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System Design Review

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HoangNguyen0403
system-design-review

Audit an existing or proposed architecture and return a scored verdict across nine axes, from requirements and capacity evidence to observability and rollout, then convert gaps into a prioritized roadmap. Use when reviewing a design doc, auditing a running system, or gating a design.

Overview

PublisherHoangNguyen0403
Repositoryagent-skills-standard
Skill namesystem-design-review
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 Review 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-review
  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-review .claude/skills/system-design-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable System Design Review 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 Review 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 Review 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 Review

Priority: P1 (HIGH)

Score claims against evidence appropriate to the declared review scope. A proposed design may support a design-readiness verdict through stated mechanisms and planned validation; it cannot claim operational readiness from plans alone. An implementation review requires implementation evidence; an operations claim requires production runtime/deployment evidence.

Nine Axes (score each applicable axis 0-10)

AxisScores 10 whenScores 0 when
RequirementsFunctional, NFR, and out-of-scope written with ownersOnly a feature description exists
Capacity evidencePeak QPS, storage, and bandwidth computed and currentNumbers absent or older than the last traffic change
RedundancyFailure domains, recovery mechanisms and ownership fit the stated objective; proposals include validation criteria, operations claims require measured drillsCritical SPOF or no viable recovery for the stated objective
Data scalingAccess patterns mapped, ownership single, growth path statedOne shared store, no growth plan, unbounded tables
CachingHot read paths cached with TTL and invalidation definedNo cache on a proven hot path, or uninvalidatable cache
Async offloadSlow/bursty work is isolated when required by stated SLO or failure constraints, with bounded drain/recovery; bounded synchronous work is valid when it meets themRequired isolation is absent or synchronous coupling violates the stated constraints
ObservabilitySignals, alert ownership, runbooks and validation fit the declared scope; operations claims require observed telemetryNo relevant signals, owner or response path for a material risk
RolloutCanary or flag with metric rollback trigger and reversible migrationsBig-bang deploy, irreversible migration
Cost proportionalitySpend is sized to the traffic and the risk, and someone can state itTopology bought for an imagined scale nobody measured

Report each applicable axis with evidence and a declared profile weighting. Use an applicable-axis denominator (10 × applicable-axis count), not a fixed /90, when an axis is justified N/A.

Profile-Aware Scoring

  • Declare the system profile and weighting before scoring. An axis may be N/A only when the profile and evidence show that it is outside the system's risk envelope; record the rationale, exclude it from the denominator, and do not silently convert it to zero.
  • Do not reward adding a cache, queue, replica, or region by vocabulary alone. A component earns credit only when a measured constraint, invariant, owner, cost, and failure/recovery behavior require it; unjustified machinery lowers cost proportionality and operability.
  • Review HLD and LLD as one trace: requirements and shaping decisions must resolve into component ownership, contracts, verification, and a stated changed-constraint trigger. A diagram is optional when prose answers the question.
  • Separate lifecycle (proposed|implemented|retired), source kind (code|document|runtime|deployment), and evidence confidence (unverified|assumed|documented|observed). Code/document citations are documented, not deployment proof; runtime/deployment captures may be observed. assumed and unverified carry no citation; explicit citations require evidence_kind.

Scope-Qualified Semantic Review

Lexical checks are smoke signals, not proof of a sound design. Apply the independent behavioral rubric in semantic evaluation. Record missing calculations, mechanisms, adverse timelines, invariants, recovery, and scope-specific evidence as findings even when expected vocabulary appears.

Review Method

  1. Establish ground truth first: current traffic, data volume, incident history, and the top pain the owner reports.
  2. Score the nine axes against artifacts and metrics; mark any unverifiable claim UNVERIFIED.
  3. Trace the hottest and the most critical path end to end; the worst hop is the real bottleneck.
  4. List findings as severity - axis - evidence - consequence - smallest fix.
  5. Convert findings into a roadmap: stop-the-bleeding now, structural next, optional later.
  6. Check operability: who runs this at 3am, which team owns which piece, and whether that team can actually operate it.

Common Mistakes to Check

  • Architecture drawn before requirements or numbers existed.
  • Redundancy claimed but sharing one config plane, credential, or control plane.
  • Cache added over a query that was never optimized.
  • Sharding adopted before indexing, replicas, and caching were exhausted.
  • Queue with no drain-rate budget, no DLQ, and alerting on depth rather than age.
  • Alerts on CPU rather than user-visible symptoms or error-budget burn.
  • Migration and code shipped as one irreversible step.

Anti-Patterns

  • No score without evidence: cite the metric, artifact, or drill; otherwise mark UNVERIFIED.
  • No rewrite recommendation by default: prefer the smallest fix that removes the proven bottleneck.
  • No uniform severity: rank by user impact and reversibility, not by axis order.
  • No finding without a next action: every gap gets an owner-ready fix.

References

  • Scorecard - scoring rubric, weighting guidance, report template
  • Mistakes Table - failure symptom, root cause, and corrective action

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 Review AI skill do?

Audit an existing or proposed architecture and return a scored verdict across nine axes, from requirements and capacity evidence to observability and rollout, then convert gaps into a prioritized roadmap. Use when reviewing a design doc, auditing a running system, or gating a design.

Why use System Design Review on TypingMind?

Because you install it once and use it with any model. System Design Review 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 Review 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-review. 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 Review?

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 Review?

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

Is the System Design Review 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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