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BC mermaid-graph-renderer

Renders Mermaid diagrams to SVG, PNG, and PDF in both web and offline contexts. Covers client-side lazy loading, SSR trade-offs, CLI batch export, and native Rust rendering. Use when exporting diagrams to images, optimizing Mermaid rendering performance on web pages, setting up CI/CD diagram pipelines, or choosing a rendering library. Activate on "render mermaid", "export diagram", "mermaid to png", "mermaid to svg", "mermaid performance", "diagram export". NOT for writing Mermaid syntax (use mermaid-graph-writer), general image processing, or non-Mermaid diagram formats.

curiositech/some_claude_skills Claude Code author: curiositech 3 files body ≈ 2 210 tokens Open the sourcegithub.com↗ analyzed 39 min ago

Renders Mermaid diagrams to SVG, PNG, and PDF in both web and offline contexts.

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorDockerPlaywrightInfrastructuretype 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%
95
Quality 40%
93
Run on models
none yet
Process rating
C
57/100
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Read Write Edit Bash

    Files scanned: 3. 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 57/100

    • 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. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 85Steps. 12 steps, 2 vague phrases
    • 100Tools and files. Tools declared in frontmatter
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2210 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

    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 578: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.