how to transfer knowledge, how to scale — then hands off to the deep skill that owns the depth. It spans the whole modeling space in one place so you can compare options that normally live in separate skills side by side:
tabular GBDT · deep net · Transformer/LLM · RAG · fine-tuning · agents
multimodal/omni · embeddings & retrieval · recsys/ranking · model architecture (dense/MoE/SSM/diffusion)
No theory dumps — decision tables, elimination logic, tradeoffs, and a pointer to the deep skill.
The architect's move is to ask before answering. The amateur hears "build an AI feature" and reaches for the model they know ("we'll fine-tune Kimi"). The architect first asks: what data type? what volume? what task? what's the success metric? do you even need a Transformer? The skill that distinguishes an architect is the willingness to say "for this, CatBoost wins," "here you need a Transformer," or "LoRA is enough here" — and to refuse to name an approach until the problem is classified. Never jump to a model before the Intake questions below are answered.
problem + data + constraints
|
v
0. INTAKE — ask before answering (see questions below)
| do NOT name a model until task + data + metric + constraints are known
v
1. classify the problem (tabular? text? generation? decision/action? retrieval?)
|
v
2. eliminate ineligible options (with a reason each — never silently drop)
|
v
3. score survivors independently (accuracy, latency, cost, data need, interpretability, ops)
|
v
4. pick the SIMPLEST that clears the bar (start simple, promote only on evidence)
|
v
5. hand off to the deep skill (ai-ml-data-science / ai-llm / ai-rag / ai-agents ...)
A request like "let's fine-tune model X" is a proposed solution, not a problem statement. Do not accept it at face value. Surface the six questions that decide the architecture, and hold any model name until they're answered. If the user can't answer one, that gap is itself a finding (most often: no success metric, or no labeled data).
| Ask | Why it changes the answer |
|---|---|
| What's the task? classify / rank / generate / extract / retrieve / act | Picks the lane before anything else — tabular-classify and open-ended-generate share no architecture |
| What's the data? type (tabular/text/image/audio), volume, labeled? private? fresh? | Tabular+small → trees; private/fresh knowledge → RAG not fine-tune; no labels → no SFT |
| What's the success metric & bar? accuracy / latency / cost-per-outcome / interpretability | "Make it work" can't be scored; the binding constraint eliminates most options |
| What are the constraints? p99 latency, $/query at volume, on-prem/cloud, no-hallucination | A <50ms or AWS-only or no-hallucination constraint forces the design more than the task does |
| Is there a regulatory/compliance driver? high-risk classification (EU AI Act), data residency, right-to-explanation, audit trail | Can force an inherently-interpretable model (trees, not a black-box net), ban sending data to a third-party API, or mandate human-in-the-loop — overriding an accuracy-only ranking |
| Do you even need an LLM/Transformer? | The cheapest thing that clears the bar wins — often a tree, a regex, or a single API call beats a fine-tune |
Breaking ties among survivors: hard constraints (a p99 ceiling, an on-prem requirement, a regulatory bar) are gates — fail one and the option is out, no matter how it scores elsewhere. Only among options that clear every gate do the soft criteria (cost, developer familiarity, marginal accuracy) act as tie-breakers. Don't let a soft criterion overrule a failed hard constraint, and don't eliminate an option on a soft criterion before checking whether a rival actually clears every gate.
| You're choosing between | Default first move | Promote when | Deep skill |
|---|---|---|---|
| Tabular model family | Gradient-boosted trees (XGBoost) | Within a tabular foundation model's envelope (TabPFN-3 ≤~1M rows; TabICL v2 if open-source) it now beats tuned GBDTs there | ai-ml-data-science |
| Trees vs deep learning (tabular) | Trees | Huge data + raw signals (images/text/audio) embedded in features | ai-ml-data-science |
| Thinking effort on one model | Low/no thinking | Gap is multi-step logic/planning: raise the thinking budget (it's a dial, not a separate model) before fine-tuning | ai-llm |
| LLM prompt vs RAG | Prompt-only | Needs current/private knowledge or citations (and the corpus won't fit affordably in a long-context window) | ai-rag |
| RAG vs fine-tuning | RAG | Behavior/format gap persists after RAG+prompt fixes; combine both (RAFT) for domain-specific RAG | ai-llm |
| Adapt vs post-train (RLHF/DPO/GRPO) | Prompt → RAG → SFT first | Behavior/preference/reasoning gap survives SFT and you own preference or verifiable-reward data | ai-post-training |
| Single LLM call vs agent | Single call / workflow |
Activate when the user asks (in any language) some form of:
If the user has already chosen the approach and wants to build it, skip this skill and go straight to the deep skill.
