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CB pptx-import

会话里有上传的 .pptx，且用户要把这份幻灯片填进一节已建的课（先 create_stage 建课，再 import_pptx 把页面追加进这节课堂，保留原版排版；课堂标题以 create_stage 为准，PPT 不接管）。随后逐页检查（有 render_scene_preview 就渲染看）并修 bad case，理解整课之后才写旁白/spotlight，最后 TTS。仅在用户要求时插入 quiz 或 interactive。不要重新规划整课。

THU-MAIC/OpenMAIC Agent Skills author: THU-MAIC MIT 2 files body ≈ 2 398 tokens Open the sourcegithub.com↗ analyzed 6 d ago

会话里有上传的 .pptx，且用户要把这份幻灯片填进一节已建的课（先 createstage 建课，再 importpptx 把页面追加进这节课堂，保留原版排版；课堂标题以 createstage 为准，PPT 不接管）。随后逐页检查（有 renderscenepreview 就渲染看）并修 bad…

As a process B 69/100 · Nearly there — weak spots: result and completion, inputs and preconditions

GeneratorPowerPointLearningtype 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
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
50
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "title"

Process rating: all ten parameters 69/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 50When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 44 steps
  • 100Failures and branches. 3 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2398 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress

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 227: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 44 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.