MoneyPrinterTurbo Video Generator
harry0703/MoneyPrinterTurbo
Installs and runs MoneyPrinterTurbo to turn a topic or script into a finished short video with voice-over, subtitles, stock footage and music.
Offline experimental phone-workflow helper that compares supplied prosodic features and suggests bounded TTS parameter changes.
Install Call Prosodic Entrainment Optimizer
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-prosodic-entrainment-optimizer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-prosodic-entrainment-optimizer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/call-prosodic-entrainment-optimizer .claude/skills/call-prosodic-entrainment-optimizer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "call-prosodic-entrainment-optimizer" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-prosodic-entrainment-optimizer into .claude/skills/call-prosodic-entrainment-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-prosodic-entrainment-optimizer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-prosodic-entrainment-optimizerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-prosodic-entrainment-optimizer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-prosodic-entrainment-optimizer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/call-prosodic-entrainment-optimizer .agents/skills/call-prosodic-entrainment-optimizer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "call-prosodic-entrainment-optimizer" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-prosodic-entrainment-optimizer into .agents/skills/call-prosodic-entrainment-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-prosodic-entrainment-optimizer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-prosodic-entrainment-optimizer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-prosodic-entrainment-optimizer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/call-prosodic-entrainment-optimizer .cursor/skills/call-prosodic-entrainment-optimizer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "call-prosodic-entrainment-optimizer" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-prosodic-entrainment-optimizer into .cursor/skills/call-prosodic-entrainment-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-prosodic-entrainment-optimizer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/CALLE-AI/awesome-phone-call-agents.git --path skills/call-prosodic-entrainment-optimizer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-prosodic-entrainment-optimizer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-prosodic-entrainment-optimizer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/call-prosodic-entrainment-optimizer .gemini/skills/call-prosodic-entrainment-optimizer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "call-prosodic-entrainment-optimizer" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-prosodic-entrainment-optimizer into .gemini/skills/call-prosodic-entrainment-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-prosodic-entrainment-optimizer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-prosodic-entrainment-optimizerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-prosodic-entrainment-optimizer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/call-prosodic-entrainment-optimizer .github/skills/call-prosodic-entrainment-optimizer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "call-prosodic-entrainment-optimizer" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-prosodic-entrainment-optimizer into .github/skills/call-prosodic-entrainment-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-prosodic-entrainment-optimizer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-prosodic-entrainment-optimizer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-prosodic-entrainment-optimizer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/call-prosodic-entrainment-optimizer .opencode/skills/call-prosodic-entrainment-optimizer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "call-prosodic-entrainment-optimizer" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-prosodic-entrainment-optimizer into .opencode/skills/call-prosodic-entrainment-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-prosodic-entrainment-optimizer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
call-prosodic-entrainment-optimizer- GitHub stars
- 107
- Token cost
- ~2.2k tokens
- SKILL.md length
- 1,003 words
- Files
- 6 (incl. scripts, references)
- Skills in repo
- 101
- Repo updated
- First seen
- Licence
- MIT
At a glance
Offline experimental phone-workflow helper that compares supplied prosodic features and suggests bounded TTS parameter changes.
- Works in 5 steps: Baseline Calibration (first 5 seconds) → Feature Extraction (per 1-second window) → Entrainment Score Computation → …
- Synthetic demonstrations and host integration planning
- SKILL.md covers Scientific Foundation, How It Works, Mode Presets and Key Features, plus 6 more sections
- Runs Python scripts from its folder
What it does
Call Prosodic Entrainment Optimizer is an agent skill from CALLE-AI/awesome-phone-call-agents. Offline experimental phone-workflow helper that compares supplied prosodic features and suggests bounded TTS parameter changes. Use for synthetic demonstrations and host integration planning.
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/examples.md`, `references/research-papers.md` and `references/safety.md`).
It sits in Media & Creative, covering Text to speech and voice. The repository describes itself as: Portable phone-call Agent Skills, apps, examples, adapters, and scheduler recipes for AI agents. The licence is MIT.
When your agent uses it
- Synthetic demonstrations and host integration planning
- Tasks that involve Text to speech and voice
Example prompts
- “/call-prosodic-entrainment-optimizer”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 38d4118. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Call Prosodic Entrainment Optimizer loads about 2.2k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 57 tokens; SKILL.md has 1,003 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from CALLE-AI/awesome-phone-call-agents at commit 38d4118, republished under its MIT licence (© CALLE-AI). 1,003 words, ~2,245 tokens.
