BD itinerary-carousel-post
Create and publish an Instagram carousel post from a tabiji.ai itinerary. Given an itinerary URL, finds Instagram-worthy photos for the destination + top attractions, applies text overlays, and publishes as a carousel. Use when asked to create an Instagram post, carousel, or social content for a tabiji destination or itinerary.
Create and publish an Instagram carousel post from a tabiji.ai itinerary. Given an itinerary URL, finds Instagram-worthy photos for the destination + top…
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
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.
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 · 4
✓ No critical or high findings
Medium and low: 4
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medium Exfiltration
net-credential-useSKILL.md:94Credential used in a network call (verify the destination is the intended service)curl -s "https://graph.facebook.com/v21.0/${POST_ID}?fields=…&access_token=…" -
medium Exfiltration
net-credential-useSKILL.md:99Credential used in a network call (verify the destination is the intended service)curl -s "https://graph.facebook.com/v21.0/${IG_USER}/media?fields=…&limit=1&access_token=…" -
low Exfiltration
exfil-secret-in-urlSKILL.md:94Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)curl -s "https://graph.facebook.com/v21.0/${POST_ID}?fields=…&access_token=…"placeholder -
low Exfiltration
exfil-secret-in-urlSKILL.md:99Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)curl -s "https://graph.facebook.com/v21.0/${IG_USER}/media?fields=…&limit=1&access_token=…"placeholder
Files scanned: 2. 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 49/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 13 mutating operations with no state check
- 60Tools and files. Uses tools (web, git) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1454 tokens
- high The skill tells the model to perform an irreversible action with no human approval
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
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 329: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.