This skill decides; these implement. Hand off once the approach is chosen. Primary skill per
lane below — the full AI skill map catalogs all 36 ai-*
skills (supporting + adjacent) grouped by lane, with one-line "owns what" descriptions.
ai-context-layer, ai-vector-brainai-bot-builder, ai-voice-bots, ai-coding-agentsWhat are you predicting / producing?
|
+- A value/label from columns (tabular) ............ CLASSICAL ML
| small data or need interpretability? ............ trees / linear
| within a tabular-FM envelope, max accuracy? ..... tabular foundation model (TabPFN-3 / TabICL v2)
| accuracy on large/wide structured data? ......... gradient-boosted trees
|
+- Rank items for a user from behavior ............. RECSYS / RANKING
| implicit feedback + <50ms + dynamic catalog? ... two-tower retrieve -> ranker (not RAG)
|
+- Text understanding/generation .................... LLM
| stable instructions, no private data? .......... prompt-only
|
name: ai-architecture-advisor description: "Chooses among AI/ML approaches: classical ML, LLM, RAG, fine-tuning, agents, multimodal, embeddings/recsys, dense/MoE/SSM/diffusion. Use when picking or scaling an architecture." compatibility: Portable core. Works on Claude Code and Codex. version: "1.7" last_validated: 2026-07-11
---
name: ai-architecture-advisor
description: "Chooses among AI/ML approaches: classical ML, LLM, RAG, fine-tuning, agents, multimodal, embeddings/recsys, dense/MoE/SSM/diffusion. Use when picking or scaling an architecture."
compatibility: Portable core. Works on Claude Code and Codex.
version: "1.7"
last_validated: 2026-07-11
---
# AI Architecture Advisor
The **front-door decision skill** for "I have problem X — what should I build with?" It owns
the **choice** — which approach fits, when to promote complexity (and when *not* to), how to
transfer knowledge, how to scale — then **hands off** to the deep skill that owns the depth. It
spans the whole modeling space in one place so you can compare options that normally live in
separate skills side by side:
```text
tabular GBDT · deep net · Transformer/LLM · RAG · fine-tuning · agents
multimodal/omni · embeddings & retrieval · recsys/ranking · model architecture (dense/MoE/SSM/diffusion)
```
No theory dumps — decision tables, elimination logic, tradeoffs, and a pointer to the deep skill.
**The architect's move is to ask before answering.** The amateur hears "build an AI feature"
and reaches for the model they know ("we'll fine-tune Kimi"). The architect first asks: *what
data type? what volume? what task? what's the success metric? do you even need a Transformer?*
The skill that distinguishes an architect is the willingness to say **"for this, CatBoost wins,"**
**"here you need a Transformer,"** or **"LoRA is enough here"** — and to refuse to name an
approach until the problem is classified. Never jump to a model before the Intake questions
below are answered.
## ASCII Flow
```text
problem + data + constraints
|
v
0. INTAKE — ask before answering (see questions below)
| do NOT name a model until task + data + metric + constraints are known
v
1. classify the problem (tabular? text? generation? decision/action? retrieval?)
|
v
2. eliminate ineligible options (with a reason each — never silently drop)
|
v
3. score survivors independently (accuracy, latency, cost, data need, interpretability, ops)
|
v
4. pick the SIMPLEST that clears the bar (start simple, promote only on evidence)
|
v
5. hand off to the deep skill (ai-ml-data-science / ai-llm / ai-rag / ai-agents ...)
```
## Intake: Ask Before You Answer
A request like "let's fine-tune model X" is a *proposed solution*, not a problem statement.
