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BC dsh-responsive-ui

Use when designing, implementing, or reviewing responsive client layouts, mobile and touch detection, text truncation, title wrapping, icon-only controls, mobile hover, tooltips and keyboard behavior, and focus during width changes in DeepSeek Harness.

The skillemall take

Skill for responsive UI work in DeepSeek Harness: checks layouts, text truncation, title wrapping, icon-only controls, mobile hover, tooltips, and focus behavior during width changes. Single file, 745 tokens, no scripts.

Scores: quality 84, process 57, safety 100. No critical errors, linter passes. Models untested, sandbox unused. Supports many platforms from Claude to OpenClaw.

Install if you need UI pattern reference for Harness. Don't expect a full toolkit with examples.

deepseek-ai/deepseek-harness Agent Skills author: deepseek-ai MIT 1 file body ≈ 1 030 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Use when designing, implementing, or reviewing responsive client layouts, mobile and touch detection, text truncation, title wrapping, icon-only controls…

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmentData and analyticsAI and agentstype 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
54/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
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: 1. 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 54/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 85Steps. 24 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1030 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 252: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 24 items
    • +4Has examples (0 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.