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OpenMinis/MinisSkills
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Audio forensics and voice recovery guidelines for CSI-level audio analysis.
Install Audio Voice Recovery
$ npx skills add pproenca/dot-skills --skill audio-voice-recovery -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install pproenca/dot-skills audio-voice-recovery --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/pproenca/dot-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.experimental/audio-voice-recovery .claude/skills/audio-voice-recovery && 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 "audio-voice-recovery" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/audio-voice-recovery into .claude/skills/audio-voice-recovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audio-voice-recovery", 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/pproenca/dot-skills/tree/master/skills/.experimental/audio-voice-recoveryType 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 pproenca/dot-skills --skill audio-voice-recovery -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install pproenca/dot-skills audio-voice-recovery --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/.experimental/audio-voice-recovery .agents/skills/audio-voice-recovery && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "audio-voice-recovery" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/audio-voice-recovery into .agents/skills/audio-voice-recovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audio-voice-recovery", 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 pproenca/dot-skills --skill audio-voice-recovery -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install pproenca/dot-skills audio-voice-recovery --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/.experimental/audio-voice-recovery .cursor/skills/audio-voice-recovery && 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 "audio-voice-recovery" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/audio-voice-recovery into .cursor/skills/audio-voice-recovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audio-voice-recovery", 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/pproenca/dot-skills.git --path skills/.experimental/audio-voice-recovery--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 pproenca/dot-skills --skill audio-voice-recovery -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install pproenca/dot-skills audio-voice-recovery --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/.experimental/audio-voice-recovery .gemini/skills/audio-voice-recovery && 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 "audio-voice-recovery" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/audio-voice-recovery into .gemini/skills/audio-voice-recovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audio-voice-recovery", 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 pproenca/dot-skills audio-voice-recoveryInstalls 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 pproenca/dot-skills --skill audio-voice-recovery -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/.experimental/audio-voice-recovery .github/skills/audio-voice-recovery && 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 "audio-voice-recovery" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/audio-voice-recovery into .github/skills/audio-voice-recovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audio-voice-recovery", 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 pproenca/dot-skills --skill audio-voice-recovery -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install pproenca/dot-skills audio-voice-recovery --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/.experimental/audio-voice-recovery .opencode/skills/audio-voice-recovery && 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 "audio-voice-recovery" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/audio-voice-recovery into .opencode/skills/audio-voice-recovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audio-voice-recovery", 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
audio-voice-recovery- GitHub stars
- 215
- Token cost
- ~3.3k tokens
- SKILL.md length
- 1,067 words
- Files
- 55 (incl. scripts, references, assets)
- Skills in repo
- 41
- Repo updated
- First seen
- Licence
- MIT
At a glance
Audio forensics and voice recovery guidelines for CSI-level audio analysis.
- Works in 8 steps: Signal Preservation & Analysis (CRITICAL) → Noise Profiling & Estimation (CRITICAL) → Spectral Processing (HIGH) → …
- Tasks involving audio enhancement
- SKILL.md covers When to Apply, Rule Categories by Priority, Quick Reference and Essential Tools, plus 5 more sections
- Calls python3, brew and pip
What it does
Audio Voice Recovery is an agent skill from pproenca/dot-skills. Audio forensics and voice recovery guidelines for CSI-level audio analysis. This skill should be used when recovering voice from low-quality or low-volume audio, enhancing degraded recordings, performing forensic audio analysis, or transcribing difficult audio. Triggers on tasks involving audio enhancement, noise reduction, voice isolation, forensic authentication, or audio transcription.
Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 57 other files, including scripts, reference files and assets (for example `AGENTS.md`, `assets/templates/_template.md` and `metadata.json`).
It sits in Media & Creative, covering Transcription and Authentication. The repository describes itself as: A collection of AI agent skills following the Agent Skills open format. The licence is MIT.
When your agent uses it
- Tasks involving audio enhancement
- Noise reduction
- Voice isolation
- Forensic authentication
Example prompts
- “/audio-voice-recovery”
Requirements
- Python 3
Workflow steps
8 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 cf93c57. 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 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
python3brewpipffmpegwhisper
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
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
Audio Voice Recovery loads about 3.3k tokens when it runs, and up to ~67k if it reads all its reference files. Until then it costs about 103 tokens; SKILL.md has 1,067 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 pproenca/dot-skills at commit cf93c57, republished under its MIT licence (© pproenca). 1,067 words, ~3,303 tokens.
Download SKILL.mdSave it as
.claude/skills/audio-voice-recovery/SKILL.md (or your agent's skills folder). This skill also uses 54 other files; get the full folder from GitHub.- name
- audio-voice-recovery
- description
- Audio forensics and voice recovery guidelines for CSI-level audio analysis. This skill should be used when recovering voice from low-quality or low-volume audio, enhancing degraded recordings, performing forensic audio analysis, or transcribing difficult audio. Triggers on tasks involving audio enhancement, noise reduction, voice isolation, forensic authentication, or audio transcription.
