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Omh Award Bar Score

CommunityPopular
rlaope
omh-award-bar-score

[omh] Hermes award-bar score workflow: score a web surface against published design-award judging axes and name the binding constraint. Use when the user says: award-bar-score, award bar score, award winning, award-winning, award winning website, award-winning website, award winning design, award ready.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-award-bar-score
Stars
2.7K
Forks
194
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Omh Award Bar Score 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/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-award-bar-score .claude/skills/omh-award-bar-score
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Award Bar Score 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 Omh Award Bar Score 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 Omh Award Bar Score 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.

Award Bar Score

This is an OMH award-bar-score workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

award-bar-score gives "make it award-winning" a measurable meaning: published axes, published weights, a published threshold, and the one axis holding the surface below it — instead of a taste argument nobody can settle.

Do Not Use When

  • The request is broad premium quality across decks, PDFs, or posters; use design-quality-gate.
  • The request is frontend implementation, layout, or design-system work; use frontend.
  • The request is WCAG, keyboard, or screen-reader conformance; use accessibility-audit.
  • The request is a rendered capture or a pixel verdict; use visual-qa.
  • The award is a business, sales, or team award with no judged web surface.

Examples

Good example:

  • Prompt: score our landing page against the css design awards bar and tell me what is holding it back
  • Expected behavior: Prepare award_bar_score/v1 with per-axis UI/UX/innovation scores from rendered evidence, the weighted total against the 8.0 threshold, the binding constraint, and the accessibility/performance tradeoff ledger.
  • Why: The request asks for a measured comparison against a published external bar, not a general polish pass.

Bad example:

  • Prompt: award-bar-score confirm this site will win website of the day
  • Expected behavior: Score the axes against the published model and refuse the outcome claim; a jury scores submissions and OMH does not.
  • Why: A rubric self-assessment cannot predict a jury result.

Completion Checklist

  • Each of UI, UX, and innovation carries its own score and the rendered evidence it was read from.
  • The weighted total is computed from the stated weights and compared against the published threshold.
  • The binding constraint names one axis and what moving it requires.
  • Any innovation move that costs accessibility or performance budget is recorded as a tradeoff the user chooses.
  • No award, jury, placement, or selection outcome is claimed.

Recovery Notes

  • If no rendered evidence exists, keep every axis not_observed and route the capture to visual-qa before scoring.
  • If the award body publishes no weights, score the axes separately and report the total as unweighted rather than inventing a ratio.

Use When

Use when a web surface must be judged against an external award bar: per-axis scores for UI, UX, and innovation, the weighted total against the published threshold, and the one axis holding the score down.

Strong routing signals: `award-bar-score`, `award bar score`, `award winning`, `award-winning`, `award winning website`, `award-winning website`, `award winning design`, `award ready`, `make it award winning`, `design award`, `design awards`, `css design awards`, `cssda`, `awwwards`, `site of the day`, `website of the day`, `wotd`, `score my site`, `어워드`, `디자인 어워드`, `어워드 수준`, `수상작 수준`, `수상 가능한 디자인`, `어워드 받을만한`, `올해의 사이트`

Catalog Metadata

Category: materials Phase: award-bar-score Quality tier: design-orchestration-gated Reasoning demand: standard

Quality bar:

  • Score each axis separately with named rendered evidence, then compute the weighted total; an overall impression is not a score and hides which axis is failing.
  • Reserve binding-constraint language for a total within about 0.3 of the threshold. Measured axis spread is roughly a twentieth of site spread, so further below the bar a weak axis is a symptom: report that the site needs a level change, never a one-axis fix.
  • Load references/award-judging-model.md for the published axes, weights, and thresholds, the measured per-axis score table, and the stack table that separates entry-fee craft (fluid type, real typography) from optional spend (WebGL).
  • Record what an innovation move costs on the accessibility and performance budgets before recommending it; half the sampled motion-heavy winners drop prefers-reduced-motion, and the two highest-scoring entries keep it, so never present the inaccessible path as the higher-scoring one.

Required inputs:

  • the target URL, route, or rendered capture being judged
  • the award model and its published axes, weights, and threshold
  • the surface's own accessibility and performance budgets
  • audience and primary user task

Expected outputs:

  • award_bar_score/v1
  • per-axis scores with named evidence for UI, UX, and innovation
  • the weighted total and its distance from the published threshold
  • the binding constraint: the axis whose gain moves the total most
  • tradeoff_ledger/v1 when an innovation move costs accessibility or performance budget
  • downstream route: frontend, accessibility-audit, visual-qa, or design-quality-gate

Artifact expectations:

  • award_bar_score/v1 with prepared_not_observed status
  • every axis score cites the rendered evidence it was read from, or stays not_observed
  • the weighted total is arithmetic over the stated weights, never an impression
  • no claim that a submission would win, place, or be selected

Safety rules:

  • A self-assessment against a published rubric is never an award, a jury outcome, or a prediction of one; juries score submissions, and OMH does not.
  • Never score an axis without rendered evidence; an unrendered page keeps every axis not_observed.
  • Quote axis weights and thresholds only from the award body's published rules, and name the body and the date they were read.
  • Accessibility and performance budgets outrank the innovation axis; when a move breaks one, record the tradeoff and let the user choose rather than defaulting to the score.
  • Do not call a browser, network service, screenshot tool, or executor from OMH core.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Report actual tool results or not_observed / not_available; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

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 Omh Award Bar Score AI skill do?

[omh] Hermes award-bar score workflow: score a web surface against published design-award judging axes and name the binding constraint. Use when the user says: award-bar-score, award bar score, award winning, award-winning, award winning website, award-winning website, award winning design, award ready.

Why use Omh Award Bar Score on TypingMind?

Because you install it once and use it with any model. Omh Award Bar Score 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 Omh Award Bar Score in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-award-bar-score. 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 Omh Award Bar Score?

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 Omh Award Bar Score?

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

Is the Omh Award Bar Score AI skill free?

Yes. It is published on GitHub by rlaope 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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