Download SKILL.mdSave it as
.claude/skills/call-prosodic-entrainment-optimizer/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- call-prosodic-entrainment-optimizer
- description
- Offline experimental phone-workflow helper that compares supplied prosodic features and suggests bounded TTS parameter changes. Use for synthetic demonstrations and host integration planning.
- version
- 1.0.0
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.
Step 2: Feature Extraction (per 1-second window)
The host must supply three features; no audio extractor is included:
- Fundamental Frequency (F0, Hz): Perceived pitch — speaker identity and emotional state marker.
- Speech Rate (WPM): Cognitive load and urgency indicator.
- RMS Energy (normalized): Loudness and engagement level.
Step 3: Entrainment Score Computation
Cosine similarity is computed between the L2-normalized caller and agent prosodic feature vectors. entrainment_score ∈ [0.0, 1.0]:
1.0= perfect prosodic synchrony0.0= complete divergence
Step 4: Status Classification & TTS Directive Generation
entrainment_score | Status | Action |
|---|---|---|
>= 0.90 | OPTIMAL | No directive — back off to avoid over-mirroring |
0.75 – 0.89 | TARGET_REACHED | Bounded directive may still be returned |
< 0.75 | LOW_ENTRAINMENT | Issue TTSDirective with bounded parameter deltas |
| First 5s | CALIBRATING | Identity directive — no adjustment |
Step 5: Safety-Bounded TTS Directive
| Parameter | Adjustment Logic | Safety Cap |
|---|---|---|
pitch_shift_semitones | Proportional to F0 delta (Hz → semitones) | ±3.0 semitones/window |
rate_multiplier | Proportional to WPM ratio | [0.80, 1.20] |
energy_scale | Proportional to RMS energy ratio | [0.70, 1.30] |
The formula uses a fixed 0.05 fraction of the feature difference, subject to the caps above. This is not a 5% output cap or a time-based rate limit; the host controls invocation timing.
Mode Presets
| Mode | Use Case | Behavior |
|---|---|---|
DEFAULT | General inbound/outbound | Balanced bidirectional convergence |
SALES | Outbound sales, lead qualification | Converge toward caller's energy to build rapport |
SUPPORT | Synthetic downward-only demonstration | Never raises pitch, rate, or energy; no clinical de-escalation efficacy is established |
Key Features
- Bounded suggestions: Parameter caps are enforced per invocation; perceptual effects have not been measured.
- Anti-over-mirroring: Backs off automatically at
score >= 0.90to prevent the "uncanny valley" of identical-sounding voices. - Host-controlled calibration:
is_calibrating=Truesuppresses adjustments. - Fail-safe on zero/silence: Muted callers, zero-rate speech, and whispering are all handled without crashes or division-by-zero errors.
- SUPPORT mode: Only holds or lowers pitch, rate, and energy. It does not implement an upward matching phase or establish de-escalation efficacy.
Show full SKILL.md (428 more words)Show less
Configuration Reference
Source constants and proposed host settings are listed below, not a runtime configuration API. The helper has no window-duration or calibration-duration parameter.
| Parameter | Default | Range | Description |
|---|---|---|---|
TARGET_THRESHOLD | 0.75 | 0.60 – 0.85 | Below this → issue TTSDirective |
OPTIMAL_CEILING | 0.90 | 0.80 – 0.95 | Above this → back off (no directive) |
MAX_PITCH_DELTA | 3.0 semitones | 1.0 – 5.0 | Safety cap on pitch adjustment per window |
MAX_RATE_DELTA | 0.20 (±20%) | 0.10 – 0.30 | Safety cap on rate multiplier delta |
MAX_ENERGY_DELTA | 0.30 (±30%) | 0.15 – 0.40 | Safety cap on energy scale delta |
step_factor | 0.05 | Source edit only | Fixed interpolation factor, not an output/time cap |
window_duration_s | 1.0 | 0.5 – 2.0 | Feature extraction window length |
calibration_duration_s | 5.0 | 3.0 – 10.0 | Baseline collection period |
Expected Outcomes & Metrics
The following are design hypotheses or operating targets, not measured outcomes of this helper:
| Metric | Expected Improvement | Notes |
|---|---|---|
| CSAT Score | Not measured | Requires a separate evaluation |
| Call Abandonment Rate | Not measured | No outcome improvement is established |
| First Call Resolution (FCR) | Not measured | No outcome improvement is established |
| Entrainment Score (avg call) | 0.78 – 0.85 | Target operating range |
| Directive latency | < 10ms | Synthesis parameter update time |
Use Cases
Proposed research contexts only, not validated clinical, crisis-response, sales, or financial deployments:
- Outbound sales calls: Mirror the prospect's energy and cadence to build trust before pitching.
