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AA clawcall

Run AI-powered outbound phone calls with Telnyx + Deepgram Voice Agent. Use when the user wants real phone outreach (follow-ups, confirmations, reminders, callbacks) with configurable personality, task context, model, and voice.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 1 494 tokens Open the sourcegithub.com↗ analyzed 33 h ago

Run AI-powered outbound phone calls with Telnyx + Deepgram Voice Agent.

As a process A 82/100 · Runs to the end — weak spots: running it twice

ProcedureAI and agentsSales 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
92/100
safety, quality, tests
Safety 60%
95
Quality 40%
87
Run on models
none yet
Process rating
A
82/100
Runs to the end
Running it twice w 4
30
Result and completion w 14
60
When it triggers w 12
70
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token package-lock.json:80
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…U1l+3HIg…YhQ==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:173
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…YZ4+PnKB…IK5/YkuhRqPJl/9FivKEw==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:290
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…FFX/+gVeY…NlM++NqRc…bqg==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:501
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…FrF+LTRo…W3g==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:510
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…A6j+hAmM…GbS+kf5c…csw==",
      detector

    Files scanned: 4. 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 82/100

    • 30Running it twice. 2 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 31 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1494 tokens
    • 100Progress reporting. Reports progress

    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 228: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 31 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)

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