Asymmetric Wins logo

Asymmetric Wins

OrganizationPopular
EpicenterHQ
asymmetric-wins

Identify an asymmetric win by refusing a small promise that would eliminate disproportionate complexity. Use when the user asks about asymmetric wins, pixel perfect fidelity, compatibility, fallbacks, modes, duplicate paths, or what would delete disproportionate complexity.

Overview

PublisherEpicenterHQ
Repositoryepicenter
Skill nameasymmetric-wins
Stars
4.8K
Forks
386
Bundled files
Instructions only
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 EpicenterHQ on GitHub. Read the source before you install it.

Installation

Install the Asymmetric Wins 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/EpicenterHQ/epicenter/tree/main/.agents/skills/asymmetric-wins
  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/EpicenterHQ/epicenter.git /tmp/epicenter
mkdir -p .claude/skills
cp -r /tmp/epicenter/.agents/skills/asymmetric-wins .claude/skills/asymmetric-wins
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Asymmetric Wins 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 Asymmetric Wins 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 Asymmetric Wins 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.

Asymmetric Wins

An asymmetric win is a refusal that gives back more complexity than it costs in product capability.

The spine:

txt
Preserve the product sentence.
Refuse the small promise.
Delete the code family.

The usual shape: trade a small amount of fidelity, compatibility, modes, or reproducibility to collapse a much larger implementation graph.

This is not arithmetic and not a quota. Do not remove arbitrary features. The job is to find the one small promise that owns a disproportionate code family, then decide whether refusing that exact promise leaves the product sentence intact.

Do not say "good enough" and ship drift. Name the product sentence first. Keep the workflow, safety, accessibility, and recognizable product feel. Refuse only the promise that was forcing a second system, and keep it when the evidence says the loss is load-bearing.

Complexity is evidence, not a verdict. A feature can own a large code family and still be worth keeping when the capability it provides is load-bearing. Establish the valuable workflow before ranking deletion prizes. Compare each refusal on two axes: what the person loses and what machinery disappears. Treat a change to the product's purpose as a separate product decision, not as an ordinary simplification.

Compose With

  • one-sentence-test detects the opportunity (the surface audit surfaces the convenience feature that forces a second product sentence). This skill owns the decision.
  • Refactoring methods guide caller counts, fixtures, state branches, styling branches, docs paths, and other code-family evidence before the refusal is executed.
  • ui-design owns visual direction, accessibility, brand, and whether a UI detail is load-bearing before pixel fidelity is refused.
  • Rethink's clean-break reference guides the resulting replacement, verification, and old-path deletion. Use rethink when the refusal calls for developing a new destination.

Domain Manifestations

The same move shows up in different clothes:

txt
UI refactor
  Refuse exact pixel or markup reproduction only when the core workflow,
  hierarchy, accessibility, important states, and product feel survive.

Code refactor
  Refuse legacy aliases, duplicate helpers, fallback parsers, and old call
  shapes when callers can move to one canonical path.

Tests and reproducibility
  Refuse exact old snapshots, fixture quirks, or transitional behavior when the
  product contract is clearer than the old artifact.

Design artifacts
  Refuse full HTML facsimiles and exhaustive component trees when a native
  primitive, screenshot, compact sketch, or state table preserves the same
  decision.

Architecture
  Refuse keeping both mental models alive. A half-old, half-new system is
  usually the expensive promise.

Procedure

txt
1. Name the product sentence and the workflow that must remain true.
2. List candidate refusal points: fast paths, old shapes, rare modes, provider
   exceptions, compatibility aliases, fallback parsers, exact reproduction,
   partial reflection, hand-reproduced UI structure.
3. For each candidate, name both the capability loss and the deletion prize:
   methods, adapters, unions, error variants, tests, docs branches, UI states,
   styling branches, fixtures, screenshots, migrations, local markup, custom CSS,
   and diagram upkeep.
4. Separate candidates that change the product's purpose from candidates that
   remove a convenience within the same workflow.
5. Prefer the smallest capability loss that removes the underlying second shape;
   do not choose a larger product sacrifice merely because it deletes more code.
6. If the loss is load-bearing, keep the feature and write down why its
   complexity is worth owning. Otherwise, refuse the behavior when the deletion
   removes a second shape and write that refusal into the spec.

