AC remove-ai-watermarks
Removes and identifies AI watermarks and provenance marks in images and video the user generated or edited. Covers Gemini sparkles, SynthID, Microsoft InvisMark, Meta Content Seal, C2PA, EXIF/XMP/IPTC metadata, and Sora/Veo/Seedance/Doubao/Dola/Hailuo/Kling labels. Use when the user wants to strip or clean AI watermarks and generation labels, or asks whether a file was AI-generated or carries AI provenance, even if they do not name remove-ai-watermarks. Do not use for stock-agency, marketplace, classifieds, or other third-party paid-asset watermarks.
Removes and identifies AI watermarks and provenance marks in images and video the user generated or edited.
As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions
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 · 0
✓ No critical or high findings
Files scanned: 5. 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 64/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 100Steps. 20 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 2693 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 556: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.