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System Design Methodology

Community
HoangNguyen0403
system-design-methodology

Drives an interactive system design session: classifies depth, elicits scale/SLO/consistency inputs, computes capacity, then reveals components one by one, each justified by a constraint. Use when designing a system or running a design session; diagrams go through `common-architecture-diagramming`.

Overview

PublisherHoangNguyen0403
Repositoryagent-skills-standard
Skill namesystem-design-methodology
Stars
573
Forks
166
Bundled files
3
LicenseMIT
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.

  • 3 bundled files

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

  • Open source

    Published by HoangNguyen0403 on GitHub. Read the source before you install it.

Installation

Install the System Design Methodology 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/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/system-design/system-design-methodology
  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/HoangNguyen0403/agent-skills-standard.git /tmp/agent-skills-standard
mkdir -p .claude/skills
cp -r /tmp/agent-skills-standard/.agents/skills/system-design/system-design-methodology .claude/skills/system-design-methodology
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable System Design Methodology 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 System Design Methodology 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 System Design Methodology 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.

System Design Methodology

Priority: P0 (CRITICAL)

Requirements before solutions. Never draw a full architecture before numbers justify it.

Phase 0 - Classify Depth (always first)

  • Quick sketch: exploratory ask, no scale numbers available, answer needed now. Assume defaults, label each one ASSUMED, skip gates.
  • Full session: real build, migration, or budget commitment. Run every phase gate.
  • State depth and mode (new design | review existing | interview practice) in one line, then continue. Interview practice runs through system-design-interview-coaching: the round on a clock, the rubric after.
  • Escalate quick to full when a hard constraint or irreversible choice appears.

Phase 1 - Intake (gate)

  • Parse request: verbs to use cases, nouns to entities, adjectives to constraints.
  • Ask max 3 blocking questions per turn, each with a recommended default. See intake checklist.
  • Required before design: DAU/actors, top 3 use cases, read:write ratio, latency SLO, consistency need, retention, peak shape, budget, team size.
  • Freeze scope: list what is explicitly out of scope.

Phase 2 - Estimation (gate)

  • Compute QPS, storage, bandwidth, and working-set memory via system-design-estimation.
  • Present the numbers, name the one quantity that shapes the design, confirm before drawing anything.

Phase 3 - High-Level Design (incremental)

  • Price the null option first: do nothing, buy it, or let an existing service absorb it. Rejecting it needs a stated reason, not silence.
  • Start with the smallest system satisfying functional requirements: client, API, service, store.
  • Add one component at a time. For each, state constraint -> component -> cost in one line. No component without a named constraint.
  • Define API surface (one endpoint per functional requirement) and data ownership before optimizing.
  • Select views only when they answer a named question, per common-architecture-diagramming: a context/container, sequence, dataflow, deployment, or state view may be used when useful; prose or a table is sufficient otherwise. Carry metric and constraint only when the design states them; never invent a number to populate a node. See phase deliverables.

HLD, LLD, and Low-Level Design Routing

  • HLD answers audience-level boundaries, shaping constraints, ownership, failure domains, and the decision to make. Use context/container or prose only when that is enough; no diagram is mandatory.
  • LLD (the same lane as “low-level design”) answers one component or critical flow: data/state ownership, API or event contracts, ordering, idempotency, failure behavior, and verification. Use sequence, dataflow, or state only when that view resolves a named question.
  • Trace every handoff as requirement -> HLD decision -> component -> LLD contract -> verification. Give each link a stable ID and carry unresolved assumptions forward; an LLD must not silently change the HLD invariant.
  • Each selected view declares audience, question, decision, scenario, invariant, scope, status, evidence, and omissions. Lifecycle is proposed|implemented|retired; evidence is a citation, not confidence. Keep evidence_kind and evidence_confidence separate per the renderer-owned diagram spec and view manifest; never infer deployment from a code/document citation.
  • Views are evidence for a question, not a completeness checklist. Prefer a precise paragraph or table over a diagram that adds no decision value.

Specialist Deep-Dive Contract

Select zero, one or multiple dives only for unresolved consequential risks; no quota or invented risks. Each brief names its decision, specialist profile, audience/question, workload/SLO/team/budget, invariant, scope, evidence status and HLD decision. Omit dives without decision impact.

  • Require options with rejection reasons, the recommended LLD contract, failure timeline/recovery, verification hooks, and any ADR reversal trigger. Merge the result back into the HLD-to-LLD trace before scoring.

Brownfield Path (review-existing mode)

  • Map current state before proposing anything: components, owners, traffic, incidents.
  • Measure, do not assume: pull real QPS, data volume, and p99 from the running system.
  • Find the binding constraint - the one that fails first at the next growth step.
  • Design the smallest change that moves it, then re-measure. A rewrite needs a structural constraint the current shape cannot satisfy.

Phase 4 - Deep Dives and Trade-offs

  • Stage what to build now, the enabling seam and metric threshold; record one ADR per irreversible decision with its reversal trigger, then score with system-design-review.

Design-to-Delivery Gate

  • Once HLD/LLD is fixed, list bounded docs/diagram slices: exact files, evidence, acceptance, verification, integrator. Route production to the cheapest qualified configured executor if available; lead owns decisions and final review.
  • If still defective after one focused correction, use the configured fallback or report BLOCKED. Log executor/model, corrections, exceptions and fallback reason; report actual usage/cost or unavailable, never assumed savings.

Anti-Patterns

  • No architecture before requirements: no diagram until Phase 1 answers exist or defaults are flagged.
  • No unjustified components: every box names the constraint it solves.
  • No design without the null option: state why doing nothing or buying loses before building.
  • No silent assumptions: an unknown input becomes a labeled ASSUMED default, never a hidden guess.
  • No full-stack reveal: never dump a finished diagram before incremental agreement.

Red Flags

  • Stop if "just give me the architecture": deliver a quick sketch with ASSUMED labels, not fake precision.
  • Stop if scale is unknown at Phase 3: return to Phase 2 and estimate from a stated assumption.

References

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 System Design Methodology AI skill do?

Drives an interactive system design session: classifies depth, elicits scale/SLO/consistency inputs, computes capacity, then reveals components one by one, each justified by a constraint. Use when designing a system or running a design session; diagrams go through `common-architecture-diagramming`.

Why use System Design Methodology on TypingMind?

Because you install it once and use it with any model. System Design Methodology 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 System Design Methodology in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/system-design/system-design-methodology. 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 System Design Methodology?

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 System Design Methodology?

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

Is the System Design Methodology AI skill free?

Yes. It is published on GitHub by HoangNguyen0403 under the MIT 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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