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AB email-deliverability

Make sure email actually reaches inboxes. Use this skill when setting up email authentication (SPF, DKIM, DMARC), diagnosing emails landing in spam, planning a domain reputation strategy, monitoring sender reputation, or hardening against email spoofing. Triggers on email deliverability, SPF, DKIM, DMARC, spam folder, sender reputation, mailbox provider, soft bounces, bounce rate, BIMI, MTA-STS, deliverability audit. Also triggers when a marketing or transactional email isn't reaching users.

rampstackco/claude-skills Agent Skills author: rampstackco 3 files body ≈ 2 706 tokens Open the sourcegithub.com↗ analyzed 7 h ago

Make sure email actually reaches inboxes.

As a process B 79/100 · Nearly there — weak spots: running it twice, progress reporting

GeneratorGmailOperations and projectstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
B
79/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Failures and branches w 10
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: email-deliverability (rampstackco/claude-skills)

How to improve

    For the model run — optional
    • 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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "catalog_summary"
    • note frontmatter-key unknown frontmatter key "display_order"

    Process rating: all ten parameters 79/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 44 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 95 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2706 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 496: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 95 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.