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 agentmods add skills/glincker/glin-profanity/content_guardnpx skills add glincker/glin-profanity --skill content_guardgit clone --depth 1 https://github.com/glincker/glin-profanityWrote 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/glincker/glin-profanity/content_guard)<a href="https://agentmods.dev/skills/glincker/glin-profanity/content_guard"><img src="https://agentmods.dev/badge/skills/glincker/glin-profanity/content_guard.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00012 | $0.00433 |
| Opus 5 | $0.00006 | $0.00217 |
| Sonnet 5 | $0.00002 | $0.00087 |
| Haiku 4.5 | $0.00001 | $0.00043 |
Grade A, and why
content_guard scanned grade A with 0 findings 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 5d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Content Guard Skill
This skill provides proactive content moderation across all messaging platforms (WhatsApp, Telegram, Discord, Slack, iMessage, etc.).
Automatic Moderation Mode
When enabled, automatically:
- Check all incoming messages for profanity
- Flag or censor based on configuration
- Log incidents for review
Configuration
The guard can be configured for different strictness levels:
Strict Mode
- Block all profanity immediately
- No context consideration
- Best for: Children's platforms, professional environments
Moderate Mode (Default)
- Check with context awareness
- Allow profanity in gaming/casual contexts
- Best for: General community platforms
Lenient Mode
- Only flag severe profanity
- Allow most casual language
- Best for: Adult communities
Usage in Conversations
When a message comes in:
- Run
check_profanityon the content - If flagged, use
analyze_profanity_contextfor context - If still flagged after context, censor or warn
Integration
This skill integrates with:
- WhatsApp groups (via OpenClaw/Moltbot bridge)
- Telegram channels
- Discord servers
- Slack workspaces
- iMessage threads
- Matrix rooms
- Microsoft Teams
Supported Languages
All 24 languages are supported for moderation: arabic, chinese, czech, danish, dutch, english, esperanto, finnish, french, german, hindi, hungarian, italian, japanese, korean, norwegian, persian, polish, portuguese, russian, spanish, swedish, thai, turkish
Credits
Powered by glin-profanity
Compatible with OpenClaw, Moltbot (legacy), and Clawdbot (legacy).
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.
- 5d ago First seen · 70 lines · 12 tokens per session scan A 726c8e9b1e8c
content_guard is a skill published in the GitHub repository glincker/glin-profanity (60 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 433 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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