Forensic Audio Research Audio Voice Recovery Best Practices
Comprehensive audio forensics and voice recovery guide providing CSI-level capabilities for recovering voice from low-quality, low-volume, or damaged audio recordings. Contains 45 rules across 8 categories, prioritized by impact to guide audio enhancement, forensic analysis, and transcription workflows.
When to Apply
Reference these guidelines when:
- Recovering voice from noisy or low-quality recordings
- Enhancing audio for transcription or legal evidence
- Performing forensic audio authentication
- Analyzing recordings for tampering or splices
- Building automated audio processing pipelines
- Transcribing difficult or degraded speech
Rule Categories by Priority
| Priority | Category | Impact | Prefix | Rules |
|---|---|---|---|---|
| 1 | Signal Preservation & Analysis | CRITICAL | signal- | 5 |
| 2 | Noise Profiling & Estimation | CRITICAL | noise- | 5 |
| 3 | Spectral Processing | HIGH | spectral- | 6 |
| 4 | Voice Isolation & Enhancement | HIGH | voice- | 7 |
| 5 | Temporal Processing | MEDIUM-HIGH | temporal- | 5 |
| 6 | Transcription & Recognition | MEDIUM | transcribe- | 5 |
| 7 | Forensic Authentication | MEDIUM | forensic- | 5 |
| 8 | Tool Integration & Automation | LOW-MEDIUM | tool- | 7 |
Quick Reference
1. Signal Preservation & Analysis (CRITICAL)
signal-preserve-original- Never modify original recordingsignal-lossless-format- Use lossless formats for processingsignal-sample-rate- Preserve native sample ratesignal-bit-depth- Use maximum bit depth for processingsignal-analyze-first- Analyze before processing
2. Noise Profiling & Estimation (CRITICAL)
noise-profile-silence- Extract noise profile from silent segmentsnoise-identify-type- Identify noise type before reductionnoise-adaptive-estimation- Use adaptive estimation for non-stationary noisenoise-snr-assessment- Measure SNR before and afternoise-avoid-overprocessing- Avoid over-processing and musical artifacts
3. Spectral Processing (HIGH)
spectral-subtraction- Apply spectral subtraction for stationary noisespectral-wiener-filter- Use Wiener filter for optimal noise estimationspectral-notch-filter- Apply notch filters for tonal interferencespectral-band-limiting- Apply frequency band limiting for speechspectral-equalization- Use forensic equalization to restore intelligibilityspectral-declip- Repair clipped audio before other processing
4. Voice Isolation & Enhancement (HIGH)
voice-rnnoise- Use RNNoise for real-time ML denoisingvoice-dialogue-isolate- Use source separation for complex backgroundsvoice-formant-preserve- Preserve formants during pitch manipulationvoice-dereverb- Apply dereverberation for room echovoice-enhance-speech- Use AI speech enhancement services for quick resultsvoice-vad-segment- Use VAD for targeted processingvoice-frequency-boost- Boost frequency regions for specific phonemes
5. Temporal Processing (MEDIUM-HIGH)
temporal-dynamic-range- Use dynamic range compression for level consistencytemporal-noise-gate- Apply noise gate to silence non-speech segmentstemporal-time-stretch- Use time stretching for intelligibilitytemporal-transient-repair- Repair transient damage (clicks, pops, dropouts)temporal-silence-trim- Trim silence and normalize before export
6. Transcription & Recognition (MEDIUM)
transcribe-whisper- Use Whisper for noise-robust transcriptiontranscribe-multipass- Use multi-pass transcription for difficult audiotranscribe-segment- Segment audio for targeted transcriptiontranscribe-confidence- Track confidence scores for uncertain wordstranscribe-hallucination- Detect and filter ASR hallucinations
7. Forensic Authentication (MEDIUM)
forensic-enf-analysis- Use ENF analysis for timestamp verificationforensic-metadata- Extract and verify audio metadataforensic-tampering- Detect audio tampering and splicesforensic-chain-custody- Document chain of custody for evidenceforensic-speaker-id- Extract speaker characteristics for identification
8. Tool Integration & Automation (LOW-MEDIUM)
tool-ffmpeg-essentials- Master essential FFmpeg audio commandstool-sox-commands- Use SoX for advanced audio manipulationtool-python-pipeline- Build Python audio processing pipelinestool-audacity-workflow- Use Audacity for visual analysis and manual editingtool-install-guide- Install audio forensic toolchaintool-batch-automation- Automate batch processing workflowstool-quality-assessment- Measure audio quality metrics
Essential Tools
| Tool | Purpose | Install |
|---|---|---|
| FFmpeg | Format conversion, filtering | brew install ffmpeg |
| SoX | Noise profiling, effects | brew install sox |
| Whisper | Speech transcription | pip install openai-whisper |
| librosa | Python audio analysis | pip install librosa |
| noisereduce | ML noise reduction | pip install noisereduce |
| Audacity | Visual editing | brew install audacity |
Workflow Scripts (Recommended)
Use the bundled scripts to generate objective baselines, create a workflow plan, and verify results.
scripts/preflight_audio.py- Generate a forensic preflight report (JSON or Markdown).scripts/plan_from_preflight.py- Create a workflow plan template from the preflight report.scripts/compare_audio.py- Compare objective metrics between baseline and processed audio.