Do not accept it at face value. Surface the six questions that decide the architecture, and
hold any model name until they're answered. If the user can't answer one, that gap is itself a
finding (most often: no success metric, or no labeled data).
| Ask | Why it changes the answer |
|---|---|
| **What's the task?** classify / rank / generate / extract / retrieve / act | Picks the lane before anything else — tabular-classify and open-ended-generate share no architecture |
| **What's the data?** type (tabular/text/image/audio), volume, labeled? private? fresh? | Tabular+small → trees; private/fresh knowledge → RAG not fine-tune; no labels → no SFT |
| **What's the success metric & bar?** accuracy / latency / cost-per-outcome / interpretability | "Make it work" can't be scored; the binding constraint eliminates most options |
| **What are the constraints?** p99 latency, $/query at volume, on-prem/cloud, no-hallucination | A <50ms or AWS-only or no-hallucination constraint forces the design more than the task does |
| **Is there a regulatory/compliance driver?** high-risk classification (EU AI Act), data residency, right-to-explanation, audit trail | Can force an inherently-interpretable model (trees, not a black-box net), ban sending data to a third-party API, or mandate human-in-the-loop — overriding an accuracy-only ranking |
| **Do you even need an LLM/Transformer?** | The cheapest thing that clears the bar wins — often a tree, a regex, or a single API call beats a fine-tune |
**Breaking ties among survivors:** hard constraints (a p99 ceiling, an on-prem requirement, a
regulatory bar) are *gates* — fail one and the option is out, no matter how it scores elsewhere.
Only among options that clear every gate do the soft criteria (cost, developer familiarity,
marginal accuracy) act as tie-breakers. Don't let a soft criterion overrule a failed hard
constraint, and don't eliminate an option on a soft criterion before checking whether a rival
actually clears every gate.
## Quick Reference
| You're choosing between | Default first move | Promote when | Deep skill |
|---|---|---|---|
| Tabular model family | Gradient-boosted trees (XGBoost) | Within a tabular foundation model's envelope (TabPFN-3 ≤~1M rows; TabICL v2 if open-source) it now beats tuned GBDTs there | [ai-ml-data-science](../ai-ml-data-science/SKILL.md) |
| Trees vs deep learning (tabular) | Trees | Huge data + raw signals (images/text/audio) embedded in features | [ai-ml-data-science](../ai-ml-data-science/SKILL.md) |
| Thinking effort on one model | Low/no thinking | Gap is multi-step *logic/planning*: raise the thinking budget (it's a dial, not a separate model) before fine-tuning | [ai-llm](../ai-llm/SKILL.md) |
| LLM prompt vs RAG | Prompt-only | Needs current/private knowledge or citations (and the corpus won't fit affordably in a long-context window) | [ai-rag](../ai-rag/SKILL.md) |
| RAG vs fine-tuning | RAG | Behavior/format gap persists after RAG+prompt fixes; combine both (RAFT) for domain-specific RAG | [ai-llm](../ai-llm/SKILL.md) |
| Adapt vs post-train (RLHF/DPO/GRPO) | Prompt → RAG → SFT first | Behavior/preference/reasoning gap survives SFT and you own preference or verifiable-reward data | [ai-post-training](../ai-post-training/SKILL.md) |
| Single LLM call vs agent | Single call / workflow | Needs tools (via MCP), multi-step planning, or external actions | [ai-agents](../ai-agents/SKILL.md) |
| Omni model vs specialist pipeline | Omni/VLM to validate | Accuracy KPI, cost-at-volume, or no-hallucination needs → disaggregate to specialists | [ai-prompt-engineering](../ai-prompt-engineering/SKILL.md) |
| Embedding / retrieval model | Strong general embedder + reranker | Domain/multilingual fit, latency, or late-interaction needs (note: swap = full re-index) | [ai-rag](../ai-rag/SKILL.md) |
| Search/rank vs recommend | — | Implicit feedback + <50ms + dynamic M-item catalog → it's recsys, not RAG | [ai-ml-data-science](../ai-ml-data-science/SKILL.md) |
| Sequence/perception net family | Transformer (text), ViT (vision) | Streaming/tiny-footprint → RNN/GRU; spatial images → CNN; text-to-text transform → encoder-decoder; encode-for-retrieval → encoder-only (BERT-class) | [ai-ml-data-science](../ai-ml-data-science/SKILL.md) |
| Model architecture (dense/MoE/SSM/diffusion) | Whatever the API serves | Only when self-hosting/training: MoE (cost), SSM/hybrid (>100k-tok throughput), diffusion-LM (tokens/sec); attention = GQA default, MLA/NSA for KV-cache/long-context | [ai-llm-inference](../ai-llm-inference/SKILL.md) |
| Train from scratch vs adapt | Adapt (prompt/RAG/fine-tune) | Research goal *is* the pre-training, or no suitable base model | [ai-pretraining](../ai-pretraining/SKILL.md) |
## When to Use This Skill
Activate when the user asks (in any language) some form of:
- "What architecture should I use for X?" / "Как выбрать архитектуру?"
