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Tech Spec

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dmmulroy
tech-spec

Write a typed call-stack architecture handoff.

Overview

Publisherdmmulroy
Repositoryskills
Skill nametech-spec
Stars
431
Forks
14
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 dmmulroy on GitHub. Read the source before you install it.

Installation

Install the Tech Spec 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/dmmulroy/skills/tree/main/tech-spec
  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/dmmulroy/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/tech-spec .claude/skills/tech-spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tech Spec 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 Tech Spec 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 Tech Spec 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.

Tech Spec

A tech spec is a typed call-stack architecture handoff: code-shaped contracts plus execution flows. Prefer TypeScript pseudocode over prose wherever precision matters.

This skill is design-only. Do not implement. Save a file only when the user asks for a file; otherwise return the spec inline.

Branch selection

  1. Use Path A: Convert context to spec when the conversation, docs, or codebase already contain enough background to describe the change.
  2. Use Path B: Grill first when the user wants a new spec but has not provided enough problem, constraints, design direction, affected code, or acceptance criteria.

If a question can be answered by exploring the codebase, inspect the codebase instead of asking.

Completion criterion: the branch is chosen from actual available context; missing architectural decisions are not invented.

Path A: Convert context to spec

1. Load standards and local context

Inspect existing code and docs for local vocabulary, module layout, domain concepts, errors, adapters, observability, runtime patterns, and test style.

Completion criterion: the spec uses project vocabulary and does not introduce a pattern, library, adapter, schema style, or test strategy before checking local precedent.

2. Extract the design problem

Capture:

  • current state;
  • problem;
  • users/callers;
  • goals;
  • non-goals;
  • constraints;
  • invariants;
  • affected systems;
  • likely entrypoints;
  • operational/runtime concerns;
  • risks;
  • open questions.

Mark unknowns as open questions instead of filling gaps with plausible design.

Completion criterion: every claimed requirement or constraint is grounded in conversation, code, docs, or an explicit open question.

3. Explore design alternatives

Produce materially different alternatives before choosing the recommended design. Alternatives should differ in interface shape, seam placement, ownership, call stack, runtime topology, or module boundaries — not just names.

For each alternative, sketch:

  • domain types and state model;
  • public/module interfaces and APIs;
  • input/output types;
  • expected failure types;
  • seams, boundaries, and adapters;
  • entrypoint-to-side-effect call stack;
  • parsing/projection strategy;
  • authorization, observability, cancellation, idempotency, and transaction flow when reachable;
  • test seam strategy;
  • tradeoffs.

Compare alternatives on:

  • caller burden;
  • module depth and leverage;
  • locality of invariants and change;
  • seam placement;
  • boundary parsing and projections;
  • error and cancellation model;
  • testability through real seams;
  • operational/runtime fit;
  • implementation complexity.

Completion criterion: the recommendation is chosen after comparing alternatives, not before.

4. Specify the recommended typed contracts

For the recommended design, outline every new, changed, or deleted:

  • domain value;
  • branded/refined type;
  • state machine variant;
  • input/output type;
  • request/response shape;
  • function signature;
  • class or module interface;
  • expected-failure/custom-error type;
  • adapter interface;
  • protocol DTO;
  • persistence DTO/projection;
  • runtime-boundary codec;
  • public API.

Name seams, adapters, implementations, ownership boundaries, and what crosses each boundary. State what each layer may know and what must not leak across the seam.

Completion criterion: every new or changed boundary has a concrete type/interface/API sketch, or an explicit reason no new contract is needed.

5. Specify call stacks and data flow

For every new, changed, or deleted behavior, show the call stack from entrypoint to side effects and response.

Include type/data flow:

txt
raw input
  -> boundary DTO / unknown
  -> parser
  -> canonical domain/application input
  -> service/module interface
  -> adapter call
  -> typed result/error
  -> projection
  -> serialized output

Include current vs proposed flow when changing existing behavior. Include failure, retry, cancellation, transactionality, idempotency, observability, authorization, and runtime-hop flow when reachable.

Completion criterion: every affected behavior has an end-to-end call stack and type/data-flow trace.

