Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vocalbridgeai/vocal-bridge-claude-plugin --skill evalgit clone --depth 1 https://github.com/vocalbridgeai/vocal-bridge-claude-pluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vocalbridgeai/vocal-bridge-claude-plugin/eval)<a href="https://agentmods.dev/skills/vocalbridgeai/vocal-bridge-claude-plugin/eval"><img src="https://agentmods.dev/badge/skills/vocalbridgeai/vocal-bridge-claude-plugin/eval.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00060 | $0.00767 |
| Opus 5 | $0.00030 | $0.00383 |
| Sonnet 5 | $0.00012 | $0.00153 |
| Haiku 4.5 | $0.00006 | $0.00077 |
Grade A, and why
eval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate a Vocal Bridge call recording with a multimodal LLM. Returns a structured score, verdict, and concrete suggestions for improving the agent's prompt.
First ensure CLI is installed and up to date:
pip install --upgrade vocal-bridge
Usage
vb eval <session_id> [--objective "..."] [--scenario "..."] [--objective-file FILE] [--scenario-file FILE] [--json]
The evaluator model is fixed by the platform.
Examples
# Basic eval against the agent's own system prompt
vb eval 550e8400-e29b-41d4-a716-446655440000
# With a specific objective
vb eval <session_id> --objective "Schedule an interview for next Tuesday"
# With both an objective and an expected scenario
vb eval <session_id> \
--objective "Confirm the candidate's availability" \
--scenario "The user is busy and tries to reschedule twice"
# Read long objective/scenario from files
vb eval <session_id> --objective-file objective.txt --scenario-file scenario.txt
# Raw JSON output (for piping into another tool)
vb eval <session_id> --json
What gets sent to the evaluator
- Full audio recording of the call (inline, up to ~18 MB)
- Agent's current system prompt (from
vb config show) - Caller-supplied
--objectiveand--scenario(optional) - Structured transcript with the agent's tool calls
- Client action events log (heartbeats and custom bidirectional events between the agent and the client app)
- Raw session report (for troubleshooting context)
The evaluator uses the audio as the primary source of truth for tone, interruptions, and timing, and uses the structured logs to verify what tools/actions actually fired.
Output
Call Evaluation
----------------------------------------
Session: 550e8400...
Score: 7/10
Verdict: partial
Summary:
The agent answered the user's questions accurately but missed
the scheduling objective when the user asked to reschedule.
What worked:
+ Greeted the caller naturally
+ Recovered cleanly from a mid-sentence interruption
What didn't:
- Did not call schedule_meeting tool when the user gave a date
- Tone became impatient on the second reschedule attempt
Suggested prompt improvements:
Add an explicit instruction to call schedule_meeting whenever
the user proposes any time, including reschedules.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 96 lines · 60 tokens per session scan A 7eae27b22647
eval is a skill published in the GitHub repository vocalbridgeai/vocal-bridge-claude-plugin (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 60 tokens to every session and 767 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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