Claude Code Templates is a command-line tool and catalogue for configuring Anthropic’s Claude Code with agents, commands, settings, hooks, integrations, skills, and project templates. Developers use it to browse and install reusable components for their coding workflows. The catalogue includes many of these Claude Code components.
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 davila7/claude-code-templates --skill multimodal-whispergit clone --depth 1 https://github.com/davila7/claude-code-templatesWrote 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/davila7/claude-code-templates/multimodal-whisper)<a href="https://agentmods.dev/skills/davila7/claude-code-templates/multimodal-whisper"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/multimodal-whisper/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/davila7/claude-code-templates/multimodal-whisper"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/multimodal-whisper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Privilege Escalation · line 46 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
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 | $0.00070 | $0.02037 |
| Opus 5.5 | $0.00028 | $0.00815 |
| Sonnet 5.5 | $0.00014 | $0.00407 |
| Haiku 4.5 | $0.00007 | $0.00204 |
Grade B, and why
multimodal-whisper scanned grade B with 1 finding 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 18d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
# Ubuntu: sudo apt install ffmpeg Copies of this mod
8 near-identical copies found in the catalogue:
- whisper — 100% identical, 11 lines differ
- whisper — 100% identical, 11 lines differ
- whisper — 100% identical, 11 lines differ
- whisper — 97% identical, 12 lines differ
- whisper — 97% identical, 12 lines differ
- whisper — 97% identical, 13 lines differ
- whisper — 97% identical, 13 lines differ
- whisper — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Whisper - Robust Speech Recognition
OpenAI's multilingual speech recognition model.
When to use Whisper
Use when:
- Speech-to-text transcription (99 languages)
- Podcast/video transcription
- Meeting notes automation
- Translation to English
- Noisy audio transcription
- Multilingual audio processing
Metrics:
- 72,900+ GitHub stars
- 99 languages supported
- Trained on 680,000 hours of audio
- MIT License
Use alternatives instead:
- AssemblyAI: Managed API, speaker diarization
- Deepgram: Real-time streaming ASR
- Google Speech-to-Text: Cloud-based
Quick start
Installation
# Requires Python 3.8-3.11
pip install -U openai-whisper
# Requires ffmpeg
# macOS: brew install ffmpeg
# Ubuntu: sudo apt install ffmpeg
# Windows: choco install ffmpeg
Basic transcription
import whisper
# Load model
model = whisper.load_model("base")
# Transcribe
result = model.transcribe("audio.mp3")
# Print text
print(result["text"])
# Access segments
for segment in result["segments"]:
print(f"[{segment['start']:.2f}s - {segment['end']:.2f}s] {segment['text']}")
Model sizes
# Available models
models = ["tiny", "base", "small", "medium", "large", "turbo"]
# Load specific model
model = whisper.load_model("turbo") # Fastest, good quality
| Model | Parameters | English-only | Multilingual | Speed | VRAM |
|---|---|---|---|---|---|
| tiny | 39M | ✓ | ✓ | ~32x | ~1 GB |
| base | 74M | ✓ | ✓ | ~16x | ~1 GB |
| small | 244M | ✓ | ✓ | ~6x | ~2 GB |
| medium | 769M | ✓ | ✓ | ~2x | ~5 GB |
| large | 1550M | ✗ | ✓ | 1x | ~10 GB |
| turbo | 809M | ✗ | ✓ | ~8x | ~6 GB |
Recommendation: Use turbo for best speed/quality, base for prototyping
Transcription options
Language specification
# Auto-detect language
result = model.transcribe("audio.mp3")
# Specify language (faster)
result = model.transcribe("audio.mp3", language="en")
# Supported: en, es, fr, de, it, pt, ru, ja, ko, zh, and 89 more
What ships with it
1 file 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.
- 18d ago Changed · +3 tokens per session ec197f12696f
- 20d ago Changed · +1 lines 3fa507d7bb32
- 2mo ago First seen · 318 lines · 67 tokens per session scan B acce14621ec8
multimodal-whisper is a skill published in the GitHub repository davila7/claude-code-templates (33,484 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 2,037 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
multimodal-whisper compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| multimodal-whisperdavila7/claude-code-templates | Skill | 33k | today | MIT | B | 70 | Claude Code, Codex |
| ccc-dataKevinZai/commander | Skill | 6 | yesterday | MIT | A | 36 | Claude Code |
| couchbase-ai-applicationscelticht32/Couchbase-Skills-for-Claude.ai | Skill | 4 | 4mo ago | MIT | A | 202 | Claude Code, Codex |
| couchbase-ftscelticht32/Couchbase-Skills-for-Claude.ai | Skill | 4 | 4mo ago | MIT | A | 208 | Claude Code, Codex |
| feature-engineerinbharatai/claude-skills | Skill | 43 | 8mo ago | MIT | A | 23 | Claude Code, Codex |
| gcp-architectinbharatai/claude-skills | Skill | 43 | 8mo ago | MIT | A | 28 | Claude Code, Codex |
| midjourney-prompterinbharatai/claude-skills | Skill | 43 | 8mo ago | MIT | A | 28 | Claude Code, Codex |