reddit-moderate
The reddit-moderate command automates Reddit community moderation by retrieving pending items from the modqueue, analyzing each against subreddit rules, and either recommending or executing moderation actions. Use interactive mode for manual review of flagged content, auto mode for hands-off removal of clear violations, or dry-run mode to preview recommendations without taking action.
git clone --depth 1 https://github.com/notque/vexjoy-agent /tmp/reddit-moderate && cp -r /tmp/reddit-moderate/skills/content/reddit-moderate ~/.claude/skills/reddit-moderateSKILL.md
# 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.
```bash
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:
```bash
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:
```bash
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; falseAnsible automation: playbooks, roles, collections, Molecule testing, Vault security.
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