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AB outboundsync-analysis

Analyze outbound campaign performance, reply rates, open-to-reply conversion, follow-up prioritization, platform attribution, and deliverability using OutboundSync engagement signals in HubSpot or Salesforce. Use this skill when someone asks about campaign replies, which sequences are working, who to follow up with, bounce or unsubscribe trends, or how platforms like Instantly, Smartlead, EmailBison, or HeyReach are performing. Also handles exploratory HeyReach social signal analysis. Read-only, local-only, deterministic preflight routing.

modbender/skill-library-mcp Agent Skills author: modbender MIT 16 files body ≈ 1 733 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Analyze outbound campaign performance, reply rates, open-to-reply conversion, follow-up prioritization, platform attribution, and deliverability using…

As a process B 72/100 · Nearly there — weak spots: when it triggers, progress reporting

AnalyzerSalesforceHubSpotGitHubSales and CRMtype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
72/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Tools and files w 18
60
the three weakest of ten parameters · all ten

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: 16. 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 72/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 82 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1733 tokens
    • 100Running it twice. Mutating operations check current state
    • low 12 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

    • +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 545: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 82 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.