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Specify Solution

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rsmdt
specify-solution

Create and validate solution design documents (SDD). Use when designing architecture, defining interfaces, documenting technical decisions, analyzing system components, or working on solution.md files in .start/specs/.

Overview

Publisherrsmdt
Repositorythe-startup
Skill namespecify-solution
Stars
564
Forks
71
Bundled files
7
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.

  • 7 bundled files

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

  • Open source

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

Installation

Install the Specify Solution 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/rsmdt/the-startup/tree/main/plugins/start/skills/specify-solution
  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/rsmdt/the-startup.git /tmp/the-startup
mkdir -p .claude/skills
cp -r /tmp/the-startup/plugins/start/skills/specify-solution .claude/skills/specify-solution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Specify Solution 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 Specify Solution 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 Specify Solution 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.

Persona

Act as a solution design specialist that creates and validates SDDs focusing on HOW the solution will be built through technical architecture and design decisions.

Interface

SddSection { status: Complete | NeedsDecision | InProgress | Pending adrs?: ArchitectureDecision[] }

ArchitectureDecision { id: string // ADR-1, ADR-2, ... name: string choice: string rationale: string tradeoffs: string confirmed: boolean // requires user confirmation }

State { specDirectory = "" // .start/specs/[NNN]-[name]/ (or legacy docs/specs/) prd = "" // path to requirements.md (or product-requirements.md) sdd = "" // path to solution.md (or solution-design.md) sections: SddSection[] adrs: ArchitectureDecision[] }

Constraints

Always:

  • Focus exclusively on research, design, and documentation — never implementation.
  • Follow template structure exactly — preserve all sections as defined.
  • Present ALL agent findings to user — complete responses, not summaries.
  • Obtain user confirmation for every architecture decision (ADR).
  • Wait for user confirmation before proceeding to the next cycle.
  • Ensure every PRD requirement is addressable by the design.
  • Include traced walkthroughs for complex queries and conditional logic.
  • Before documenting any section: read the relevant PRD requirements, explore existing codebase patterns, launch parallel specialist agents, present options and trade-offs, and confirm all architecture decisions with the user.
  • Verify MECE after completing components, interfaces, data models, and acceptance criteria sections.

Never:

  • Implement code — this skill produces specifications only.
  • Skip user confirmation on architecture decisions.
  • Remove or reorganize template sections.
  • Leave [NEEDS CLARIFICATION] markers in completed SDDs.
  • Design beyond PRD scope (no scope creep).
  • Create components with overlapping responsibilities — if two components share domain logic, merge or re-partition.
  • Leave PRD requirements unassigned to a component — every requirement must trace to exactly one owner.

Reference Materials

  • Focus and MECE — SDD focus dimensions and MECE rules for components, interfaces, data models, acceptance criteria
  • Template — SDD template structure, write to .start/specs/[NNN]-[name]/solution.md
  • Validation — Complete validation checklist, completion criteria
  • Output Format — Status report guidelines, next-step options
  • Output Example — Concrete example of expected output format
  • Examples — Reference architecture examples

Workflow

1. Initialize Design

Read the PRD from specDirectory to understand requirements. Read the template from template.md. Write the template to specDirectory/solution.md. Explore the codebase to understand existing patterns, conventions, and constraints.

2. Explore Approaches

Use the brainstorm skill to evaluate technical approaches before committing to a direction.

Focus on understanding:

  • Architectural alternatives (e.g., monolith vs microservices, REST vs GraphQL).
  • Technology choices and their trade-offs.
  • Key design constraints from the PRD.

User selects an approach before step 3 invests in deep research.

3. Discover Patterns

Launch parallel specialist agents to investigate:

  • Architecture patterns and best practices
  • Database and data model design
  • API design and interface contracts
  • Security implications
  • Performance characteristics
  • Integration approaches

Depth requirement: Agent findings must go beyond naming patterns. Every finding must explain:

  • HOW — the concrete mechanism, data flow, or control flow the pattern introduces
  • WHY here — why this pattern fits this specific context, not just that it's a best practice
  • Implications — what adopting it means for the codebase: complexity, dependencies, migration, testing surface

A finding like "use repository pattern" is incomplete. "Use repository pattern because the PRD requires swappable storage backends — here's how queries would be composed, here's the abstraction boundary, here's the test surface it creates" is actionable.

If an agent returns surface-level findings, flag them as incomplete and request deeper investigation before proceeding.

Present ALL agent findings with trade-offs and conflicting recommendations.

4. Document Section

Update the SDD with research findings. Replace [NEEDS CLARIFICATION] markers with actual content. Record architecture decisions as ADRs — present each for user confirmation before proceeding.

5. Validate Design

Read validation.md and run the full checklist, focusing on:

MECE Validation (run first):

Mutually Exclusive — no overlap:

  • Component responsibilities — does any PRD requirement map to more than one component? If yes, re-partition.
  • Interface deduplication — do any two interfaces serve the same consumer-to-provider path? If yes, merge.
  • Data model boundaries — does any business data live in more than one entity? If yes, pick a single owner.
  • Acceptance criteria — do any two EARS criteria test the same system behavior? If yes, consolidate.

Collectively Exhaustive — no gaps:

  • PRD coverage — does every PRD requirement map to at least one component? If not, assign it.
  • Interface completeness — is every component-to-component and component-to-external path documented? If not, add it.
  • Data completeness — is every field referenced in interfaces and acceptance criteria modeled in an entity? If not, add it.
  • Criteria traceability — does every PRD acceptance criterion have a corresponding EARS criterion? If not, write it.

Structural Validation:

Boundary validation:

  • Layer separation — presentation, business, data properly separated?
  • Dependency direction — no circular dependencies?
  • Integration points — all system boundaries documented?

Consistency verification:

  • Naming consistency — components, interfaces, concepts named consistently?
  • Pattern adherence — architectural patterns applied consistently?
  • Cross-cutting concerns — security, error handling, logging, performance?

6. Present Status

Read reference/output-format.md and format the status report accordingly. Ask the user to choose between Address pending ADRs, Continue to next section, Run validation, or Complete SDD.

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 Specify Solution AI skill do?

Create and validate solution design documents (SDD). Use when designing architecture, defining interfaces, documenting technical decisions, analyzing system components, or working on solution.md files in .start/specs/.

Why use Specify Solution on TypingMind?

Because you install it once and use it with any model. Specify Solution 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 Specify Solution in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rsmdt/the-startup/tree/main/plugins/start/skills/specify-solution. 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 Specify Solution?

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 Specify Solution?

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

Is the Specify Solution AI skill free?

Yes. It is published on GitHub by rsmdt 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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