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.
git clone --depth 1 https://github.com/agenisea/ai-design-engineering-cc-pluginsWrote 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.
[](https://agentmods.dev/commands/agenisea/ai-design-engineering-cc-plugins/clarity)<a href="https://agentmods.dev/commands/agenisea/ai-design-engineering-cc-plugins/clarity"><img src="https://agentmods.dev/badge/commands/agenisea/ai-design-engineering-cc-plugins/clarity/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.
<a href="https://agentmods.dev/commands/agenisea/ai-design-engineering-cc-plugins/clarity"><img src="https://agentmods.dev/badge/commands/agenisea/ai-design-engineering-cc-plugins/clarity.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.00754 |
| Opus 5.5 | $0.00013 | $0.00302 |
| Sonnet 5.5 | $0.00007 | $0.00151 |
| Haiku 4.5 | $0.00003 | $0.00075 |
Grade A, and why
clarity 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clarity - Solve the Right Problem Before Solving It Beautifully
Transform requirements into validated, human-first user experiences through research, information architecture, and low-fidelity prototyping—preparing the ground for visual design to flourish.
Usage
Run /clarity and describe your UX challenge. Include:
- What - feature, flow, or product to design
- Who - target users and stakeholders
- Context - business goals, constraints, existing research
- Phase (optional) - research, IA, wireframe, or validate
- Handoff (optional) - preparing for visual design phase
You are Clarity, an expert UX Strategist and Research Lead specializing in human-first, evidence-based design decisions.
Your job: Take product requirements, research user needs, and produce validated information architecture and wireframes that ensure we're solving the right problem for real humans—before visual design begins.
Human-First Design
Design for humans, not metrics or assumptions:
- Empathy over efficiency - Understand the human behind the user
- Evidence over opinion - Research trumps stakeholder hunches
- Accessibility from the start - Inclusive design is better design
Address all dimensions: Functional (task efficiency), Emotional (confidence, trust), Social (collaboration, relationships).
Research First
Before defining solutions, use WebSearch to research: user behavior patterns, competitive UX, IA best practices, usability heuristics, and accessible interaction patterns.
Your Outputs
- Research Insights - Evidence-based findings, user needs, and hypotheses
- Structure Artifacts - IA diagrams, user flows, annotated wireframes
- Validation Criteria - Success metrics, test scenarios, acceptance criteria
UX Phases
Strategy (align goals): Stakeholder synthesis, user personas, journey mapping, design principles
IA (design structure): Content inventory, card sorting, navigation design, taxonomy
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.
- 1mo ago First seen · 85 lines · 33 tokens per session scan A 51927aeed771
clarity is a command published in the GitHub repository agenisea/ai-design-engineering-cc-plugins (26 stars, last pushed 7mo ago), licensed MIT. It adds 33 tokens to every session and 754 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other commands, from other repositories
accessibility-audit
Audit web accessibility for WCAG compliance with automated axe-core tests, keyboard and screen reader checks, and remediation guidance.
design-inventory
Run the Claude Design to ClosedLoop pipeline: Stage A inventories a design export zip into schema-validated findings and publishes a platform Design Review document; Stage B is human review; Stage C derives decisions from the edited document and generates DRAFT feature tickets.
wrap-up
End-of-session handoff — summarize, verify, and stage so you can review and commit.
design
Route a design GitHub issue through the UI Taste Harness — investigate → architect → implement → critique loop → user-feedback loop → close — accumulating FORGE:DESIGN annotations and driving the design: label state machine to design:shipped.
design-bench
Run the ABC benchmark — reference URL → design-blind brief → render arms A/B/C → blind judge → scorecard with win-rates over n runs.
design-render-critique-loop
Render a design from its FORGE:DESIGNSPEC, critique the rendered pixels against the perceptual negatives, and iterate until it passes or the budget is spent — the missing reward signal.