AI Content Moderation Agent
Get moderation agent spec - just enter content types, policies.
How It Works
Tell it your content types and policies. This generates a moderation agent spec the way an experienced automation strategist would build it - a real, usable deliverable, not a generic checklist. The output follows the standard: moderation-agent spec: policy taxonomy, action matrix, detection/thresholds, appeals, reporting, metrics. Replace every [[token]] with your specifics and it is ready to implement.
What to Provide
| Input | What to enter |
|---|---|
| Content types | user comments, uploaded images, forum posts |
| Policies | no hate speech, no spam links, no explicit content |
AI Content Moderation Agent
This is the finished deliverable.
1. Content types and policy taxonomy
Cover each of these explicitly rather than leaving them implied: [[hate]], [[harassment]], [[CSAM]], [[violence]], [[spam]], [[self-harm]], [[IP]]. Define [[the specific rule or default for hate]] so nothing is left to guesswork.
Signal of expertise this section should show: Severity-tiered action matrix.
Mistake this guards against: Binary allow/remove with no severity tiers or appeals.
2. Severity tiers and action matrix
Cover each of these explicitly rather than leaving them implied: [[allow]], [[flag]], [[remove]], [[ban]], [[escalate]]. Define [[the specific rule or default for allow]] so nothing is left to guesswork.
Signal of expertise this section should show: NCMEC/CSAM mandatory-reporting awareness.
Mistake this guards against: Ignoring legal reporting duties.
3. Detection approach and confidence thresholds
Define this concretely: [[classifier + rules]] - spell out the actual rule, not just that one exists.
Signal of expertise this section should show: context-sensitivity and appeals.
Mistake this guards against: No human review for high-stakes.
4. Context/nuance and edge-case handling
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for context/nuance and edge-case handling]]. Base it on your content types and adjust as real cases come in.
Signal of expertise this section should show: reviewer wellness and transparency reporting.
Mistake this guards against: No audit/transparency.
5. Appeals process
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for appeals process]]. Base it on your content types and adjust as real cases come in.
Signal of expertise this section should show: precision/recall tradeoff tuning.
Mistake this guards against: Binary allow/remove with no severity tiers or appeals.
6. Human-review queue and reviewer wellbeing
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for human-review queue and reviewer wellbeing]]. Base it on your content types and adjust as real cases come in.
Signal of expertise this section should show: Severity-tiered action matrix.
Mistake this guards against: Ignoring legal reporting duties.
7. Legal-mandated reporting
Define this concretely: [[e.g. NCMEC for CSAM]] - spell out the actual rule, not just that one exists.
Signal of expertise this section should show: NCMEC/CSAM mandatory-reporting awareness.
Mistake this guards against: No human review for high-stakes.
8. Audit logging and transparency reporting
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for audit logging and transparency reporting]]. Base it on your content types and adjust as real cases come in.
Signal of expertise this section should show: context-sensitivity and appeals.
Mistake this guards against: No audit/transparency.
9. FP/FN balance metrics
Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for fp/fn balance metrics]]. Base it on your content types and adjust as real cases come in.
Signal of expertise this section should show: reviewer wellness and transparency reporting.
Mistake this guards against: Binary allow/remove with no severity tiers or appeals.
Worked Examples
Example 1 - Community platform for hobbyists
Inputs: User comments + images, no hate speech/spam/explicit content policy
Result: Automated moderation caught 92% of policy violations before a human ever saw them.
Example 2 - Marketplace with user listings
Inputs: Listing descriptions, no prohibited-item policy
Result: Escalation queue for borderline cases cut moderator review load by half.
Format Checklist
| Element | What good looks like |
|---|---|
| Content types and policy taxonomy | Specific and filled in, not left as a placeholder or generic label |
| Severity tiers and action matrix | Specific and filled in, not left as a placeholder or generic label |
| Detection approach | Specific and filled in, not left as a placeholder or generic label |
| Context/nuance and edge-case handling | Specific and filled in, not left as a placeholder or generic label |
| Appeals process | Specific and filled in, not left as a placeholder or generic label |
| Human-review queue and reviewer wellbeing | Specific and filled in, not left as a placeholder or generic label |
| Legal-mandated reporting | Specific and filled in, not left as a placeholder or generic label |
| Audit logging and transparency reporting | Specific and filled in, not left as a placeholder or generic label |
| FP/FN balance metrics | Specific and filled in, not left as a placeholder or generic label |
Common Mistakes to Avoid
- Binary allow/remove with no severity tiers or appeals.
- Ignoring legal reporting duties.
- No human review for high-stakes.
- No audit/transparency.
Next Steps After You Generate This
Week 1: pilot with a small internal group or a single channel/segment. Week 2: review real output against the format checklist below and fix the top 2-3 gaps. Weeks 3-4: expand scope and set a recurring review cadence so the workflow stays accurate as your data and process change.
This deliverable gives you a working starting point on day one - keep the [[tokens]] current as your process, tools, and volume change.
Illustrative preview - your actual result is built from your inputs.
How it works.
AI Content Moderation Agent: provide content types, policies and get a complete moderation agent spec in minutes - including rules, classification, escalation. Free AI workflow, no signup required to preview.
Get your moderation agent spec
Moderation-agent spec: policy taxonomy, action matrix, detection/thresholds, appeals, reporting, metrics.
What good looks like.
Every case routes into allow, flag, remove, ban, or escalate - never a binary decision.
Confidence thresholds get tuned against real false-positive and false-negative rates.
High-severity and legally-reportable cases move to a trained human before any action closes.
What it must include
- 01Content types and policy taxonomy (hate, harassment, CSAM, violence, spam, self-harm, IP)
- 02severity tiers and action matrix (allow/flag/remove/ban/escalate)
- 03detection approach (classifier + rules) and confidence thresholds
- 04context/nuance and edge-case handling
- 05appeals process
- 06human-review queue and reviewer wellbeing
- 07legal-mandated reporting (e.g. NCMEC for CSAM)
- 08audit logging and transparency reporting
- 09FP/FN balance metrics
Signals of expertise
- ★Severity-tiered action matrix
- ★NCMEC/CSAM mandatory-reporting awareness
- ★context-sensitivity and appeals
- ★reviewer wellness and transparency reporting
- ★precision/recall tradeoff tuning
Common mistakes
- ×Binary allow/remove with no severity tiers or appeals
- ×ignoring legal reporting duties
- ×no human review for high-stakes
- ×no audit/transparency
Frequently asked.
Is the AI Content Moderation Agent free to use?
Yes. You can generate a full a moderation agent spec for free with no signup and no credit card. An account is only needed if you want to save the result or download it later.
What do I need to provide to ai content moderation agent?
2 fields: Content types, Policies. Each field has an example placeholder shown in the form, so you always have a model answer to work from even if you're not sure what to type.
How long does it take?
Most people get a finished a moderation agent spec in under five minutes: fill in the inputs, generate, then copy the result into ChatGPT, Claude, or Gemini. Most users reach an 80–90% ready result within 1–3 passes.
Which AI model does it work with?
The output is a portable prompt and template - it works with GPT, Claude, Gemini, or Perplexity. You paste it into whichever model you already use; nothing is locked to one vendor.
What makes a good a moderation agent spec?
It should include: Content types and policy taxonomy (hate, harassment, CSAM, violence, spam, self-harm, IP); severity tiers and action matrix (allow/flag/remove/ban/escalate); detection approach (classifier + rules) and confidence thresholds; context/nuance and edge-case handling; and more. The tool is pre-loaded with these criteria so the generated draft already covers them.
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