The rule is evidence-seeking, not dramatic: if the valuable workflow survives and the code family disappears, refusal is the default recommendation. Keep the feature when the user loss is load-bearing or when the "deletion" would only move complexity somewhere harder to see.

Decision Template

Use this shape in specs and design notes:

txt
Product sentence:
  ...

Candidate refusal:
  ...

Deletion prize:
  ...

User loss:
  ...

Decision:
  Refuse it / keep it because ...

UI Refactor Template

Use this when the promise is visual or interaction fidelity:

txt
Product sentence:
  ...

Must preserve:
  workflow, information hierarchy, accessibility, important states,
  recognizable product feel, inspectable state, reviewable intent

Can refuse:
  exact spacing, old breakpoints, one-off hover states, incidental animation,
  pixel-perfect empty/loading/error states, duplicate responsive layouts,
  locally reproduced HTML when a shared primitive owns the same contract,
  exhaustive component trees when a smaller artifact preserves the decision

Deletion prize:
  ...

Replacement artifact:
  shared primitive / screenshot / compact sketch / state table / full tree

Decision:
  Refuse it / keep it because ...

Pixel, markup, and screenshot fidelity are load-bearing when the exact detail carries comprehension, accessibility, trust, brand, or a regression-sensitive state. Otherwise, exact reproduction is a promise like any other: keep it only when it earns the code family it forces.

UI Artifact Ladder

Use the smallest artifact that preserves the design decision:

txt
1. Shared primitive or existing component
   Use when the UI library already owns the structure, spacing, states, and
   accessibility contract.

2. Screenshot or generated image
   Use when visual appearance matters more than exact markup.

3. Compact sketch or state table
   Use when the decision is hierarchy, state, or flow.

4. Partial tree
   Use when parent-child structure is the point under review.

5. Full HTML diagram
   Use only when exact DOM shape is the product contract or the bug.

Do not make agents reproduce a full HTML tree to prove a design unless the tree is the thing that must stay stable. Prefer the natural primitive, then verify the states that matter.

Worked Example: Social Sign-In

txt
Product sentence:
  All social sign-in routes through the API-hosted page via OAuth 2.1 PKCE.

Candidate refusal:
  Browser SPAs can use Google GIS for a roughly 1-second sign-in.

Deletion prize:
  signInWithIdToken
  OIDCProvider narrowing
  per-app GIS helpers
  GIS blocked-browser UI
  SocialSignInUnavailable
  provider-specific SDK scaling for Apple and Microsoft
  two social sign-in docs branches
  two social sign-in test paths

User loss:
  Google sign-in is a few seconds slower in browser SPAs.

Decision:
  Refuse it. The UX loss is small; the second auth shape is permanent.

The product still has social sign-in. It refuses one fast path so one invariant can own every provider and environment.

For narrative context, see docs/articles/20260504T160541-asymmetric-wins-support-fewer-features-to-collapse-complexity.md.

Frequently asked questions

What does the Asymmetric Wins AI skill do?

Identify an asymmetric win by refusing a small promise that would eliminate disproportionate complexity. Use when the user asks about asymmetric wins, pixel perfect fidelity, compatibility, fallbacks, modes, duplicate paths, or what would delete disproportionate complexity.

Why use Asymmetric Wins on TypingMind?

Because you install it once and use it with any model. Asymmetric Wins 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 Asymmetric Wins in TypingMind?

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

Which AI models can use Asymmetric Wins?

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 Asymmetric Wins?

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

Is the Asymmetric Wins AI skill free?

It is published on GitHub by EpicenterHQ. Check the repository for licensing terms. 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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