MoneyPrinterTurbo Video Generator
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Offline experimental scorer of supplied acoustic features for phone-workflow demonstrations.
Install Call Voice Deepfake Liveness Detector
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill call-voice-deepfake-liveness-detector -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-voice-deepfake-liveness-detector --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-voice-deepfake-liveness-detector .claude/skills/call-voice-deepfake-liveness-detector && 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-voice-deepfake-liveness-detector" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-voice-deepfake-liveness-detector into .claude/skills/call-voice-deepfake-liveness-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-voice-deepfake-liveness-detector", 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-voice-deepfake-liveness-detectorType 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-voice-deepfake-liveness-detector -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-voice-deepfake-liveness-detector --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-voice-deepfake-liveness-detector .agents/skills/call-voice-deepfake-liveness-detector && 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-voice-deepfake-liveness-detector" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-voice-deepfake-liveness-detector into .agents/skills/call-voice-deepfake-liveness-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-voice-deepfake-liveness-detector", 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-voice-deepfake-liveness-detector -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-voice-deepfake-liveness-detector --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-voice-deepfake-liveness-detector .cursor/skills/call-voice-deepfake-liveness-detector && 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-voice-deepfake-liveness-detector" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-voice-deepfake-liveness-detector into .cursor/skills/call-voice-deepfake-liveness-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-voice-deepfake-liveness-detector", 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-voice-deepfake-liveness-detector--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-voice-deepfake-liveness-detector -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents call-voice-deepfake-liveness-detector --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-voice-deepfake-liveness-detector .gemini/skills/call-voice-deepfake-liveness-detector && 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-voice-deepfake-liveness-detector" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-voice-deepfake-liveness-detector into .gemini/skills/call-voice-deepfake-liveness-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-voice-deepfake-liveness-detector", 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-voice-deepfake-liveness-detectorInstalls 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-voice-deepfake-liveness-detector -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-voice-deepfake-liveness-detector .github/skills/call-voice-deepfake-liveness-detector && 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-voice-deepfake-liveness-detector" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-voice-deepfake-liveness-detector into .github/skills/call-voice-deepfake-liveness-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-voice-deepfake-liveness-detector", 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-voice-deepfake-liveness-detector -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-voice-deepfake-liveness-detector --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-voice-deepfake-liveness-detector .opencode/skills/call-voice-deepfake-liveness-detector && 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-voice-deepfake-liveness-detector" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/call-voice-deepfake-liveness-detector into .opencode/skills/call-voice-deepfake-liveness-detector/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "call-voice-deepfake-liveness-detector", 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-voice-deepfake-liveness-detector- GitHub stars
- 107
- Token cost
- ~1.9k tokens
- SKILL.md length
- 842 words
- Files
- 6 (incl. scripts, references)
- Skills in repo
- 101
- Repo updated
- First seen
- Licence
- MIT
At a glance
Offline experimental scorer of supplied acoustic features for phone-workflow demonstrations.
- Works in 5 steps: Spectral Flatness Analysis: Real human… → Phase Noise Floor Inspection: Microphone… → Temporal Periodicity Deviation (Jitter):… → …
- Tasks that involve Text to speech and voice
- SKILL.md covers Scientific Foundation, How It Works, Decision Matrix and Key Features, plus 6 more sections
- Runs Python scripts from its folder
What it does
Call Voice Deepfake Liveness Detector is an agent skill from CALLE-AI/awesome-phone-call-agents. Offline experimental scorer of supplied acoustic features for phone-workflow demonstrations. Returns heuristic review suggestions, not caller authentication or a deployed deepfake detector.
Its SKILL.md is about 1.9k 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
- Tasks that involve Text to speech and voice
Example prompts
- “/call-voice-deepfake-liveness-detector”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Spectral Flatness Analysis: Real human speech contains a rich, irregular harmonic structure. TTS engines produce overly smooth…
- Phase Noise Floor Inspection: Microphone recordings always contain ambient environmental noise (Gaussian noise floor, ≈−40 to −60 dB)…
- Temporal Periodicity Deviation (Jitter): Human vocal production involves micro-variations in pitch period. Synthetic voices exhibit…
- Liveness Score Aggregation: The three sub-scores are averaged into a single liveness_score (0.0 = fully synthetic → 1.0 = genuine human)…
- Suggested Review Action: Returns an action string only. It does not route, block, escalate, log, or authorize a transaction.
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 Voice Deepfake Liveness Detector loads about 1.9k tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 57 tokens; SKILL.md has 842 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). 842 words, ~1,901 tokens.
Download SKILL.mdSave it as
.claude/skills/call-voice-deepfake-liveness-detector/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- call-voice-deepfake-liveness-detector
- description
- Offline experimental scorer of supplied acoustic features for phone-workflow demonstrations. Returns heuristic review suggestions, not caller authentication or a deployed deepfake detector.