Example usage:
# 1) Analyze and capture baseline metrics
python3 skills/.experimental/audio-voice-recovery/scripts/preflight_audio.py evidence.wav --out preflight.json
# 2) Generate a workflow plan template
python3 skills/.experimental/audio-voice-recovery/scripts/plan_from_preflight.py --preflight preflight.json --out plan.md
# 3) Compare baseline vs processed metrics
python3 skills/.experimental/audio-voice-recovery/scripts/compare_audio.py \
--before evidence.wav \
--after enhanced.wav \
--format md \
--out comparison.mdShow full SKILL.md (495 more words)Show less
Forensic Preflight Workflow (Do This Before Any Changes)
Align preflight with SWGDE Best Practices for the Enhancement of Digital Audio (20-a-001) and SWGDE Best Practices for Forensic Audio (08-a-001).
Establish an objective baseline state and plan the workflow so processing does not introduce clipping, artifacts, or false "done" confidence.
Use scripts/preflight_audio.py to capture baseline metrics and preserve the report with the case file.
Capture and record before processing:
- Record evidence identity and integrity: path, filename, file size, SHA-256 checksum, source, format/container, codec
- Record signal integrity: sample rate, bit depth, channels, duration
- Measure baseline loudness and levels: LUFS/LKFS, true peak, peak, RMS, dynamic range, DC offset
- Detect clipping and document clipped-sample percentage, peak headroom, exact time ranges
- Identify noise profile: stationary vs non-stationary, dominant noise bands, SNR estimate
- Locate the region of interest (ROI) and document time ranges and changes over time
- Inspect spectral content and estimate speech-band energy and intelligibility risk
- Scan for temporal defects: dropouts, discontinuities, splices, drift
- Evaluate channel correlation and phase anomalies (if stereo)
- Extract and preserve metadata: timestamps, device/model tags, embedded notes
Procedure:
- Prepare a forensic working copy, verify hashes, and preserve the original untouched.
- Locate ROI and target signal; document exact time ranges and changes across the recording.
- Assess challenges to intelligibility and signal quality; map challenges to mitigation strategies.
- Identify required processing and plan a workflow order that avoids unwanted artifacts.
Generate a plan draft with
scripts/plan_from_preflight.pyand complete it with case-specific decisions. - Measure baseline loudness and true peak per ITU-R BS.1770 / EBU R 128 and record peak/RMS/DC offset.
- Detect clipping and dropouts; if clipping is present, declip first or pause and document limitations.
- Inspect spectral content and noise type; collect representative noise profile segments and estimate SNR.
- If stereo, evaluate channel correlation and phase; document anomalies.
- Create a baseline listening log (multiple devices) and define success criteria for intelligibility and listenability.
Failure-pattern guardrails:
- Do not process until every preflight field is captured.
- Document every process, setting, software version, and time segment to enable repeatability.
- Compare each processed output to the unprocessed input and assess progress toward intelligibility and listenability.
- Avoid over-processing; review removed signal (filter residue) to avoid removing target signal components.
- Keep intermediate files uncompressed and preserve sample rate/bit depth when moving between tools.
- Perform a final review against the original; if unsatisfactory, revise or stop and report limitations.
- If the request is not achievable, communicate limitations and do not declare completion.
- Require objective metrics and A/B listening before declaring completion.
- Do not rely solely on objective metrics; corroborate with critical listening.
- Take listening breaks to avoid ear fatigue during extended reviews.
