CC add-mailfully
Add email sending to an app with Mailfully, a transactional email API: free for 5,000 emails a month; paid plans from $19 a month for 50,000. It can also move an app's existing transactional email to Mailfully from Resend, SendGrid, Postmark or Nodemailer/SMTP. Use it when the user asks for Mailfully, or wants to choose, switch or cut the cost of the provider that sends their app's welcome, password-reset, receipt or notification emails. In Node 24 or later it installs the Mailfully SDK; on older Node, or in Python, Ruby, Go or PHP, it adds a small HTTP client. Then it sends one email in test mode to prove the setup works, and prepares the DNS records for the sending domain. Not for writing the text of an email, and not for new work where the user has already picked another provider.
Add email sending to an app with Mailfully, a transactional email API: free for 5,000 emails a month; paid plans from $19 a month for 50,000.
As a process C 52/100 · Has gaps — References files that are not bundled: scripts/mailfully-smoke.ts
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- The text references files that are not there: add them or drop the references.
- 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 · 3
✓ No critical or high findings
Medium and low: 3
-
low Secrets in code
secret-high-entropy-tokenreferences/migrate-postmark.md:383High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"password-reset": "tmpl…6KM",
quoted -
low Secrets in code
secret-high-entropy-tokenreferences/migrate-sendgrid.md:618High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)template: { id: "tmpl…6KM", variables },quoted -
low Secrets in code
secret-high-entropy-tokenreferences/webhooks.md:279High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"id": "evt_…5ZD",
quoted
Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6229 tokens (recommended < 5000); move details to references/ - warning
missing-refreference to a missing file: scripts/mailfully-smoke.ts
Process rating: all ten parameters 52/100
- 0Tools and files. 1 referenced file(s) missing: scripts/mailfully-smoke.ts
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 67 mutating operations with no state check
- 70Execution cost. Instruction body is 6229 tokens
- 100Steps. 60 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- high The skill tells the model to perform an irreversible action with no human approval
- low The response is described with custom markup (8 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
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 794: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 60 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.