AB pixelmsg
Render HTML templates to pixel-perfect PNG image cards using Playwright and send them as rich image messages — instead of plain text. Use this skill whenever the user asks for a visual card, image, dashboard, widget, or styled message — including weather cards, GitHub stats, todo boards, reports, daily digests, announcements, and data summaries. Also trigger when the user says things like "给我做个图", "做张卡片", "发图片", "图片形式展示", "用图片显示", "rich message", "beautiful card", or anytime a visual representation would be more memorable or polished than a plain-text reply. When in doubt, prefer generating an image — it almost always delights more than text.
As a process B 66/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- 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
-
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:14High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…uWm+fFRcIOgKBMiOBP+eXiy…9ab+DDKA==",
detector
Files scanned: 14. 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 66/100
- 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
- 55Failures and branches. 1 branches
- 100Tools and files. No external tools needed
- 100Steps. 32 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2248 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
- +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
- -4Absolute local paths (C:\Users, /home/…): not portable
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 650: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 32 items
- +4Has examples (14 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.