Ln 23 System Design Proposal Builder logo

Ln 23 System Design Proposal Builder

Community
levnikolaevich
ln-23-system-design-proposal-builder

Designs target system boundaries, contracts and tradeoffs from requirements; does not plan tasks or implement.

Overview

Publisherlevnikolaevich
Repositoryclaude-code-skills
Skill nameln-23-system-design-proposal-builder
Stars
565
Forks
84
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Ln 23 System Design Proposal Builder 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.
  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/levnikolaevich/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/plugins/architecture-suite/skills/ln-23-system-design-proposal-builder .claude/skills/ln-23-system-design-proposal-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ln 23 System Design Proposal Builder 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 Ln 23 System Design Proposal Builder 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 Ln 23 System Design Proposal Builder 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 Proposal Builder

Goal: Create a proportionate, evidence-backed target system design that turns requirements into explicit boundaries, contracts, data flow, failure behavior, operations, and tradeoffs. Change only the approved design document; do not implement, audit, or approve the delivery.

Execution contract: The checklist defines completion. Track each item internally as PENDING, PROVEN with evidence, CLEARED with evidence its condition is absent, or UNPROVEN with a gap; reading, delegation, or tool failure is not proof. Reconcile after each section. Before returning, resolve all PENDING, count only PROVEN and CLEARED, and apply verdict and approval rules to every gap. Preserve intent, scope, and existing authorization. Continue authorized work; ask only for consequential unresolved choices or required external approval. Scale depth to material risk without skipping checks. Preserve dependency and safety order; otherwise choose an appropriate verification method. Accept equivalent user or repository evidence; no other skill, named artifact, or complete lifecycle is required. Preserve source requirement and decision IDs. Bind reused evidence to relevant source versions, dirty changes, configuration, and environment; invalidate only affected claims. On continuation, reconcile task, authorization, current state, and unresolved evidence. For long work, return a compact continuation record or update an already authorized artifact; read-only skills do not persist it. Distinguish artifact readiness, verified behavior, and external-action authority. Prepare authorized work before required approval. If blocked by an instruction, cite its exact source and unresolved boundary; do not invent approval gates from caution.

Tool Routing

NeedPreferred capabilityFallback
Requirements and constraintsApproved requirements, baseline, decisions, and direct stakeholder inputMark material gaps and ask the smallest decision question
Current implementation and conventionsRepository search, manifests, entrypoints, and architecture artifactsTreat as greenfield only when the user or repository establishes that fact; otherwise mark current state UNKNOWN and return REVISE or BLOCKED when the gap can change boundaries, compatibility, or migration
External capabilities and limitsCurrent official documentation and specificationsMark claims UNVERIFIED; avoid vendor-dependent commitment
EstimatesReproducible arithmetic from sourced workload assumptionsUse ranges and sensitivity; never present estimates as measurements
Document mutationMinimal patch to the approved target-design artifactReturn BLOCKED if scope or path is unsafe

Use patterns as candidate solutions, not goals. Introduce infrastructure only when a requirement, failure mode, ownership boundary, or measured horizon pays for its lifecycle cost.

Artifact Rules

  • Reuse a clear target-design document; otherwise use docs/architecture/target-design.md.
  • Read available baseline, current-state, decision, interface, diagram, and migration artifacts by path; none is mandatory.
  • Label facts, assumptions, estimates, proposed decisions, and unresolved choices separately.
  • Compare credible alternatives for consequential decisions, including the simplest feasible option; when constraints permit only one, document why rather than inventing another.
  • Prefer reversible choices and the simplest topology fitting the system. For a new application, consider a modular monolith before independent services; do not force that shape onto libraries, plugins, or an established topology.
  • Do not silently change an accepted decision; record the conflict and required governance action.

Checklist

1. Frame the Design

  • Resolve business outcome, actors, journeys, scope, non-goals, horizon, readers, language, and the approved canonical destination before editing.
  • Read repository instructions and inspect relevant architecture artifacts and current implementation.
  • Extract functional requirements and measurable quality drivers, preserving their source and status.
  • Identify architecture-critical unknowns and ask only questions whose answers change the target shape.
  • Return BLOCKED when a required business boundary or safety constraint cannot be responsibly assumed.

2. Estimate Before Choosing Components

  • Estimate average and peak request or event rates, concurrency, payload and bandwidth, storage growth, retention, and recovery volume where relevant.
  • Show formulas, ranges, growth horizon, and assumptions; identify the variables that can reverse a choice.
  • Identify likely first bottlenecks and explicit thresholds for deferred scaling mechanisms.
  • Separate availability, latency, durability, consistency, security, cost, and operability requirements from implementation preferences.
  • Reject speculative scale and list complex mechanisms intentionally deferred.

