SKILLEMALL.ai
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AC md-slides

Use when converting a markdown deck into a single-file HTML presentation with keyboard navigation, presenter notes and print-to-PDF. Splits slides with slide_splitter.py and renders with deck_html_renderer.py.

The skillemall take

Converts markdown decks into single-file HTML presentations with keyboard controls, speaker notes, and PDF export. Uses slide_splitter.py to parse slides and deck_html_renderer.py to render output.

Grade A, quality score 87, process score 59. No critical issues, lint passes, all references intact. Eight files, three scripts—lean setup. Runs on Claude, Cursor, OpenClaw, and eight other platforms.

Install if you need markdown-to-presentation conversion without fuss. No red flags, safety at 100%, ready to use.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI 8 files · 3 scripts body ≈ 1 348 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Splits slides with slidesplitter.py and renders with deckhtmlrenderer.py.

As a process C 59/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

GeneratorPowerPointInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 0

    ✓ No critical or high findings

    Files scanned: 8. 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 59/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 33 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1348 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +3Description length 209: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 33 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +3All 3 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.