BC hubspot-email
Create, convert, personalize, and validate HubSpot marketing email HTML. Use when creating emails, aligning existing frames with HubSpot, adding personalization tokens, or exporting HTML, including follow-ups.
Promises to build and edit HubSpot emails: convert frames, inject personalization tokens, validate HTML, prepare follow-ups. One instruction file, 2337 tokens, no scripts, no critical errors. Quality score 76 (passable), process score 58 (weak), zero medium/high findings. Runs on all major platforms from Claude to DeepSeek.
In practice: handles basic HTML and HubSpot token operations, but no sandbox or model runs leave room for doubt about platform-specific edge cases. Low process score hints at reproducibility gaps. Worth installing if you need email markup help, but expect to debug outputs.
Create, convert, personalize, and validate HubSpot marketing email HTML.
As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, 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: 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 58/100
- 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. 3 mutating operations with no state check
- 85Steps. 6 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2326 tokens
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)
- +4Structure: 2 headings, hard to scan
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 209: enough signal without eating the budget
- +3Step-by-step instructions: 6 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.