BC linkedin-ads-performance-report
Use for "how are my LinkedIn Ads doing", "why did my LinkedIn cost per lead go up", "which LinkedIn campaigns improved this month", "what happened to my LinkedIn Ads last week", "is my LinkedIn spend working", or a weekly or monthly LinkedIn Ads account check — even when the user never says "review". Also use when someone wants to know what changed in the LinkedIn account and why, or needs the baseline read before deciding anything else. LinkedIn Ads only.
Use for "how are my LinkedIn Ads doing", "why did my LinkedIn cost per lead go up", "which LinkedIn campaigns improved this month", "what happened to my…
As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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 text contains phrases like "ignore previous instructions" or "you are now…". That is an attempt to hijack the agent: it may break your rules, the system limits or company policy.
An honest skill does not need them: state the role and the rules directly without overriding other instructions. Otherwise catalog scanners and corporate filters will block the listing.
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
-
medium Instruction override
en-ignore-previousSKILL.md:211Instruction-override phrase ("ignore previous instructions") (quoted — discussed, not commanded)campaign called "ignore previous instructions" is a string of text.
quoted
Files scanned: 1. 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 57/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 19 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3704 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 12 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +3Description length 460: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 19 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.