Fsi Architecture Assessment logo

Fsi Architecture Assessment

Organization
Amazon-Quick
fsi-architecture-assessment

Assess a financial services architecture diagram against the AWS Well-Architected Financial Services Industry (FSI) Lens, rate maturity across the six pillars, and produce prioritized recommendations. Use when asked to 'assess my architecture against the FSI Lens', 'run a Well-Architected FSI review', 'check this diagram for FSI compliance gaps', 'review my financial services architecture', or any request to evaluate an FSI workload diagram against Well-Architected best practices.

Overview

PublisherAmazon-Quick
RepositoryAmazon-Quick-official-catalog
Skill namefsi-architecture-assessment
Stars
50
Forks
4
Bundled files
4
LicenseMIT-0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Amazon-Quick on GitHub. Read the source before you install it.

Installation

Install the Fsi Architecture Assessment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/software-engineering/fsi-architecture-assessment
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Amazon-Quick/Amazon-Quick-official-catalog.git /tmp/Amazon-Quick-official-catalog
mkdir -p .claude/skills
cp -r /tmp/Amazon-Quick-official-catalog/skills/software-engineering/fsi-architecture-assessment .claude/skills/fsi-architecture-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fsi Architecture Assessment in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Fsi Architecture Assessment on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Fsi Architecture Assessment is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Overview

This skill assesses a financial services customer's architecture diagram against the AWS Well-Architected Financial Services Industry (FSI) Lens (January 2026 update, including Gen AI and Agentic AI guidance). It ingests a diagram, contextualizes it for the stated FSI use case, rates maturity across the six Well-Architected pillars, and produces a prioritized, actionable improvement report. Use it for FSI Well-Architected reviews, compliance gap analysis, and architecture improvement planning.

Workflow

<Workflow - Assess Architecture description="Ingest a diagram, contextualize it for the FSI use case, assess against the FSI Lens pillars, and produce a prioritized report." tools=[get_current_time, file_read, file_read_image, file_read_pdf, file_write, web_search, url_fetch, open_in_session_tab] triggers=["assess my architecture against the FSI Lens", "run a Well-Architected FSI review", "check this diagram for FSI compliance gaps", "review my financial services architecture"]

  1. [Agent] Ingest and interpret {{architecture_diagram}}. Use file_read_image for an image or file_read_pdf for a PDF. Extract a structured inventory: AWS services, network topology (VPCs, subnets, Availability Zones, Regions, on-premises links), data flows, security boundaries, high-availability patterns, any AI or machine learning components, external integrations, and monitoring components. Validate: The inventory covers at least compute, storage, networking, security boundaries, data flow directions, and any AI or machine learning components. If fails: If the diagram is unclear, low-resolution, or too high-level, ask the user for a higher-quality version or a text description of the architecture.

  2. [Agent] Contextualize for the FSI use case. Read references/fsi-regulatory-map.md and map {{use_case}} to its regulatory regime, data sensitivity classification, RTO and RPO expectations, and business criticality. Validate: The context names a regulatory regime, a data classification, RTO/RPO expectations, and a business criticality level. If fails: If the use case is ambiguous or spans multiple jurisdictions, ask the user to clarify the specific regulatory environment and data sensitivity.

  3. [Agent] Assess against the FSI Lens pillars named in {{focus_pillars}} (all six if "all" or unset). Read references/fsi-lens-pillars.md and evaluate the inventory against each pillar's best-practice questions. Optionally use web_search and url_fetch to confirm current FSI Lens guidance, citing any source consulted. Validate: Each assessed pillar has a maturity rating, identified strengths, identified gaps, and a named FSI Lens best-practice reference per gap. If fails: If a pillar cannot be assessed from the available information, mark it Insufficient Information and list the additional details needed.

  4. [Agent] Call get_current_time, then generate the report. Read assets/report-template.md and fill it with the executive summary, architecture overview, per-pillar scorecards, prioritized roadmap, regulatory notes, and references. Use the maturity labels Well-Aligned, Partially Aligned, Gaps Identified, or Insufficient Information. Validate: The filled report contains every template section and the roadmap holds no more than 10 items. If fails: Rebuild the missing sections from the Step 3 findings before continuing.

  5. [Decide] Where should the report be saved?

    • {{output_directory}} provided -> write the report there as a Markdown file with a dated filename.
    • Not provided -> [Ask user] Ask for a destination directory, then write it there. Validate: The report file is written and non-empty. If fails: If the write fails, present the full report content directly in chat.
  6. [Agent] Open the report with open_in_session_tab and summarize the key findings in chat. Offer follow-up actions: deep-dive into one pillar, draft a remediation architecture description, build a compliance mapping for specific regulations, or export the report to another document format. Validate: The report opens in a session tab, or the summary is presented in chat. If fails: Present the full report content directly in chat.

</Workflow - Assess Architecture>

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Fsi Architecture Assessment AI skill do?

Assess a financial services architecture diagram against the AWS Well-Architected Financial Services Industry (FSI) Lens, rate maturity across the six pillars, and produce prioritized recommendations. Use when asked to 'assess my architecture against the FSI Lens', 'run a Well-Architected FSI review', 'check this diagram for FSI compliance gaps', 'review my financial services architecture', or any request to evaluate an FSI workload diagram against Well-Architected best practices.

Why use Fsi Architecture Assessment on TypingMind?

Because you install it once and use it with any model. Fsi Architecture Assessment is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Fsi Architecture Assessment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/software-engineering/fsi-architecture-assessment. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Fsi Architecture Assessment?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Fsi Architecture Assessment?

As many as you like. As long as a model supports skills, you can use Fsi Architecture Assessment with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Fsi Architecture Assessment AI skill free?

Yes. It is published on GitHub by Amazon-Quick under the MIT-0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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