SKILLEMALL.ai

CC pptx

Use this skill any time a presentation file is the primary input or output. Covers: creating new .pptx decks from scratch or from an outline/document (pptxgenjs / python-pptx); editing and restyling an existing .pptx; reading and extracting text/structure from a deck (markitdown); and rendering a deck to PDF (LibreOffice) or to per-slide PNG images (pdftoppm) for previews, thumbnails, or visual review. Trigger when the user references a .pptx file by name or path, says 'make/build a deck, slides, presentation, PPT, 幻灯片, 演示文稿, 汇报/路演材料', or asks to convert, export, render, or preview slides as PDF or images (e.g. 'PPT转PDF', 'ppt导出图片', 'turn these slides into a PDF').

asgeirtj/system_prompts_leaks Agent Skills author: asgeirtj 1 file body ≈ 13 274 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Use this skill any time a presentation file is the primary input or output.

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, execution cost

AnalyzerPowerPointSoftware developmentWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
60/100
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 ≈ 13274 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 60/100

  • 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
  • 40Execution cost. Instruction body is 13274 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
  • 85Steps. 137 steps, 2 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 28 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (7 tags): a typed call is more reliable

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
  • -5TODO / placeholder text left in the skill
  • -228 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 673: enough signal without eating the budget
  • +4Structure: 34 headings
  • +3Step-by-step instructions: 137 items
  • +4Has examples (18 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.