SKILLEMALL.ai
GitHub ↗ RU Sign in

BB sendgrid-automation

Automate SendGrid email delivery workflows including marketing campaigns (Single Sends), contact and list management, sender identity setup, and email analytics through Composio's SendGrid toolkit.

The skillemall take

The skill promises SendGrid workflow automation: campaigns, contact management, sender setup, and analytics via Composio integration. Files contain core operations: Single Sends, list management, identity configuration, reporting. Checks show grade B (quality 84, process 67), no critical issues or safety concerns.

In practice, it handles SendGrid routine at agent level—sends campaigns, updates contacts, pulls metrics hands-free. Works across all major platforms from Claude to DeepSeek. Install if SendGrid is your primary channel and you want automation without UI clicks.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 1 file body ≈ 3 518 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Automate SendGrid email delivery workflows including marketing campaigns (Single Sends), contact and list management, sender identity setup, and email…

As a process B 67/100 · Nearly there — weak spots: result and completion, when it triggers, running it twice

ProcedureData and analyticsMarketingAI and agentstype 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
B
67/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: agentic-awesome-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: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 77, 172, 174, 185, 189, 190): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 67/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 159 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 110 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3518 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 197: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 110 items
    • +4Has examples (0 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.