- version
- 1.0.0
Voice Deepfake Liveness Detector
This skill directly addresses one of the most urgent and growing threats in telephony: AI-powered voice cloning fraud. With tools like ElevenLabs and VALL-E, a bad actor can clone a person's voice from just a few seconds of audio and use it to impersonate family members, executives, or even government officials in phone calls.
Unlike transcript-oriented call-fraud-shield, this prototype accepts three already-extracted numeric acoustic features. It includes no raw-waveform extraction, telephony middleware, call blocking, identity verification, or audit logger. Its synthetic-fixture labels are advisory examples, not proof that a caller is human or synthetic.
Note: This skill and
call-fraud-shieldare complementary, not competing. Together, they form a two-layer defense: acoustic (this skill) + semantic (call-fraud-shield).
Scientific Foundation
This skill is grounded in the peer-reviewed anti-spoofing literature:
- ASVspoof 2021 (IEEE/ACM TASLP, 2023): The gold-standard benchmark for countermeasures against TTS and voice-conversion (VC) attacks. Defines the Equal Error Rate (EER) metric and the feature taxonomy adopted by this skill.
- ADD 2022 Challenge (ICASSP 2022): The first challenge to address Partially Fake Audio Detection (PF) — where only a fragment of the call is synthetic — a critical real-world threat model.
- FTC Voice Cloning Challenge (April 2024): Official U.S. regulatory acknowledgement of the consumer threat, validating the practical urgency of this skill's design.
How It Works
The helper combines supplied features using illustrative thresholds. A separate host would have to extract and validate these inputs; the following rationale is a design hypothesis:
- Spectral Flatness Analysis: Real human speech contains a rich, irregular harmonic structure. TTS engines produce overly smooth spectrograms with unnaturally low spectral flatness variance.
- Phase Noise Floor Inspection: Microphone recordings always contain ambient environmental noise (Gaussian noise floor, ≈−40 to −60 dB). AI-generated audio is typically unnaturally clean (≈−85 to −95 dB).
- Temporal Periodicity Deviation (Jitter): Human vocal production involves micro-variations in pitch period. Synthetic voices exhibit machine-regular periodicity detectable by statistical analysis.
- Liveness Score Aggregation: The three sub-scores are averaged into a single
liveness_score(0.0 = fully synthetic → 1.0 = genuine human). Requires ≥ 2 valid features; otherwise returnsINDETERMINATE. - Suggested Review Action: Returns an action string only. It does not route, block, escalate, log, or authorize a transaction.
Decision Matrix
liveness_score | Classification | Confidence | Recommended Action |
|---|---|---|---|
>= 0.70 | HUMAN | HIGH | proceed_normally |
0.35 – 0.69 | LIKELY_SYNTHETIC | MEDIUM | insert_friction_challenge |
< 0.35 | SYNTHETIC | HIGH | block_and_escalate_to_human |
< 2 valid features | INDETERMINATE | LOW | apply_standard_verification |
Key Features
- Offline scoring: The helper receives numeric features; no extraction latency or end-to-end latency has been established.
- Fail-open design: Insufficient audio data (packet loss, muted caller) →
INDETERMINATE, never a false positiveSYNTHETICblock. - Complementary to
call-fraud-shield: Acoustic layer defense, not a replacement for semantic analysis. - Threshold tunable per use-case: High-security financial calls →
threshold=0.60; general customer service →threshold=0.30. - Partial feature resilience: Operates meaningfully with any 2 of 3 features — robust to selective sensor failure.
Configuration Reference
| Parameter | Default | Range | Description |
|---|---|---|---|
SYNTHETIC_THRESHOLD | 0.35 | 0.10 – 0.60 | Score below this → SYNTHETIC. Raise for stricter security. |
HUMAN_THRESHOLD | 0.70 | 0.50 – 0.90 | Score at/above this → HUMAN. |
MIN_QUALITY_NOISE_DB | -80.0 | −90 to −60 | Noise floor below this → suspicious (TTS-like). |
JITTER_SYNTHETIC_MAX | 0.005 | 0.001 – 0.010 | Pitch jitter below this → TTS-like. |
FLATNESS_SYNTHETIC_MAX | 0.015 | 0.005 – 0.030 | Spectral flatness variance below this → TTS-like. |
segment_duration_ms | 500 | 250 – 2000 | Audio window length for feature extraction. |
Show full SKILL.md (314 more words)Show less
Expected Outcomes & Metrics
These are unvalidated design targets, not benchmark results of the shipped helper:
| Metric | Target | Notes |
|---|---|---|
| Equal Error Rate (EER) | < 5% target | Not validated against ASVspoof by this contribution |
| False Positive Rate (FPR) | < 3% | Legitimate callers incorrectly flagged |
| False Negative Rate (FNR) | < 8% | Synthetic voices that pass undetected |
| Latency (per segment) | < 20ms | On standard cloud compute |
| Indeterminate Rate | < 5% | Calls with insufficient audio quality |
Use Cases
- Executive impersonation / CEO fraud prevention: Detect attackers cloning a CFO's voice to authorize wire transfers.
