reddit-moderate

name	reddit-moderate
description	Reddit moderation via PRAW: fetch modqueue, classify reports, take actions.
user-invocable	false
argument-hint	[--auto] [--dry-run]
agent	python-general-engineer
allowed-tools	["Bash","Read"]
routing	{"triggers":["moderate Reddit","modqueue","Reddit reports","Reddit moderation","check reports"],"category":"process","pairs_with":["content-calendar"]}

Reddit Moderate

On-demand Reddit community moderation powered by PRAW. Fetches your modqueue, classifies content against subreddit rules and author history using LLM-powered report classification, and executes mod actions you confirm.

Modes

Mode	Invocation	Behavior
Interactive	/reddit-moderate	Fetch queue, classify, present with analysis, you confirm actions
Auto	/loop 10m /reddit-moderate --auto	Fetch queue, classify, auto-action high-confidence items, flag rest
Dry-run	/reddit-moderate --dry-run	Fetch queue, classify, show recommendations without acting

Reference Loading Table

Signal	Load These Files	Why
Classifying items, category definitions, confidence thresholds	classification-prompt.md	Routes to the matching deep reference
Prompt template, untrusted content handling, prompt injection defense	classification-prompt.md	Routes to the matching deep reference
Action mapping by confidence level, config.json format	classification-prompt.md	Routes to the matching deep reference
Per-item classification steps, repeat offender check, mass-report detection	classification-prompt.md	Routes to the matching deep reference
Script subcommands, flags, usage examples	script-commands.md	Routes to the matching deep reference
Exit codes, error troubleshooting	script-commands.md	Routes to the matching deep reference
Scan commands, setup commands, queue/report commands	script-commands.md	Routes to the matching deep reference
Subreddit data directory structure, file purposes	context-loading.md	Routes to the matching deep reference
Setup flow for new subreddits, bootstrapping	context-loading.md	Routes to the matching deep reference
Credentials, prerequisites, dry-run default	context-loading.md	Routes to the matching deep reference
Context loading sequence, missing file handling	context-loading.md	Routes to the matching deep reference

Instructions

Interactive Mode (default)

Phase 1: FETCH -- Get the modqueue with classification prompts.

python3 skills/content/reddit-moderate/scripts/reddit-mod.py queue --json --limit 25 | python3 skills/content/reddit-moderate/scripts/reddit-mod.py classify

This pipes modqueue items through the classify subcommand, which loads subreddit context from reddit-data/{subreddit}/ and assembles a classification prompt for each item. The output is a JSON array where each result contains item metadata, heuristic flags (mass_report_flag, repeat_offender_count), and a prompt field with the fully rendered classification prompt.

The classify subcommand is a prompt assembler only; it does not call any LLM. Fields classification, confidence, and reasoning are null/empty placeholders for the LLM to fill in Phase 2.

Read the output. For each item, read the prompt field and classify it.

Phase 2: CLASSIFY -- For each item, read the rendered classification prompt and assign a classification. The prompt contains all subreddit context, rules, author history, and report signals. Classify as one of: FALSE_REPORT, VALID_REPORT, MASS_REPORT_ABUSE, SPAM, BAN_RECOMMENDED, NEEDS_HUMAN_REVIEW.

Assign a confidence score (0-100) and one-sentence reasoning for each item.

Load references/classification-prompt.md for category definitions, the full prompt template, per-item classification steps, and confidence thresholds.

Phase 3: PRESENT -- For each modqueue item, present a summary grouped by classification. Include the classification label and confidence:

Item 1: [t3_abc123] "Post title here"
  Author: u/username (score: 5, reports: 2)
  Report reasons: "spam", "off-topic"
  Body: [first 200 chars of content]
  Classification: VALID_REPORT (confidence: 92%)
  Reasoning: Author history shows 5 promotional posts in 7 days with no
             community engagement. Violates subreddit rules against self-promotion.
  Recommendation: REMOVE (reason: Rule 3)

Item 2: [t1_def456] "Comment text here"
  Author: u/other_user (score: 12, reports: 1)
  Report reason: "rude"
  Classification: FALSE_REPORT (confidence: 88%)
  Reasoning: Sarcastic but within community norms. Report appears frivolous.
  Recommendation: APPROVE

Phase 4: CONFIRM -- Ask the user to confirm or override recommendations. Wait for user input. Wait for explicit user confirmation before proceeding.

Phase 5: ACT -- Execute confirmed actions:

python3 skills/content/reddit-moderate/scripts/reddit-mod.py approve --id t1_def456
python3 skills/content/reddit-moderate/scripts/reddit-mod.py remove --id t3_abc123 --reason "Rule 3: Self-promotion"

Report results after each action.

Load references/script-commands.md for all subcommand flags and examples.

Auto Mode (for /loop)

When invoked with --auto argument or when the user says "auto mode":

1. Fetch queue and build classification prompts:

   python3 skills/content/reddit-moderate/scripts/reddit-mod.py queue --auto --since-minutes 15 --json | python3 skills/content/reddit-moderate/scripts/reddit-mod.py classify
   

2. For each item, read the rendered prompt field and classify it using the categories and confidence scoring from references/classification-prompt.md.

3. For items meeting the confidence threshold:

   • FALSE_REPORT / MASS_REPORT_ABUSE => approve
   • SPAM => remove as spam
   • VALID_REPORT => remove with generated reason
   • BAN_RECOMMENDED => always skip (requires human review regardless of confidence)

4. For items below the confidence threshold => skip (leave for human review).

5. Output a summary of actions taken, items skipped, and classifications.

Critical auto-mode rules:

• Always require human review before banning users
• Always require human review before locking threads
• When in doubt, SKIP; false negatives are better than false positives
• Log every auto-action for the user to review later

Proactive Scan Mode

Scan recent posts/comments for rule violations that were not reported:

python3 skills/content/reddit-moderate/scripts/reddit-mod.py scan --json --classify --limit 50 --since-hours 24

With --classify, the scan output includes classification prompts. Read each prompt and classify the item. Items with scan_flags (job_ad_pattern, training_vendor_pattern, possible_non_english) have heuristic signals that supplement the LLM classification.

Same confidence thresholds and safety rules as auto mode apply.

Reference Loading

Load these references when the task matches the signal:

Signal / Task	Reference File
Classifying items, category definitions, confidence thresholds	references/classification-prompt.md
Prompt template, untrusted content handling, prompt injection defense	references/classification-prompt.md
Action mapping by confidence level, config.json format	references/classification-prompt.md
Per-item classification steps, repeat offender check, mass-report detection	references/classification-prompt.md
Script subcommands, flags, usage examples	references/script-commands.md
Exit codes, error troubleshooting	references/script-commands.md
Scan commands, setup commands, queue/report commands	references/script-commands.md
Subreddit data directory structure, file purposes	references/context-loading.md
Setup flow for new subreddits, bootstrapping	references/context-loading.md
Credentials, prerequisites, dry-run default	references/context-loading.md
Context loading sequence, missing file handling	references/context-loading.md

References

This skill uses these shared patterns:

• Untrusted Content Handling - Prompt injection defense for all Reddit content fed into LLM classification