- Healthcare intake: Automatically slow pace and lower pitch to match an elderly or anxious caller, reducing cognitive load and improving information capture.
- Debt collection: Reduce confrontational dynamics by actively converging toward a calm, measured pace even when the caller is agitated.
- Mental health support lines: Gently mirror distressed caller's cadence (SUPPORT mode) while guiding toward slower, regulated rhythm via Match & Lead.
- High-volume IVR exit: Reduce caller frustration after a failed IVR interaction by rapidly entraining to their speech pattern when a live-agent-style AI picks up.
Limitations & Known Constraints
- Cosine similarity is angle-based: Features that point in the same direction in feature space can score high even with different magnitudes. This is expected behavior — the system is measuring style convergence, not energy matching. The energy scale directive handles amplitude alignment separately.
- TTS dependency: The
TTSDirectiveoutput requires a TTS engine that accepts real-time prosodic parameter overrides (e.g., SSML<prosody>tags or equivalent API). Not all TTS providers support this. - Non-verbal speakers: The host must detect missing/unreliable features and choose whether to keep calibration enabled. The helper does not infer this condition.
- Accent diversity: Threshold calibration must include diverse vocal profiles to prevent systematic bias against non-standard prosodic patterns.
Integration
[Caller Audio]
|
[ASR + Prosodic Feature Extractor]
|
[call-prosodic-entrainment-optimizer] <-- this skill
|
TTSDirective { pitch=-1.2, rate=0.94, energy=0.91 }
|
[TTS Synthesis Engine] <-- applies SSML prosody overrides
|
[Agent Voice → Caller]References
See references/research-papers.md for research inspiration; citations are not implementation validation.
See references/safety.md for safety and host responsibilities; no ISO conformity is established.
See references/examples.md for end-to-end scenario walkthroughs.
© CALLE-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 5 other files (scripts, references) in skills/call-prosodic-entrainment-optimizer of CALLE-AI/awesome-phone-call-agents.
- SKILL.md
- references/examples.md
- references/research-papers.md
- references/safety.md
- scripts/entrainment_optimizer.py
- scripts/test_entrainment_optimizer.py
Open the folder on GitHubat commit 38d4118
Compare with similar skills
Call Prosodic Entrainment Optimizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Call Prosodic Entrainment Optimizer this skillCALLE-AI/awesome-phone-call-agents | 107 | — | ~2.2k | Automated safety check: Pass | MIT | |
| MoneyPrinterTurbo Video Generatorharry0703/MoneyPrinterTurbo | 130k | — | ~2.1k | Automated safety check: Warn | MIT | |
| HyperFrames Media Useheygen-com/hyperframes | 60k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Openspec OnboardSAP/e-mobility-charging-stations-simulator | 227 | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Blog AudioAgriciDaniel/claude-blog | 2.3k | 1 repos | ~2.2k | Automated safety check: Notes | MIT | |
| Musictadaspetra/loop | 296 | 2 repos | ~827 | Automated safety check: Pass | MIT |
Similar skills
HyperFrames Media Use
heygen-com/hyperframes
Finds, generates and edits media for HyperFrames video projects: music, sound effects, images, icons, logos, voiceovers, captions and color grades.
- Official
Openspec Onboard
SAP/e-mobility-charging-stations-simulator
Guided onboarding for OpenSpec - walk through a complete workflow cycle with narration and real codebase work.
Blog Audio
AgriciDaniel/claude-blog
Generate audio narration of blog posts using Google Gemini TTS.
Music
tadaspetra/loop
Generate music using ElevenLabs Music API. An agent skill from tadaspetra/loop.
Create News Video
hoquanghai/Auto-Create-Video
Tạo video tin tức ngắn 9:16 (~60s) từ URL bài báo hoặc file .txt tiếng Việt.
