CB fotor-skills
Fotor AI image generator and AI video generator for photo editing, background remover, background replacement, product photos, ad creatives, social media graphics, poster and banner design, image upscaling, photo restoration, portrait enhancement, text-to-video, and image-to-video. Built for e-commerce, marketing, branding, and content creation.
As a process B 66/100 · Nearly there — weak spots: result and completion, inputs and preconditions
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.
Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 5
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high Concealment
en-hide-from-userSKILL.md:227Instruction to hide actions from the user5. After `.venv` is ready and `ensure_sdk.py` completes, silently run `./.venv/bin/python scripts/check_skill_update.py --mark-notified --check-interval-hours 24`. Do not inspect the state file manual
Medium and low: 4
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medium Exfiltration
net-redirectable-api-keyscripts/run_task.py:184Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
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medium Exfiltration
net-redirectable-api-keyscripts/upload_image.py:95Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
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medium Dangerous commands
cmd-pipe-to-shell-known-hostSKILL.md:72Pipe-to-shell installer from a well-known host (still executes remote code)curl -LsSf https://astral.sh/uv/install.sh | sh
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medium Dangerous commands
cmd-execpolicy-bypassSKILL.md:75Runs PowerShell with execution policy bypassedpowershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
Files scanned: 16. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 66/100
- 0Result and completion. Does not say what the result is
- 30Inputs and preconditions. Does not say what the process needs to start
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 4034 tokens
- 100Steps. 96 steps
- 100Failures and branches. 12 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low 12 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (3 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
- +1No license
- +2Single-language instructions
- +3Description length 347: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 96 items
- +4Has examples (12 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.