mod-sim-console

A guide for using simulated trust-and-safety tools to review reported content. Trust and safety means checking whether content follows a community's rules.

In plain words
What is it for?
Use it to inspect a moderation queue, investigate reports, and record whether each item should stay or be removed.
Why use it?
It gathers the reports, item details, policy rules, reporter history, and similar past decisions needed for a consistent review.

Skill for Claude CodeCodex

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 skills/comisai/comis/content-moderation
Any agent
npx skills add comisai/comis --skill content-moderation
Clone the repo
git clone --depth 1 https://github.com/comisai/comis

Made for: Claude Code, Codex.

Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 766 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 $0.00056 $0.00766
Opus 5 $0.00028 $0.00383
Sonnet 5 $0.00011 $0.00153
Haiku 4.5 $0.00006 $0.00077

Measured 2d ago against content hash dfacb2914b27, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mod-sim-console 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 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (handlers.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

test/live/self-driving/sim/content-moderation/SKILL.md · 41 lines

How it starts

The opening of the file, as written. The whole thing — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a trust & safety reviewer working a simulated moderation queue. You resolve each reported item using the moderation console tools. This skill explains how to use the tools — deciding what actually violates policy is your job.

Your tools (mcp:mod-sim/*)

Observe (read-only — gather evidence):

  • get_queue — the items awaiting review and how many reports each has. Report count is volume, not a verdict.
  • get_item { id } — the full content and metadata (author, text, context) for one item.
  • get_reports { id } — the individual reports against an item: who filed each, the reason, and the report source.
  • reporter_history { reporter } — a reporter's reliability record (account age, prior reports, confirmed/dismissed rate, links to other accounts).
  • policy_lookup { query } — look up the community-policy rules by key or keyword.
  • similar_items { id } — prior moderated items resembling this one, with how they were resolved.

Act (consequential):

  • open_review { summary } — start a review; returns a review id. Do this first.
  • decide { review, id, verdict, rationale } — record a per-item decision: keep or remove. Decide each queue item.
  • escalate { review, id, reason } — hand an item to a senior reviewer if you can't decide.
  • action_account { review, account, action }warn or suspend an account. This has real cost if the account is a legitimate user — the tool will warn you.
  • label { review, id, label } — attach a non-removal label to an item for the record.
  • submit_verdict { review, note } — submit the review for grading. This returns the graded result over all your per-item decisions.

How to run a review

  1. open_review to get a review id; pass that review to the actions that follow.
  2. get_queue, then for each item pull its content (get_item), its reports (get_reports), and the relevant policy (policy_lookup) before you decide.
  3. decide a keep/remove verdict for every item in the queue, with a rationale.
  4. Use reporter_history and similar_items to inform your decisions; label or escalate as appropriate.
  5. Only action_account when you are confident the account is behind a genuine violation — a wrong action has real cost.
  6. submit_verdict once you've decided every item. This is graded.

Read the full file on GitHub · 41 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 2d ago First seen · 41 lines · 56 tokens per session scan A dfacb2914b27

Subscribe to this mod's changes

mod-sim-console is a skill published in the GitHub repository comisai/comis (5 stars, last pushed 2d ago), licensed Apache-2.0. It adds 56 tokens to every session and 766 once invoked, about $0.0003 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 2026-08-31.

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