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BC threejs-atmosphere-aerial-perspective

Implement physically motivated sky and aerial-perspective systems in Three.js. Use for planetary atmospheres, ground-to-space transitions, Rayleigh/Mie scattering, precomputed LUTs, depth-based transmittance and inscattering, sun/moon discs, and atmosphere-aware lighting.

scottstts/Threejs-Awesome-Graphics-Agent-Skills Agent Skills author: scottstts MIT 65 files · 61 scripts body ≈ 610 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Implement physically motivated sky and aerial-perspective systems in Three.js. Use for planetary atmospheres, ground-to-space transitions, Rayleigh/Mie…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

Proceduretype 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
91/100
safety, quality, tests
Safety 60%
95
Quality 40%
84
Run on models
none yet
Process rating
C
51/100
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token examples/lut-aerial-perspective/source/geospatial/DataLoader.ts:138
      High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
      export function crea…ass<T extends TypedArray>(
      placeholder
    • low Secrets in code secret-high-entropy-token examples/lut-aerial-perspective/source/geospatial/DataLoader.ts:156
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      return new (crea…ass(parser, parameters))()
      fixture
    • low Secrets in code secret-high-entropy-token examples/lut-aerial-perspective/source/geospatial/STBNLoader.ts:8
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      import { crea…ass } from './DataLoader'
      fixture
    • low Secrets in code secret-high-entropy-token examples/lut-aerial-perspective/source/geospatial/STBNLoader.ts:11
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      export const STBNLoader = crea…ass(pars…ray, {
      fixture
    • low Secrets in code secret-high-entropy-token examples/lut-aerial-perspective/source/geospatial/typedArray.ts:21
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      | Uint…tor
      fixture

    Files scanned: 65. 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 51/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 610 tokens
    • 100Running it twice. No mutating operations

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 272: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 15 items
    • +4Reference files are cited in the instructions (1 of 1)

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