flo-moderate

flo-moderate is a command for coding agents from Flomenco-Inc/flo-plugin. It costs 0 tokens per session (394 once invoked), scanned A, original, MIT.

A command that asks Flo's content-checking agent to assess whether a media asset fits a chosen content rating and distribution platform. It checks areas such as violence, language, sexual content, substance use, and platform policy issues.

In plain words
What is it for?
Use it to moderate a media asset against ratings such as PG-13 or TV-MA and platforms such as YouTube, Netflix, Prime Video, or broadcast television.
Why use it?
Reviewing media for age-rating concerns and platform rules can be time-consuming and inconsistent. The command turns the required context into a structured moderation report.

Command

Part of the flo-plugin plugin — 5 skills, 9 commands, 1 MCP server shipped together

Install

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.

agentmods
npx agentmods add commands/flomenco-inc/flo-plugin/flo-moderate
Clone the repo
git clone --depth 1 https://github.com/Flomenco-Inc/flo-plugin

Or install flo-plugin, the plugin that ships this one along with the rest of its 5 skills, 9 commands, 1 MCP server.

Wrote 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.

agentmods badge for flo-moderate

README.md
[![agentmods](https://agentmods.dev/badge/commands/flomenco-inc/flo-plugin/flo-moderate.svg)](https://agentmods.dev/commands/flomenco-inc/flo-plugin/flo-moderate)
Your own site
<a href="https://agentmods.dev/commands/flomenco-inc/flo-plugin/flo-moderate"><img src="https://agentmods.dev/badge/commands/flomenco-inc/flo-plugin/flo-moderate.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 394 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00394
Opus 5 $0.00000 $0.00197
Sonnet 5 $0.00000 $0.00079
Haiku 4.5 $0.00000 $0.00039

Measured 3d ago against content hash aeeabe1434be, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

flo-moderate 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 3d 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.

commands/flo-moderate.md · 58 lines

What it actually says

/flo-moderate

Run a content appropriateness check on a media asset using the Flo Appropriateness Checker agent.

Usage

/flo-moderate

Behavior

When this command is invoked:

  1. Ask the user for the following required inputs:

    • Asset ID: The media asset ID to moderate
    • Target Rating: The intended content rating (e.g. G, PG, PG-13, TV-14, TV-MA)
    • Target Platform: The distribution platform (e.g. Netflix, Prime Video, YouTube, Broadcast)
  2. Confirm the inputs with the user before proceeding.

  3. Invoke the flo_command MCP tool with:

/flo:moderate <assetId>

If that does not return a structured report, fall back to flo_query with a prompt structured as follows:

Run a content appropriateness check on asset [ASSET_ID].

Target rating: [TARGET_RATING]
Target platform: [TARGET_PLATFORM]

Check for:
- Violence (intensity, frequency, context)
- Language (profanity, slurs, adult dialogue)
- Sexual content (nudity, suggestive material)
- Substance use (drugs, alcohol, tobacco)
- Platform-specific policy violations for [TARGET_PLATFORM]

Return a structured moderation report with:
- Overall pass/fail against the target rating
- Per-category severity scores
- Timecode references for flagged segments
- Platform policy compliance status
- Recommended action (approve / flag for review / reject)
  1. Present the moderation report clearly, grouping flags by category with timecodes.

Notes

  • Requires active Flo authentication. Run flo_auth_login first if not already authenticated.
  • Platform policy rules are maintained by the Flo platform and may be updated independently of this plugin.
  • For delivery spec validation, use /flo-deliver.
Changes

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.

  1. 3d ago First seen · 58 lines · 0 tokens per session scan A aeeabe1434be

Subscribe to this mod's changes

flo-moderate is a command published in the GitHub repository Flomenco-Inc/flo-plugin (0 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 394 tokens. 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 2026-08-31.