Ln 21 System Design Baseline Builder logo

Ln 21 System Design Baseline Builder

Community
levnikolaevich
ln-21-system-design-baseline-builder

Defines measurable architecture drivers and constraints before system design; edits architecture docs only.

Overview

Publisherlevnikolaevich
Repositoryclaude-code-skills
Skill nameln-21-system-design-baseline-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 21 System Design Baseline 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-21-system-design-baseline-builder .claude/skills/ln-21-system-design-baseline-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ln 21 System Design Baseline 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 21 System Design Baseline 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 21 System Design Baseline 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 Baseline Builder

Goal: Create or update one durable source of truth for the project's architecture-driving requirements and constraints. Change only the approved architecture document; do not design the solution, review a plan, audit implementation, edit product code, or invent missing targets.

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
Repository rules and document conventionsNative file reads plus focused searchUser-provided convention with an explicit limitation
Existing requirements and architecture artifactsNarrow repository search and direct readsConversation evidence marked with its source
Current workload or service evidenceMetrics, dashboards, logs, manifests, or checked-in reportsMark UNKNOWN; never manufacture production numbers
Current external limits or standardsOfficial documentation or specificationsMark the claim UNVERIFIED
Document mutationMinimal patch to the approved Markdown artifactReturn BLOCKED if no safe writable path is authorized

Use external research only when a time-sensitive fact changes a constraint. Do not browse for values that must come from product owners, operators, the repository, or measured workload.

Artifact Rules

  • Prefer an existing unambiguous architecture-requirements document.
  • Otherwise use docs/architecture/system-design-baseline.md.
  • Read before writing, preserve unrelated content, and update facts in place instead of creating parallel truth.
  • Classify applicability separately as APPLICABLE or NOT_APPLICABLE, with evidence for exclusions.
  • Rank each applicable item as DRIVER, SUPPORTING, or INFORMATIONAL.
  • Classify evidence separately as CONFIRMED, ASSUMED, or UNKNOWN.
  • Separate observed current values, required targets, hard limits, and future evolution triggers.
  • Express quality attributes through observable scenarios and response measures; record undecided targets as missing decisions rather than inventing numbers.
  • Treat the baseline as versioned project knowledge, not an immutable promise.

Checklist

1. Establish Scope and Destination

  • Resolve the project, business outcome, intended readers, approved documentation scope, and language.
  • Read applicable repository instructions and inspect Git state so unrelated changes remain untouched.
  • Search for existing requirement, architecture, SLO, recovery, security, cost, and ownership documents.
  • Select one canonical artifact: reuse a clear equivalent or choose the default path; explain why no duplicate will be created.
  • Return BLOCKED if the destination is ambiguous and choosing one could split project truth.

2. Build the Evidence Ledger

  • Collect business goals, actors, journeys, scope, non-goals, and decision horizon with their sources and confidence for the driver analysis; do not create a second context inventory.
  • Record sources for current workload, data volume, service behavior, platform limits, and existing commitments.
  • Separate repository facts from stakeholder choices and estimates.
  • Detect contradictions between documents, code, configuration, and stated requirements; preserve both claims until resolved.
  • Ask only for choices whose absence materially changes architecture; mark all other gaps UNKNOWN.

3. Define and Prioritize Architecture Drivers

  • Business and scope: Record actors, critical journeys, business horizon, scope, non-goals, and externally committed outcomes.
  • Demand and data scale: Record current and target users, rates, concurrency, payloads, growth, retention, and forecast horizon where relevant.
  • User-observable service quality: Define SLIs and SLOs for availability, latency, throughput, error rate, correctness, or freshness with measurement windows.
  • Data semantics and recovery: Define consistency, ordering, idempotency, reconciliation, durability, backup, RTO, RPO, and acceptable data loss at affected boundaries.
  • Security, privacy, and compliance: Define trust boundaries, data classification, residency, access, audit, and destructive-action constraints.
  • Operations and economics: Define ownership, operational capacity, cost envelope, supported regions, delivery cadence, and platform or vendor constraints.
  • Evolution: Record thresholds, business events, or evidence that justify revisiting an assumption, target, or deferred capability.
  • Separate applicability, criticality, and evidence status; do not use UNKNOWN to mean unimportant or NOT_APPLICABLE.
  • Prioritize the few scenarios most likely to shape architecture and express each as source/stimulus/environment/artifact/response/measure.

4. Write the Baseline

  • Create or update the artifact with: identity and status; business context; scope and non-goals; critical scenarios; workload and data; quality targets; recovery; consistency; security; cost and operations; constraints; assumptions and unknowns; review triggers.
  • Give material parameters their theme, applicability, criticality, evidence status, value/range, source, owner, as-of date, and review trigger. State shared metadata once with explicit inheritance; use UNKNOWN for missing ownership or values.
  • Keep calculations reproducible and label estimates separately from observed measurements.
  • Link shared architecture artifacts only by repository path or document title; never require a particular workflow or tool.
  • Preserve historical context needed to understand changed requirements instead of silently rewriting prior commitments.

5. Validate and Report

  • Re-read the written artifact and verify that no unknown was converted into a confident fact.
  • Check that targets are measurable, internally consistent, and proportionate to the evidenced business horizon.
  • Check that every architecture-critical gap has an owner or exact next evidence action.
  • Link architecture drivers to their source product requirements; preserve the product source as owner of functional rules rather than copying a second requirements baseline.
  • Use READY only when the baseline is usable for decisions and no material unknown lacks a safe handling rule; use INCOMPLETE for a useful artifact with consequential open drivers; use BLOCKED when scope, authority, or destination prevents safe creation.

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, established drivers and prioritized quality scenarios, applicable constraints, and changed sections. The artifact owns the driver register: theme/parameter, applicability, criticality, evidence status, value/measure, source/owner, as-of date, and review trigger. Summarize only decision-changing gaps; do not copy the register into the response.

Frequently asked questions

What does the Ln 21 System Design Baseline Builder AI skill do?

Defines measurable architecture drivers and constraints before system design; edits architecture docs only.

Why use Ln 21 System Design Baseline Builder on TypingMind?

Because you install it once and use it with any model. Ln 21 System Design Baseline 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 21 System Design Baseline 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-21-system-design-baseline-builder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ln 21 System Design Baseline 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 21 System Design Baseline Builder?

As many as you like. As long as a model supports skills, you can use Ln 21 System Design Baseline Builder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ln 21 System Design Baseline 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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