- "When should I use CatBoost / Transformer / RAG / fine-tuning / an agent?"
- "Trees or neural net for this tabular problem?"
- "Do I need RAG, or is fine-tuning better?"
- "Should this be one LLM call or a multi-agent system?"
- "Omni model or a specialist pipeline for my images/audio?"
- "Which embedding model / do I need a reranker?"
- "Is this a search problem or a recommendation problem?"
- "Dense or MoE / should I care about Mamba / diffusion LLMs?" (model-architecture axis)
- "How do I find and evaluate newer architectures than what I know?"
- "How do I transfer knowledge to a new task?" (transfer learning / distillation / RAG)
- "How do I scale this model?" (data, params, test-time compute, serving, or distributed training)
- Any side-by-side comparison of modeling approaches before committing to a build.
If the user has **already** chosen the approach and wants to build it, skip this skill and
go straight to the deep skill.
## Scope Boundaries (Use These Skills for Depth)
This skill decides; these implement. Hand off once the approach is chosen. Primary skill per
lane below — the **[full AI skill map](references/ai-skill-map.md)** catalogs all 36 `ai-*`
skills (supporting + adjacent) grouped by lane, with one-line "owns what" descriptions.
- **Classical ML / data** → [ai-ml-data-science](../ai-ml-data-science/SKILL.md) (GBDT, EDA, train a specialist), [ai-ml-timeseries](../ai-ml-timeseries/SKILL.md)
- **LLM lifecycle & prompting** → [ai-llm](../ai-llm/SKILL.md) (fine-tune, migrate, select), [ai-prompt-engineering](../ai-prompt-engineering/SKILL.md), [ai-post-training](../ai-post-training/SKILL.md) (RLHF/DPO/GRPO/RLVR)
- **Retrieval & context** → [ai-rag](../ai-rag/SKILL.md), `ai-context-layer`, [ai-vector-brain](../ai-vector-brain/SKILL.md)
- **Agents & applied bots** → [ai-agents](../ai-agents/SKILL.md), `ai-bot-builder`, [ai-voice-bots](../ai-voice-bots/SKILL.md), [ai-coding-agents](../ai-coding-agents/SKILL.md)
- **Pre-training** → [ai-pretraining](../ai-pretraining/SKILL.md), [ai-distributed-training](../ai-distributed-training/SKILL.md), [ai-scaling-laws](../ai-scaling-laws/SKILL.md), [ai-data-curation-pretraining](../ai-data-curation-pretraining/SKILL.md)
- **Serving, evals & ops** → [ai-llm-inference](../ai-llm-inference/SKILL.md), [ai-local-model-ops](../ai-local-model-ops/SKILL.md), [ai-evals](../ai-evals/SKILL.md), [ai-deep-research](../ai-deep-research/SKILL.md), [ai-mlops](../ai-mlops/SKILL.md), [ai-product-operating-model](../ai-product-operating-model/SKILL.md)
- **Scouting new architectures** → [research-arxiv-scout](../research-arxiv-scout/SKILL.md), [research-scout](../research-scout/SKILL.md)
- **Beyond the AI family** → [foundations-decision-theory](../foundations-decision-theory/SKILL.md) (choice under uncertainty), [software-architecture-design](../software-architecture-design/SKILL.md) (system/runtime topology)
## Default Workflow
1. **Classify the problem.** What is the output and the data?