More from CALLE-AI/awesome-phone-call-agents
All 101 skills in this repoAccessible Outing Verifier
CALLE-AI/awesome-phone-call-agents
Demonstrates advisory accessibility-planning checks with offline fixtures and a proposed bounded CALL-E workflow; use for exploring unknown or qualified venue claims without making calls.
Ground Truth Gate
CALLE-AI/awesome-phone-call-agents
A skill your agent uses when an agent holds some evidence for a physical-world claim but the evidence is broader, narrower, or older than the exact question asked, and it must first decide whether a…
Is It Accessible
CALLE-AI/awesome-phone-call-agents
Call a venue and ask the accessibility questions that matter to one specific person — step-free entry, hearing loop, guide dogs, quiet hours, changing places — then return a per-need verdict backed…
Landmark Navigation Assist
CALLE-AI/awesome-phone-call-agents
Turns a pre-written, building-level location config into a CALL-E outbound phone-call task that guides a delivery driver through the last few hundred metres to a specific building using landmarks…
Research Gap Call Verifier
CALLE-AI/awesome-phone-call-agents
Turn cited business research into a bounded, approval-gated phone-call plan that asks only unresolved factual questions, then reconcile CALL-E-compatible results without treating voicemail, refusal…
Structured Outcome Followup Call
CALLE-AI/awesome-phone-call-agents
Place a goal-driven CALL-E call that collects specific structured answers, score those answers against a deterministic rubric you supply, and conditionally trigger a follow-up action — all runnable…
Related
Categories
Questions about Call Prosodic Entrainment Optimizer
What does Call Prosodic Entrainment Optimizer do?
Offline experimental phone-workflow helper that compares supplied prosodic features and suggests bounded TTS parameter changes. Call Prosodic Entrainment Optimizer is an agent skill from CALLE-AI/awesome-phone-call-agents. Offline experimental phone-workflow helper that compares supplied prosodic features and suggests bounded TTS parameter changes.
When should I use Call Prosodic Entrainment Optimizer?
Call Prosodic Entrainment Optimizer fits situations like: synthetic demonstrations and host integration planning; tasks that involve Text to speech and voice.
How do I install Call Prosodic Entrainment Optimizer in Claude Code?
Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill call-prosodic-entrainment-optimizer -a claude-code`. Or copy the skill folder (skills/call-prosodic-entrainment-optimizer in CALLE-AI/awesome-phone-call-agents) into .claude/skills/call-prosodic-entrainment-optimizer in your project. Claude Code loads it when a task matches its description.
How do I install Call Prosodic Entrainment Optimizer in Codex?
Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill call-prosodic-entrainment-optimizer -a codex`. Or copy the skill folder (skills/call-prosodic-entrainment-optimizer in CALLE-AI/awesome-phone-call-agents) into .agents/skills/call-prosodic-entrainment-optimizer in your project. Codex loads it when a task matches its description.
Can I use Call Prosodic Entrainment Optimizer in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add CALLE-AI/awesome-phone-call-agents --skill call-prosodic-entrainment-optimizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/call-prosodic-entrainment-optimizer, .gemini/skills/call-prosodic-entrainment-optimizer, .github/skills/call-prosodic-entrainment-optimizer and .opencode/skills/call-prosodic-entrainment-optimizer in your project.
What does Call Prosodic Entrainment Optimizer need to run?
Going by SKILL.md and its folder, Call Prosodic Entrainment Optimizer needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Call Prosodic Entrainment Optimizer access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Call Prosodic Entrainment Optimizer safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Call Prosodic Entrainment Optimizer use?
Call Prosodic Entrainment Optimizer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Call Prosodic Entrainment Optimizer use?
About 2.2k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.2k tokens, read only when the agent opens those files.
What are the alternatives to Call Prosodic Entrainment Optimizer?
Skills that share tags, products or a category with Call Prosodic Entrainment Optimizer: MoneyPrinterTurbo Video Generator (harry0703/MoneyPrinterTurbo, 130k stars), HyperFrames Media Use (heygen-com/hyperframes, 60k stars), Openspec Onboard (SAP/e-mobility-charging-stations-simulator, 227 stars) and Blog Audio (AgriciDaniel/claude-blog, 2.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Call Prosodic Entrainment Optimizer?
CALLE-AI (a GitHub organization) maintains it in CALLE-AI/awesome-phone-call-agents, which has 107 GitHub stars. The repository holds 101 skills in this directory. The repository was last updated on October 10, 2026.
Source: CALLE-AI/awesome-phone-call-agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.