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AC ux-friction-analyzer

Comprehensive UX analysis using cognitive psychology, ADHD-friendly design, Gestalt principles, and flow state engineering. Specializes in friction audits, user journey simulation, cognitive load optimization, and Fitts' Law application. Activate on "analyze UX", "friction audit", "user journey", "ADHD-friendly", "optimize flow", "reduce cognitive load", "UX audit", "conversion optimization". NOT for visual design execution (use web-design-expert), A/B testing implementation (use frontend-developer), or accessibility compliance auditing (use accessibility-auditor).

curiositech/some_claude_skills Agent Skills author: curiositech 2 files body ≈ 3 230 tokens Open the sourcegithub.com↗ analyzed 39 min ago

Comprehensive UX analysis using cognitive psychology, ADHD-friendly design, Gestalt principles, and flow state engineering.

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerDesigntype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 59/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 104 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3230 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 571: enough signal without eating the budget
    • +4Structure: 27 headings
    • +3Step-by-step instructions: 104 items
    • +4Has examples (9 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.