3. Define Domains, Data, and Contracts

  • Map business capabilities, domains or modules, ownership, invariants, and allowed dependency direction.
  • Define systems of record, data models at architecture depth, lifecycle, retention, consistency, and transaction boundaries.
  • Define public APIs, events, commands, schemas, errors, idempotency, ordering, versioning, and compatibility expectations.
  • Define trust boundaries, identities, authorization, sensitive data, secrets, abuse controls, and audit needs proportionate to risk.
  • Keep framework and vendor details outside the core model unless they are genuine constraints.

4. Build HLD and Critical LLD

  • Describe system context, deployable units, stores, queues, external systems, responsibilities, and labeled data flows.
  • Trace success, overload, dependency failure, partial failure, retry, timeout, degradation, recovery, and cancellation for critical journeys.
  • Deep-dive only components whose correctness, scale, security, or reversibility risk warrants implementation-level detail.
  • Define observability, SLI measurement points, health, deployment strategy, rollback, backup, and operator actions.
  • Define ownership, team impact, cost drivers, and operational burden for the proposed topology.

5. Decide and Validate

  • Compare credible alternatives against requirements, estimates, failure behavior, complexity, cost, migration, and future triggers.
  • State selected and rejected options with consequences, sensitivity points, and assumptions that would reopen the decision.
  • Identify significant decisions that deserve their own compact decision records without requiring another workflow.
  • Define architecture acceptance evidence appropriate to each material driver: contract checks, load/failure experiments, security validation, recovery proof, or observability signals. Specify prerequisites and pass criteria; do not execute them during design.
  • Outline current-to-target implications and compatibility needs without expanding into a full implementation plan.

6. Write and Report

  • Write context, drivers, estimates, domains, contracts, HLD, critical LLD, failure and operations model, security, alternatives, decisions, validation, open questions, and evolution triggers.
  • Preserve existing content outside the approved scope and link shared artifacts only by document path or title.
  • Re-read the proposal for unsupported facts, hidden decisions, mixed abstraction, and unjustified machinery.
  • Demonstrate that each material design boundary is implementable: link source requirements to contracts, data, failure behavior, unresolved choices, and acceptance evidence; distinguish a ready proposal from an accepted decision.
  • Use READY only when the design is decision-complete enough for implementation planning; use REVISE for material but solvable gaps; use BLOCKED when required intent, evidence, authority, or destination is unavailable.

Self-Check

  • Reconcile before returning. Check item-level evidence, requirement coverage, contradictions, scope, verdict, and applicable cleanup. Correct the report or authorized artifacts. Reuse valid evidence; do not automatically rescan the repository or rerun successful commands. Repeat checks only for relevant changes, failures, or unresolved evidence. Disclose remaining gaps.

Output Contract

Report in the user's language, in this order; retain all five fields and state each fact once. Small results may use one line per field; omit empty tables and do not copy linked artifacts:

  1. Result: Skill-specific verdict and supported outcome.
  2. Scope: Reviewed/changed scope, exclusions, baseline, and material assumptions.
  3. Evidence: Skill-specific fields below; distinguish facts, inferences, and unverified claims. Link artifacts; use tables when useful.
  4. Verification: Checks/results, unavailable evidence, and applicable cleanup/external state.
  5. Completion: Checklist: X/Y complete; Incomplete: None or each UNPROVEN item's reason, outcome impact, and exact next action; residual risks and required decisions.

Skill-specific evidence: Artifact path; requirements, estimates, boundaries, contracts, HLD/critical LLD, selected decisions, alternatives, evidence, and reopen triggers. Summarize validation/transition needs, compatibility, rollout, rollback, observability, and only unresolved choices that affect implementation planning.

Frequently asked questions

What does the Ln 23 System Design Proposal Builder AI skill do?

Designs target system boundaries, contracts and tradeoffs from requirements; does not plan tasks or implement.

Why use Ln 23 System Design Proposal Builder on TypingMind?

Because you install it once and use it with any model. Ln 23 System Design Proposal Builder 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 Ln 23 System Design Proposal Builder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/levnikolaevich/claude-code-skills/tree/master/plugins/architecture-suite/skills/ln-23-system-design-proposal-builder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ln 23 System Design Proposal Builder?

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 Ln 23 System Design Proposal Builder?

As many as you like. As long as a model supports skills, you can use Ln 23 System Design Proposal Builder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ln 23 System Design Proposal Builder AI skill free?

Yes. It is published on GitHub by levnikolaevich 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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