SKILLEMALL.ai
GitHub ↗ RU Sign in

BC threejs-lighting

Light types and physical units, shadow maps, HDR environment lighting, light probes, and lighting performance, on WebGL or with WebGPU and TSL. Use when adding or tuning lights, fixing shadow acne, setting up image-based lighting, or dimming an environment map.

CloudAI-X/threejs-skills Agent Skills author: CloudAI-X MIT 1 file body ≈ 5 776 tokens Open the sourcegithub.com↗ analyzed 4 h ago

Light types and physical units, shadow maps, HDR environment lighting, light probes, and lighting performance, on WebGL or with WebGPU and TSL.

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureSoftware developmenttype 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
C
50/100
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

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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

  • warning body-long SKILL.md body ≈ 5776 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 171, 460, 461, 470): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 50/100

  • 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. 2 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5776 tokens
  • 100Steps. 24 steps
  • 100Consistency. Name and required fields are in place
  • low 16 top-level sections: this looks like several domains in one skill

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 261: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 24 items
  • +4Has examples (22 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.