Ln 12 Product Requirements Builder logo

Ln 12 Product Requirements Builder

Community
levnikolaevich
ln-12-product-requirements-builder

Defines product requirements, business rules and acceptance criteria for a committed intent; edits product docs only.

Overview

Publisherlevnikolaevich
Repositoryclaude-code-skills
Skill nameln-12-product-requirements-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 12 Product Requirements 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/product-discovery-suite/skills/ln-12-product-requirements-builder .claude/skills/ln-12-product-requirements-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ln 12 Product Requirements 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 12 Product Requirements 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 12 Product Requirements 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.

Product Requirements Builder

Goal: Create or update a usable product requirements artifact that preserves the owner's intent and makes expected behavior testable. Change only authorized product documentation; do not invent commitments, design architecture, or implement.

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
Intent and prior decisionsUser request, product documents and accepted decisionsBounded assumptions; ask only for missing consequential intent
Existing behaviorFocused repository, UI, contract and analytics evidenceSupplied examples with explicit uncertainty
Requirement artifactExisting canonical product document and focused editorUser-approved destination; BLOCKED if no safe destination is available

Domain Rules

  • Reuse the existing requirement owner; otherwise use an authorized docs/product/requirements.md. Do not impose a new document hierarchy on an established project.
  • Separate observed behavior, owner preference, proposed requirements, accepted commitments, and unresolved choices. A discovery recommendation is not authorization to build.
  • Use stable requirement identifiers when traceability spans artifacts. Keep functional rules here and reference architecture constraints by source; user stories are optional representations.

Checklist

1. Establish Intent and Authority

  • Resolve the problem, affected actors, intended outcome, horizon, approved documentation scope, and protected existing experience.
  • Read repository instructions, relevant user evidence and existing requirements; inspect target files and user changes before editing.
  • Identify one authoritative requirements destination and applicable decision owners; preserve unresolved conflicting sources.
  • Separate non-goals, optional ideas and committed scope; clarify only choices that change acceptance or product intent.

2. Specify Observable Behavior

  • Describe the initiating event, actor permissions, preconditions, successful outcome and meaningful alternatives for every in-scope journey.
  • Specify business rules, calculations, entities and lifecycle transitions where they determine observable behavior.
  • Specify applicable failure, empty, loading, retry, duplicate, cancellation and recovery behavior without inventing irrelevant states.
  • Record affected integrations, external commitments, compatibility and data constraints from authoritative sources.
  • Capture accessibility, privacy and other applicable user-facing constraints; reference architecture-driving targets without duplicating their owner.
  • Separate required new UX from protected existing flows, copy and behavior.

3. Define Acceptance and Outcome

  • Give each material requirement observable acceptance conditions with prerequisites and an expected result independent of implementation.
  • Define the intended business effect, available baseline, measurement window and evidence source; keep unknown targets unknown.
  • Identify dependencies and assumptions that can reverse scope, acceptance or the chosen product direction.
  • Distinguish functional acceptance from product impact and from permission to publish or run an experiment.

4. Write and Validate

  • Write the approved artifact with requirement IDs, source/status, acceptance, non-goals, assumptions and unresolved decisions.
  • Preserve unrelated content and history of changed commitments; mark supersession instead of silently replacing accepted intent.
  • Check consistency across rules, scenarios and acceptance; expose requirements that cannot yet be implemented or tested safely.
  • Report the exact consequential gaps and next evidence actions; do not treat the document's existence as readiness.

Verdict

  • READY: requirements are consistent and testable, with no consequential unresolved intent preventing the next decision; proposed status does not imply owner acceptance.
  • INCOMPLETE: a useful artifact exists but named requirements or decisions remain unresolved.
  • BLOCKED: scope, authority, essential intent or a safe destination prevents responsible 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, intent, protected behavior, requirement/source/status/acceptance mapping, changed commitments, outcome measures, and consequential unknowns with their next evidence action.

Frequently asked questions

What does the Ln 12 Product Requirements Builder AI skill do?

Defines product requirements, business rules and acceptance criteria for a committed intent; edits product docs only.

Why use Ln 12 Product Requirements Builder on TypingMind?

Because you install it once and use it with any model. Ln 12 Product Requirements 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 12 Product Requirements Builder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/levnikolaevich/claude-code-skills/tree/master/plugins/product-discovery-suite/skills/ln-12-product-requirements-builder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ln 12 Product Requirements 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 12 Product Requirements Builder?

As many as you like. As long as a model supports skills, you can use Ln 12 Product Requirements Builder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ln 12 Product Requirements 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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