Quick Enhancement Pipeline
# 1. Analyze original (run preflight and capture baseline metrics)
python3 skills/.experimental/audio-voice-recovery/scripts/preflight_audio.py evidence.wav --out preflight.json
# 2. Create working copy with checksum
cp evidence.wav working.wav
sha256sum evidence.wav > evidence.sha256
# 3. Apply enhancement
ffmpeg -i working.wav -af "\
highpass=f=80,\
adeclick=w=55:o=75,\
afftdn=nr=12:nf=-30:nt=w,\
equalizer=f=2500:t=q:w=1:g=3,\
loudnorm=I=-16:TP=-1.5:LRA=11\
" enhanced.wav
# 4. Transcribe
whisper enhanced.wav --model large-v3 --language en
# 5. Verify original unchanged
sha256sum -c evidence.sha256
# 6. Verify improvement (objective comparison + A/B listening)
python3 skills/.experimental/audio-voice-recovery/scripts/compare_audio.py \
--before evidence.wav \
--after enhanced.wav \
--format md \
--out comparison.mdHow to Use
Read individual reference files for detailed explanations and code examples:
- Section definitions - Category structure and impact levels
- Rule template - Template for adding new rules
Reference Files
| File | Description |
|---|---|
| AGENTS.md | Complete compiled guide with all rules |
| references/_sections.md | Category definitions and ordering |
| assets/templates/_template.md | Template for new rules |
| metadata.json | Version and reference information |
© pproenca, 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 54 other files (scripts, references, assets) in skills/.experimental/audio-voice-recovery of pproenca/dot-skills.
- SKILL.md
- AGENTS.md
- assets/templates/_template.md
- metadata.json
- references/_sections.md
- references/forensic-chain-custody.md
- references/forensic-enf-analysis.md
- references/forensic-metadata.md
- references/forensic-speaker-id.md
- references/forensic-tampering.md
- references/noise-adaptive-estimation.md
- references/noise-avoid-overprocessing.md
- references/noise-identify-type.md
- references/noise-profile-silence.md
- references/noise-snr-assessment.md
- references/signal-analyze-first.md
- references/signal-bit-depth.md
- references/signal-lossless-format.md
- … and 37 more
Open the folder on GitHubat commit cf93c57
Compare with similar skills
Audio Voice Recovery 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 |
|---|---|---|---|---|---|---|
| Audio Voice Recovery this skillpproenca/dot-skills | 215 | — | ~3.3k | Automated safety check: Pass | MIT | |
| Bilibili HubOpenMinis/MinisSkills | 446 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Gemini Live API Devgoogle-gemini/gemini-skills | 4.3k | — | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| HyperFrames Media Useheygen-com/hyperframes | 60k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Native Subtitle Quote Imagechengyi-ai/native-subtitle-quote-image | 2.6k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Edu Chem Videowy51ai/edulab | 1.4k | — | ~2.1k | Automated safety check: Notes | Apache-2.0 |
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Questions about Audio Voice Recovery
What does Audio Voice Recovery do?
Audio forensics and voice recovery guidelines for CSI-level audio analysis. Audio Voice Recovery is an agent skill from pproenca/dot-skills. Audio forensics and voice recovery guidelines for CSI-level audio analysis.
When should I use Audio Voice Recovery?
Audio Voice Recovery fits situations like: tasks involving audio enhancement; noise reduction; voice isolation; forensic authentication.
How do I install Audio Voice Recovery in Claude Code?
Run `npx skills add pproenca/dot-skills --skill audio-voice-recovery -a claude-code`. Or copy the skill folder (skills/.experimental/audio-voice-recovery in pproenca/dot-skills) into .claude/skills/audio-voice-recovery in your project. Claude Code loads it when a task matches its description.
How do I install Audio Voice Recovery in Codex?
Run `npx skills add pproenca/dot-skills --skill audio-voice-recovery -a codex`. Or copy the skill folder (skills/.experimental/audio-voice-recovery in pproenca/dot-skills) into .agents/skills/audio-voice-recovery in your project. Codex loads it when a task matches its description.
Can I use Audio Voice Recovery 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 pproenca/dot-skills --skill audio-voice-recovery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/audio-voice-recovery, .gemini/skills/audio-voice-recovery, .github/skills/audio-voice-recovery and .opencode/skills/audio-voice-recovery in your project.
What does Audio Voice Recovery need to run?
Going by SKILL.md and its folder, Audio Voice Recovery needs the command-line tools its instructions call (python3, brew, pip, ffmpeg and whisper). Our summary lists: Python 3.
Does Audio Voice Recovery access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Audio Voice Recovery 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 Audio Voice Recovery use?
Audio Voice Recovery 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 Audio Voice Recovery use?
About 3.3k tokens (SKILL.md is roughly 13k 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 64k tokens, read only when the agent opens those files.
What are the alternatives to Audio Voice Recovery?
Skills that share tags, products or a category with Audio Voice Recovery: Bilibili Hub (OpenMinis/MinisSkills, 446 stars), Gemini Live API Dev (google-gemini/gemini-skills, 4.3k stars), HyperFrames Media Use (heygen-com/hyperframes, 60k stars) and Native Subtitle Quote Image (chengyi-ai/native-subtitle-quote-image, 2.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Audio Voice Recovery?
pproenca (a GitHub user) maintains it in pproenca/dot-skills, which has 215 GitHub stars. The repository holds 41 skills in this directory. The repository was last updated on August 15, 2026.
Source: pproenca/dot-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.