podcast-generation
No security findings. Scanned 1 time, most recently 2026-02-05.
Second scanner: NVIDIA SkillSpector
Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Generate AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.
- Package
- 5 files, 29 KB
- SKILL.md
- 371 words, 12+ sections
- Code
- Python (1)
SKILL.md covers: Podcast Generation with GPT Realtime Mini · Quick Start · Environment Configuration · Core Workflow · Backend Audio Generation · Convert HTTPS endpoint to WebSocket URL · Convert PCM to WAV (see scripts/pcm_to_wav.py) · Frontend Audio Playback · Voice Options · Realtime API Events · Audio Format · References
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 3.7 KB |
references/acceptance-criteria.md | doc | 12 KB |
references/architecture.md | doc | 4.6 KB |
references/code-examples.md | doc | 6.0 KB |
scripts/pcm_to_wav.py | script | 2.4 KB |
External hosts referenced
my-resource.cognitiveservices.azure.com your-resource.cognitiveservices.azure.com
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Package contents recorded 2026-10-22 at commit cfbcbbdc679c. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Llm analyzer. A language model reads the skill and reports intent-level issues the rules cannot express.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
- Meta analyzer. A second model pass that validates, deduplicates and re-scores the other analyzers' findings.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://github.com/microsoft/agent-skills
- Raw scan result: github-skills-podcast-generation-scan.json
- Badge for your README:
- Request a re-scan