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AB ringg-voice-agent

Integrate Ringg AI voice agents with OpenClaw for making, receiving, and managing phone calls powered by Ringg's Voice OS. Use this skill when the user wants to: (1) make outbound voice calls via Ringg AI agents, (2) trigger Ringg AI campaigns from OpenClaw, (3) check call status or retrieve call history/analytics from Ringg, (4) manage Ringg AI assistants (list, create, update), (5) connect OpenClaw to Ringg's voice platform for automated phone interactions like lead qualification, feedback collection, appointment reminders, or order confirmations, (6) set up Ringg AI as a voice provider for the OpenClaw agent. Triggers on mentions of "ringg", "voice call", "phone call via ringg", "ringg agent", "ringg campaign", "voice AI call", or any request to initiate/manage calls through the Ringg AI platform.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 1 478 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Integrate Ringg AI voice agents with OpenClaw for making, receiving, and managing phone calls powered by Ringg's Voice OS.

As a process B 69/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

IntegrationAI and agentsData and analyticsMarketingtype 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%
99
Quality 40%
90
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration exfil-webhook-url SKILL.md:164
      Webhook / callback URL commonly used for exfiltration (verify the destination) (placeholder value)
      "url": "https://your-ngrok-url.ngrok.io/webhook/ringg",
      placeholder

    Files scanned: 2. 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 69/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 16 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1478 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 811: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.