- Structured/tabular rows -> classical ML lane.
- Natural-language understanding or generation -> LLM lane.
- Retrieve-then-answer over a corpus -> RAG lane.
- Multi-step actions / tool use -> agent lane.
- Images/audio/raw signals -> deep-learning lane.
2. **Eliminate ineligible options with a reason.** Never silently drop a candidate. E.g.
"fine-tuning eliminated: the gap is missing knowledge, not behavior — RAG first."
3. **Score the survivors independently** on: accuracy ceiling, latency, cost-per-outcome,
data required, interpretability, and operational burden. Don't pre-commit before scoring.
4. **Pick the simplest option that clears the bar.** Start simple; promote complexity only
on evidence of a repeated, stable failure the simpler option can't fix.
5. **State the decision and hand off** to the deep skill, with the *reason* and the
*promotion trigger* ("revisit if X").
## Decision Tree: The Lanes
```text
What are you predicting / producing?
|
+- A value/label from columns (tabular) ............ CLASSICAL ML
| small data or need interpretability? ............ trees / linear
| within a tabular-FM envelope, max accuracy? ..... tabular foundation model (TabPFN-3 / TabICL v2)
| accuracy on large/wide structured data? ......... gradient-boosted trees
|
+- Rank items for a user from behavior ............. RECSYS / RANKING
| implicit feedback + <50ms + dynamic catalog? ... two-tower retrieve -> ranker (not RAG)
|
+- Text understanding/generation .................... LLM
| stable instructions, no private data? .......... prompt-only
| Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
66/100
Promising
Trust
67/100
Sandbox only
Audit
79/100
Risky
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, network or browser access",
"GitHub adoption: 86 GitHub stars",
"Stars/forks activity: 86 stars, 18 forks; issue activity unavailable in current metadata",
"Permission surface: secrets or environment access, network or browser access"
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"Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
"Financial research output is not financial advice; require human review before any live investment decision.",
"This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, network or browser access",
"GitHub adoption: 86 GitHub stars"
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"label": "Blocked for auto-install",
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"Audit risk risky exceeds max_risk=medium",
"High-risk permission hints: Secrets or environment access",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required"
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"task": "Use ai-architecture-advisor in an agent workflow",
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"install_used": true,
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"endpoints": {
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"api": "https://www.openagentskill.com/api/agent/skills/vasilyu1983-ai-architecture-advisor",
"audit": "https://www.openagentskill.com/skills/vasilyu1983-ai-architecture-advisor/audit",
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"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ai-architecture-advisor%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/vasilyu1983-ai-architecture-advisor/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/vasilyu1983-ai-architecture-advisor"
}
}Listing source
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[](https://www.openagentskill.com/skills/vasilyu1983-ai-architecture-advisor/audit)
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| Needs tools (via MCP), multi-step planning, or external actions |
| ai-agents |
| Omni model vs specialist pipeline | Omni/VLM to validate | Accuracy KPI, cost-at-volume, or no-hallucination needs → disaggregate to specialists | ai-prompt-engineering |
| Embedding / retrieval model | Strong general embedder + reranker | Domain/multilingual fit, latency, or late-interaction needs (note: swap = full re-index) | ai-rag |
| Search/rank vs recommend | — | Implicit feedback + <50ms + dynamic M-item catalog → it's recsys, not RAG | ai-ml-data-science |
| Sequence/perception net family | Transformer (text), ViT (vision) | Streaming/tiny-footprint → RNN/GRU; spatial images → CNN; text-to-text transform → encoder-decoder; encode-for-retrieval → encoder-only (BERT-class) | ai-ml-data-science |
| Model architecture (dense/MoE/SSM/diffusion) | Whatever the API serves | Only when self-hosting/training: MoE (cost), SSM/hybrid (>100k-tok throughput), diffusion-LM (tokens/sec); attention = GQA default, MLA/NSA for KV-cache/long-context | ai-llm-inference |
| Train from scratch vs adapt | Adapt (prompt/RAG/fine-tune) | Research goal is the pre-training, or no suitable base model | ai-pretraining |
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.