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AC infographic-from-photo

Drop in any photo — plant, sneaker, mushroom, cocktail tool — and get a clean editorial infographic with the subject identified and explained. How it works: 1. Identifies the subject from your photo automatically 2. Picks 4 to 6 key facts the audience would want to know 3. Lays out a hero shot with fact modules and icons 4. Adds common name, scientific name, and origin labels 5. Outputs a 4:5 vertical infographic ready to share

genspark Agent Skills author: Genspark 1 file body ≈ 7 983 tokens Open the sourcegenspark.ai↗ analyzed 3 d ago

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, running it twice

ProcedureAI and agentsMarketingtype and topics are labelled automatically from the skill text
Runs in: Genspark 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
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
When it triggers w 12
50
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 ≈ 7983 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "triggers"

Process rating: all ten parameters 63/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 11 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 7983 tokens
  • 85Steps. 82 steps, 1 vague phrases
  • 100Failures and branches. 6 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 18 top-level sections: this looks like several domains in one skill

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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 432: enough signal without eating the budget
  • +4Structure: 36 headings
  • +3Step-by-step instructions: 82 items
  • +3Output format is stated explicitly
  • +4Has examples (15 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.