heuristic-evaluation-ai

heuristic-evaluation-ai is a skill for Claude Code from Owl-Listener/ai-design-skills. It costs 23 tokens per session (672 once invoked), scanned A, original, MIT.

A usability review method for AI interfaces that adapts Nielsen’s ten guidelines for checking traditional software usability. It also considers AI issues such as uncertainty, generated responses, and user control.

In plain words
What is it for?
It is for evaluating AI products and conversational interfaces for clarity, feedback, consistency, error prevention, transparency, and user control.
Why use it?
It helps reveal when an AI product hides its status, uses confusing language, behaves inconsistently, or makes actions hard to stop or undo.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the evaluation plugin — 7 skills, 3 commands shipped together

Good fit It is for evaluating AI products and conversational interfaces for clarity, feedback, consistency, error prevention, transparency, and user control.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/owl-listener/ai-design-skills/heuristic-evaluation-ai
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Owl-Listener/ai-design-skills --skill heuristic-evaluation-ai
Clone the repo
git clone --depth 1 https://github.com/Owl-Listener/ai-design-skills

Made for: Claude Code.

Or install evaluation, the plugin that ships this one along with the rest of its 7 skills, 3 commands.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for heuristic-evaluation-ai

README.md
[![agentmods](https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/heuristic-evaluation-ai/github.svg)](https://agentmods.dev/skills/owl-listener/ai-design-skills/heuristic-evaluation-ai)
Your own site
<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/heuristic-evaluation-ai"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/heuristic-evaluation-ai/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for heuristic-evaluation-ai

Your own site · 80×15
<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/heuristic-evaluation-ai"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/heuristic-evaluation-ai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 672 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00023 $0.00672
Opus 5 $0.00012 $0.00336
Sonnet 5 $0.00005 $0.00134
Haiku 4.5 $0.00002 $0.00067

Measured 12d ago against content hash 83642665904c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

heuristic-evaluation-ai scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

claude-plugin/evaluation/skills/heuristic-evaluation-ai/SKILL.md · 47 lines

How it starts

The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Heuristic Evaluation for AI

Nielsen's 10 usability heuristics were designed for traditional software. AI products need adapted heuristics that address the unique challenges of probabilistic, generative, and conversational systems.

Classic Heuristics, Adapted for AI

1. Visibility of system status AI adaptation: The user should always know what the AI is doing, what it's working with, and how confident it is. Progress indicators for generation. Transparency about data sources. 2. Match between system and real world AI adaptation: The AI should use language and concepts the user understands. Don't expose model internals. Frame capabilities in terms of user tasks, not technical features. 3. User control and freedom AI adaptation: Users must be able to stop generation, undo AI actions, edit outputs, and override suggestions. AI autonomy should always have an exit. 4. Consistency and standards AI adaptation: The AI should behave consistently across similar requests. Same input type should produce same output format. Persona should be stable. 5. Error prevention AI adaptation: Design prompts and interfaces that guide users toward effective interactions. Suggest clarifications before producing low-quality output. 6. Recognition rather than recall AI adaptation: Show users what the AI can do rather than requiring them to discover commands. Surface relevant capabilities contextually. 7. Flexibility and efficiency of use AI adaptation: Support both novice (guided) and expert (shortcut) interaction modes. Power users should be able to customise AI behavior. 8. Aesthetic and minimalist design AI adaptation: AI outputs should be concise and well-structured. Don't pad responses with unnecessary caveats or filler. 9. Help users recognise, diagnose, and recover from errors AI adaptation: When the AI fails, explain what went wrong in user terms, not technical terms. Offer clear recovery paths. 10. Help and documentation AI adaptation: Provide contextual guidance on how to interact with the AI effectively. Teach prompting skills through the interface.

Read the full file on GitHub · 47 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 47 lines · 23 tokens per session scan A 83642665904c

Subscribe to this mod's changes

heuristic-evaluation-ai is a skill published in the GitHub repository Owl-Listener/ai-design-skills (173 stars, last pushed 3mo ago), licensed MIT. It adds 23 tokens to every session and 672 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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