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BC gooey-blob-system

Create a gooey blob system using SVG filters where multiple shapes merge into a single fluid form. Use overlapping circles combined with a Gaussian blur and color matrix filter to produce a continuous, organic mass. The forms should visually fuse and separate based on proximity. Focus on filter-driven merging (blur + threshold effect), soft organic boundaries with no hard edges, multiple independent shapes behaving as one system, and smooth continuous motion that feels fluid and cohesive.

MengTo/Skills Agent Skills author: MengTo MIT 4 files body ≈ 205 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Create a gooey blob system using SVG filters where multiple shapes merge into a single fluid form.

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

Generatortype 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%
98
Quality 40%
80
Run on models
none yet
Process rating
C
58/100
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Obfuscation obf-base64-blob demo/index.html:19
      Long base64-looking blob (quoted — discussed, not commanded)
      const encodedHtml = "PCFE…bWw+Cjxo…YWQ+Cjxt…bmV
      quoted
    • low Secrets in code secret-high-entropy-token demo/index.html:19
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      const encodedHtml = "PCFE…bWw+Cjxo…YWQ+Cjxt…bmV
      quoted

    Files scanned: 4. 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 58/100

    • 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
    • 100Tools and files. No external tools needed
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 205 tokens
    • 100Running it twice. No mutating operations
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 493: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 9 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.