6. Map files and modules

List:

  • files/modules to add;
  • files/modules to change;
  • files/modules to delete, if any;
  • test files;
  • config/migration/runtime files, if any.

For each file, state the contract, code path, boundary, adapter, domain concept, or test responsibility it owns.

Completion criterion: every contract and call-stack step maps to a file/module or an open question.

7. Write the RGR TDD test plan

Use the sibling TDD workflow and testing standards. Plan vertical Red-Green-Refactor slices: one failing behavior test, minimal implementation, repeat. Do not write a horizontal "all tests first, all code later" plan.

Favor behavior through public interfaces and real seams over implementation-coupled mocks.

Cover proportionately:

  • happy paths;
  • failure paths;
  • parser rejection and accepted shapes;
  • domain invariants and state transitions;
  • adapter contracts;
  • persistence/runtime semantics;
  • cancellation/retry/idempotency paths;
  • observability and safe summaries where relevant;
  • end-to-end flows for high-consequence behavior.

Completion criterion: every public behavior, invariant, important failure path, changed boundary, and changed seam has a red test slice or an explicit reason not to test it.

8. Produce the spec

Return the spec inline unless the user requested a file path. If a file was requested, save it there.

Do not implement and do not ask to implement by default.

Completion criterion: the output follows the outline below and is implementation-ready for another engineer.

Path B: Grill first

  1. Do not write a full spec yet.
    • State that there is not enough context for an implementation-ready tech spec.
    • Completion criterion: the agent has not invented requirements, APIs, files, or call stacks.
  2. Start a grilling interview.
    • Ask one question at a time and provide the recommended answer with each question.
    • If a question can be answered by exploring the codebase, inspect the codebase instead of asking.
    • Completion criterion: the interview has enough context for Path A: problem, users/callers, constraints, affected systems, desired behavior, boundaries, likely APIs, invariants, risks, and acceptance tests.
  3. Convert to the spec.
    • Once grilling context is sufficient, run Path A.
    • Completion criterion: the final artifact is a typed call-stack architecture handoff, not interview notes.

Required spec outline

Use this shape unless the task is tiny enough to compress without losing contracts or call stacks:

md
# <Title>

## Summary

## Context / Current State

## Goals

## Non-Goals

## Invariants

## Design Constraints

## Alternatives Considered

### Option 1: <name>

### Option 2: <name>

### Option 3: <name>

## Recommendation

## Proposed Design

## Domain Model and Types

## Types, Interfaces, and APIs

## Seams, Boundaries, Adapters, and Implementations

## Call Stacks and Data Flow

### Current / Old Flow

### Proposed / New Flow

### Failure Flow

### Retry / Cancellation / Idempotency Flow

### Observability Flow

## Files to Add / Change / Delete

## RGR TDD Test Plan

## Risks and Open Questions

Omit sections that truly do not apply, but do not omit typed contracts, seams, call stacks, or tests merely because they are hard to specify.

Writing rules

  • Code first: TypeScript pseudocode defines contracts, APIs, and data flow.
  • Prose explains why; types and call stacks define what changes.
  • Focus on types, interfaces, APIs, inputs/outputs, seams, boundaries, adapters, domain modules, service modules, external adapters, and call stacks.
  • Prefer precise domain values over strings, booleans, nullable bags, and loosely shaped objects.
  • Keep seams real: adapters translate framework, persistence, network, time, randomness, telemetry, runtime, or platform boundaries.
  • Avoid speculative abstraction; every seam earns its existence through invariants, locality, leverage, testing, or a real boundary.
  • Keep a single source of truth; do not restate the same rule in multiple sections unless one section points to the other.
  • Unknowns stay open questions. Do not invent product requirements, domain rules, APIs, or call stacks to make the spec feel complete.

Frequently asked questions

What does the Tech Spec AI skill do?

Write a typed call-stack architecture handoff.

Why use Tech Spec on TypingMind?

Because you install it once and use it with any model. Tech Spec 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 Tech Spec in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dmmulroy/skills/tree/main/tech-spec. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Tech Spec?

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 Tech Spec?

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

Is the Tech Spec AI skill free?

Yes. It is published on GitHub by dmmulroy 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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