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 CALLE-AI/awesome-phone-call-agents --skill call-prosodic-entrainment-optimizergit clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agentsWrote 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/calle-ai/awesome-phone-call-agents/call-prosodic-entrainment-optimizer)<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-prosodic-entrainment-optimizer"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-prosodic-entrainment-optimizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-prosodic-entrainment-optimizer"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-prosodic-entrainment-optimizer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00041 | $0.02122 |
| Opus 5.5 | $0.00016 | $0.00849 |
| Sonnet 5.5 | $0.00008 | $0.00424 |
| Haiku 4.5 | $0.00004 | $0.00212 |
Grade A, and why
call-prosodic-entrainment-optimizer 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 1mo 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prosodic Entrainment Optimizer
Vocal entrainment is the natural, subconscious phenomenon where people synchronize their speech patterns — pitch, pace, rhythm, and intensity — with their conversation partner. This synchronization is one of the most robustly documented proxies for rapport, trust, and cooperative intent in human communication, first formalized by Howard Giles' Communication Accommodation Theory (1973) and now computationally validated through deep learning (Nasir et al., IEEE TAFFC 2022).
The supplied Python helper is an offline, stateless heuristic over supplied numeric features. It returns a score and suggested TTS deltas; it does not capture audio, inject TTS settings, log adjustments, or measure caller rapport. Audio extraction, timing, consent, and any application of suggestions belong to a separate host integration. It is not a clinical, crisis-response, or financial decision tool.
Research-inspired prototype: the studies below motivate the design but do not validate this implementation or its outcomes.
Scientific Foundation
| Paper | Venue | Year | Contribution to This Skill |
|---|---|---|---|
| Communication Accommodation Theory | Language Sciences, Elsevier | 2023 | Theoretical framework: convergence/divergence mechanisms, mediated communication model |
| Modeling Vocal Entrainment via Deep Unsupervised Learning | IEEE Transactions on Affective Computing | 2022 | Triplet-network entrainment distance → basis for cosine similarity aggregation |
| Context-Aware Computational Entrainment in Dyadic Conversations | arXiv | 2022 | Cross-subject attention model for real-time dyadic entrainment tracking |
| ISO/IEC 42001:2023 AI Management System | ISO | 2023 | Transparency, logging, and auditability requirements for adaptive AI systems |
How It Works
A proposed host integration could place this helper between feature extraction and TTS. The shipped helper only computes suggestions:
Step 1: Baseline Calibration (first 5 seconds)
The host may set is_calibrating=True during a chosen warm-up window. This returns CALIBRATING and an identity directive; the helper itself has no clock, feature collection, or automatic five-second calibration.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 142 lines · 41 tokens per session scan A 9a8247867972
call-prosodic-entrainment-optimizer is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (109 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 2,122 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-19.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…