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BB genomics-viz

Builds interactive D3 visualizations of genetics and genomics concepts for the vault's docs/ GitHub Pages site — each one a teaching object with a takeaway-stating title, an annotation layer, and a guided reading order. Specializes d3-visualization, visual-storytelling-design, and cognitive-design for the crop-genetics / genomic-selection domain, supplying a concept-to-visualization catalog, preattentive encoding guidance, colorblind-safe palettes, accessibility requirements, and a fixed docs/ file layout. Delegates design review to the cognitive-design-architect agent and runs design-evaluation-audit and cognitive-fallacies-guard before publish. Use when turning an evergreen claim into an interactive figure, building or revising a docs/ visualization, or planning the site's viz layout. Trigger keywords — D3 viz, interactive figure, GitHub Pages chart, Hardy-Weinberg, Manhattan plot, allele drift, LD decay, breeder's equation, genomic prediction accuracy, reaction norm, kinship heatmap.

lyndonkl/claude Agent Skills author: lyndonkl 1 file body ≈ 2 494 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Builds interactive D3 visualizations of genetics and genomics concepts for the vault's docs/ GitHub Pages site — each one a teaching object with a…

As a process B 66/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, running it twice

GeneratorGitHubAI and agentsLearningtype 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
B
66/100
Nearly there
Inputs and preconditions w 11
0
When it triggers w 12
20
Running it twice w 4
30
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 66/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 24 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2494 tokens
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    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)
    • +3Description length 1001: 120–800 characters recommended
    • +1No license
    • +2Single-language instructions
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 24 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)

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