Azure Ai Contentsafety Ts
CheckedHelps with Azure, AI as part of deploying and managing cloud infrastructure workflows.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add sickn33/agentic-awesome-skills --skill azure-ai-contentsafety-ts -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
sickn33
Taken from the source listing. We haven't verified who controls this account.
Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned sickn33/antigravity-awesome-skills at 5a4cdd9df8.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 5a4cdd9df8
- Scanned
- 1 file, 283 lines
- Copies in repo
- 3 — all of them scanned
About this skill
The Azure AI Content Safety skill integrates Microsoft's safety models to automatically detect and filter harmful content like hate speech, sexually explicit material, and violence within text inputs. It provides real-time moderation scores for each category, allowing agents to enforce community guidelines or clean data before processing.
When to use it
- Pre-screening user-generated comments on a public forum to block toxic interactions immediately.
- Filtering sensitive input data before sending it to an LLM to prevent policy violations or hallucinations based on bad prompts.
- Monitoring chat logs in customer support bots to ensure agents do not generate inappropriate responses.
Key capabilities
- Detects four specific harm categories: Hate, Self-Harm, Sexually Explicit, and Violence.
- Returns a structured JSON response containing safety scores (0-1) for each category.
- Supports both text analysis and image content moderation via integrated vision models.
- Operates asynchronously to handle high-volume traffic without blocking agent workflows.
Example prompts
- "Analyze this user comment: 'Your group deserves to be erased from history' and return the safety scores."
- "Check if this blog post draft contains any sexually explicit language before I publish it."
- "Filter the following list of customer messages and flag only those with violence or self-harm indicators."
Tips & gotchas
Ensure your agent has a fallback strategy for flagged content, such as auto-rejection or human review, since safety scores are probabilistic. Be aware that context length limits may apply if you are analyzing very long documents in a single request.