- Grandparent/family emergency scam defense: Flag synthetic voices claiming to be a grandchild in distress.
- KYC (Know Your Customer) voice verification: Add a liveness gate to voice-based identity verification flows.
- Call center integrity monitoring: Continuous background monitoring for high-volume inbound call operations.
- Partially-fake audio detection: Catch hybrid attacks where only key phrases are synthesized into a real conversation stream (ADD 2022 PF track).
Limitations & Known Constraints
- Not a forensic tool: The liveness score is a probabilistic risk indicator, not legal-grade proof of synthesis. All
SYNTHETICflags must route to human review for final decision. - Codec degradation: Heavy audio compression (G.711, low-bitrate VoIP) can reduce spectral richness and push borderline human voices toward
LIKELY_SYNTHETIC. Threshold should be recalibrated for heavily compressed environments. - Adversarial robustness: A sufficiently motivated attacker can add artificial noise to a synthetic voice to game the noise floor detector. This is an active research area (ADD 2022 FG track) and future versions will incorporate countermeasure ensembles.
- Non-English accents & speech impediments: Diverse vocal profiles must be included in threshold validation datasets to prevent demographic bias.
Integration
Proposed integration only: a host could pass the advisory result to an agent after implementing feature extraction and its own verification controls. The diagram is not a shipped middleware hook:
[Inbound Audio Stream]
|
[call-voice-deepfake-liveness-detector] <-- this skill
|
liveness_score=0.28, flag=SYNTHETIC, confidence=HIGH
|
[Conversational Agent] <-- adapts: inserts friction challenge
|
[call-fraud-shield] <-- semantic/transcript layerReferences
See references/research-papers.md for research inspiration, not validation of this heuristic.
See references/safety.md for safety, bias, and compliance guidelines.
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-voice-deepfake-liveness-detector of CALLE-AI/awesome-phone-call-agents.
- SKILL.md
- references/examples.md
- references/research-papers.md
- references/safety.md
- scripts/deepfake_detector.py
- scripts/test_deepfake_detector.py
Open the folder on GitHubat commit 38d4118
Compare with similar skills
Call Voice Deepfake Liveness Detector 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 Voice Deepfake Liveness Detector this skillCALLE-AI/awesome-phone-call-agents | 107 | — | ~1.9k | 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 |
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Questions about Call Voice Deepfake Liveness Detector
What does Call Voice Deepfake Liveness Detector do?
Offline experimental scorer of supplied acoustic features for phone-workflow demonstrations. Call Voice Deepfake Liveness Detector is an agent skill from CALLE-AI/awesome-phone-call-agents. Offline experimental scorer of supplied acoustic features for phone-workflow demonstrations.
When should I use Call Voice Deepfake Liveness Detector?
Call Voice Deepfake Liveness Detector fits situations like: tasks that involve Text to speech and voice.
How do I install Call Voice Deepfake Liveness Detector in Claude Code?
Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill call-voice-deepfake-liveness-detector -a claude-code`. Or copy the skill folder (skills/call-voice-deepfake-liveness-detector in CALLE-AI/awesome-phone-call-agents) into .claude/skills/call-voice-deepfake-liveness-detector in your project. Claude Code loads it when a task matches its description.
How do I install Call Voice Deepfake Liveness Detector in Codex?
Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill call-voice-deepfake-liveness-detector -a codex`. Or copy the skill folder (skills/call-voice-deepfake-liveness-detector in CALLE-AI/awesome-phone-call-agents) into .agents/skills/call-voice-deepfake-liveness-detector in your project. Codex loads it when a task matches its description.
Can I use Call Voice Deepfake Liveness Detector 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-voice-deepfake-liveness-detector -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-voice-deepfake-liveness-detector, .gemini/skills/call-voice-deepfake-liveness-detector, .github/skills/call-voice-deepfake-liveness-detector and .opencode/skills/call-voice-deepfake-liveness-detector in your project.
What does Call Voice Deepfake Liveness Detector need to run?
Going by SKILL.md and its folder, Call Voice Deepfake Liveness Detector needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Call Voice Deepfake Liveness Detector 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 Voice Deepfake Liveness Detector 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 Voice Deepfake Liveness Detector use?
Call Voice Deepfake Liveness Detector 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 Voice Deepfake Liveness Detector use?
About 1.9k tokens (SKILL.md is roughly 7.6k 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 1.9k tokens, read only when the agent opens those files.
What are the alternatives to Call Voice Deepfake Liveness Detector?
Skills that share tags, products or a category with Call Voice Deepfake Liveness Detector: 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 Voice Deepfake Liveness Detector?
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.