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Sisyphus Design

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arkbriar
sisyphus-design

Produce high-quality design documents through research, divergent thinking, and iterative refinement

Overview

Publisherarkbriar
Repositorysisyphus-design
Skill namesisyphus-design
Stars
32
Forks
1
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Sisyphus Design 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/arkbriar/sisyphus-design/tree/main/skills/design
  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/arkbriar/sisyphus-design.git /tmp/sisyphus-design
mkdir -p .claude/skills
cp -r /tmp/sisyphus-design/skills/design .claude/skills/sisyphus-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sisyphus Design 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 Sisyphus Design 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 Sisyphus Design 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.

Design Skill

Design requirement: $ARGUMENTS

If no arguments provided, use AskUserQuestion to ask for the design requirement before proceeding.

Read before starting:

Phase 0: Research & Diverge (no human)

For trivial requirements (single flag, simple rename, obvious-one-solution), skip Phase 0 and Phase 1 — go directly to Phase 2. Present a single approach and identify the consumer as part of the decision presentation.

  1. Investigate: spawn a fresh research agent. It receives the design requirement and returns a structured summary: existing patterns found, constraints discovered, relevant prior art. Use web search whenever the domain is unfamiliar, prior art may exist, or something is unclear. Keep the main context clean — only bring back the summary.
  2. Think from first principles: what is this system fundamentally trying to do? What are the core constraints? Strip away conventional framing and reason from the ground up.
  3. Identify the critical path: the component or decision that, if wrong, makes the system fail regardless of how good everything else is.
  4. Generate 2-3 fundamentally different approaches that diverge on the critical path. Reuse obvious choices elsewhere.
  5. For each approach: one-sentence core idea, key trade-off, when it fails.

Phase 1: Interrogate (human)

Use AskUserQuestion to present concisely:

  1. Consumer: who implements from this design. Their first unanswered question.
  2. Critical path: the component or decision from Phase 0. Why this is the critical path.
  3. Approaches: the 2-3 from Phase 0 with trade-offs.
  4. Recommended approach: which one and why, based on the analysis.
  5. Discriminating questions: only questions whose answer would change the recommendation.

Wait for answers. User confirms, redirects, or asks follow-ups.

Phase 2: Decisions (human)

  1. Present the decisions as conversation text per the Decision Log format in template.md, including expanded blocks for critical path decisions and the end-to-end example trace (what data enters, what choices apply, what comes out — component-level state detail comes in Phase 3).
  2. Use AskUserQuestion to collect feedback — approve, reject, or comment. Keep the question short; do not repeat the decisions.
  3. Revise if rejected. All open questions must be answered before proceeding. If the user or the decisions reveal the requirement is more complex than initially assessed (especially if the trivial shortcut was used), return to Phase 0 for proper research.

Phase 3: Draft & Refine (no human)

  1. Draft the design using template.md. Write to design/<name>.md. Derive <name> as YYYY-MM-DD-<topic> (e.g., 2026-03-19-auth). If that file already exists, append -2, -3, etc. Where the input is ambiguous, state the ambiguity and chosen interpretation in the design. Carry the Phase 2 example trace into the Interactions section, updating it to match final component names.
  2. Self-check against checklist.md, skipping procedure step 0b (baseline sketch — the author cannot objectively evaluate their own design against a baseline). Fix all issues found.
  3. Spawn a fresh evaluator agent (Agent tool). The agent must:
    • Have NO context about the design's history, decisions, or prior findings
    • Read the design file and checklist.md
    • Read the original design requirement and the Phase 0 research summary (pass both in the agent prompt). The approved decision log is already in the design file's Decision Log section.
    • Output findings per checklist format
  4. Fix root cause clusters first. Then do a nit pass on remaining SPECIFY/ACKNOWLEDGE findings — a pile of small issues is still a quality problem.
  5. Convergence check — open a finding ledger for this refinement loop:
    • Each finding = question number + concepts touched + concrete gap description
    • Two findings match if same question and same concepts
    • Track resolved vs new findings each cycle
    • Stop if: 0 REDESIGN FAILs, or 3 evaluator cycles reached
    • Otherwise: loop back to step 3
    • At stop: outstanding REDESIGN FAILs become Known Limitations in the design.
  6. Present the design file to the user.

Phase 4: Review (human)

User reads the design file and provides comments.

Phase 5: Refine from Comments (no human)

Open a new finding ledger. Same loop as Phase 3, seeded with human comments as mandatory fixes. Human comments override evaluator findings when they concern the same component or concept.

Phase 6: Deliver

Present:

  • Path to the final design file
  • Change summary: redesigns made, recurring fixes, novel FAILs remaining
  • Finding summary from the most recent non-empty refinement loop
  • Q17 FLAGs as known uncertainties

Rules

  • Phases execute in order. No skipping unless the trivial-requirement shortcut applies.
  • Never add a component to fix a finding unless the finding traces to a specific input requirement or an approved decision. If it does not, add to out-of-scope or acknowledge in Known Limitations.
  • Never mitigate environmental limitations with mechanism. Acknowledge in prose.
  • After decisions are approved, the design must be directive. No hedging, no presenting unchosen alternatives as viable options. The consumer implements from this — tell them what to build, not what you considered.
  • All design content goes in the file, not conversation output.
  • Research agents: spawn a fresh agent whenever investigation is needed. Keep research results out of the main design context — only bring back the summary.
  • All spawned agents (research, evaluator) MUST explicitly use the current model. Do not let the runtime downgrade.

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 Sisyphus Design AI skill do?

Produce high-quality design documents through research, divergent thinking, and iterative refinement

Why use Sisyphus Design on TypingMind?

Because you install it once and use it with any model. Sisyphus Design 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 Sisyphus Design in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/arkbriar/sisyphus-design/tree/main/skills/design. 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 Sisyphus Design?

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 Sisyphus Design?

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

Is the Sisyphus Design AI skill free?

Yes. It is published on GitHub by arkbriar 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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