BC meta-ads-analysis
[Didoo AI] Analyzes Meta Ads campaign performance in depth — metrics, funnel, trends, and anomalies. Use when user wants to understand how a campaign is performing, identify weak points, or get data before receiving recommendations. Outputs structured analysis only; never provides recommendations. Triggers on: "analyze", "deep dive", "why is performance", "diagnose", "full audit".
[Didoo AI] Analyzes Meta Ads campaign performance in depth — metrics, funnel, trends, and anomalies.
As a process C 59/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice
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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 59/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 2 mutating operations with no state check
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 57 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2206 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 15 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 5 example trigger phrases
- +3Description length 383: enough signal without eating the budget
- +4Structure: 22 headings
- +3Step-by-step instructions: 57 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.