Reddit Monitor

Reddit Monitor (opportunity scanner)

Daily Reddit-only skill. Scans the subreddits qualified by reddit-audience-finder and outputs a ranked list of engagement opportunities — threads where a substantive reply is achievable today. Designed for /loop 1d.

This skill replaces the previous multi-channel monitor. X/Twitter and forums are handled elsewhere. Keep the concern pure: Reddit opportunities, Reddit drafts.

Fresh top-level posts are handled by reddit-post-composer. Comment follow-ups are handled by reddit-reply. This skill is the bridge between audience and action.

Dependencies

Load in order:

1. social-media:social-identity — voice + banned patterns + 30% mention cap
2. commons:identity — ICP and positioning
3. commons:brand-voice — tone rules

If reports/social/reddit/audience/subreddits.json does not exist, instruct the user to run reddit-audience-finder first and abort.

Reddit data access

Use curl in bash with a browser User-Agent. WebFetch may be blocked for www.reddit.com in some environments.

UA="Mozilla/5.0 (compatible; systempromptbot/1.0)"
curl -s -A "$UA" "https://www.reddit.com/r/$SUB/hot.json?limit=25"
curl -s -A "$UA" "https://www.reddit.com/r/$SUB/new.json?limit=25"

Parallelise across subs with & + wait. Sleep 0.3s between batches.

Cadence

Tier assignments come from reports/social/reddit/audience/subreddits.json. Never hard-code a sub list in this skill.

Step 1: Load audience

Read reports/social/reddit/audience/subreddits.json. If older than 120 days, emit a warning: the list is likely stale, recommend re-running reddit-audience-finder.

Step 2: Fetch hot + new for each qualified sub

For each Tier A, B, and (on Monday) C sub, pull both endpoints. hot.json catches peaked threads; new.json catches threads before they saturate.

Merge, dedupe by post ID, and keep the following fields per thread:

• permalink, url, title, selftext[:800]
• num_comments, score, upvote_ratio
• created_utc, author
• subreddit
• link_flair_text

Discard threads older than 48 hours unless num_comments >= 100 and the conversation is still moving.

Step 3: Relevance filter (drop most threads)

Keep a thread only if title OR selftext matches a governance-adjacent keyword:

ai, llm, gpt, chatgpt, claude, copilot, cursor, anthropic, openai, mcp, agent, prompt, shadow ai, rag, govern, polic, complian, audit, soc ?2, iso ?42001, eu ai, nist, endpoint, dlp, injection, standardi[sz], rollout, observab, self-host, air-gap, cost, policy, permission, skill, plugin, hook

Discard everything else. Typical retention: 8–15% of fetched threads.

Step 4: Score each surviving thread (0–100)

Four dimensions, 25 points each:

D1: Topical fit (25)

D2: Opportunity freshness (25)

D3: Poster quality (25)

Proxy from selftext:

D4: Our authority (25)

Can we add value the thread doesn't already have? Check comments via {permalink}.json if uncertain. Avoid threads where the answer is already given thoroughly.

Sum to a total. Select the top 8 threads for the action list. Enforce variety:

• At least 1 Tier-A thread if any scored ≥ 50
• No more than 3 threads from the same sub
• Mix post-types (technical, venting, strategic)

Step 5: Draft reply hooks

For each selected thread, draft a reply. Hook, not essay.

Structure (3–6 sentences):

1. Open with the poster's specific situation. Reference their exact setup or problem.
2. Provide value: concrete technical insight, specific suggestion, or a relevant distinction (2–4 sentences).
3. Close with a forward-looking thought, a specific next step, or a tangible resource. Never a generic engagement question.

Tone matching (from subreddits.json tone field):

• r/cto, r/compliance — formal, strategic, regulation-literate
• r/ExperiencedDevs, r/devops — technical, no-nonsense
• r/sysadmin — dry, operational
• r/cybersecurity — rigorous, threat-model-aware
• r/ITManagers, r/msp — business-practical, margin-aware
• r/ClaudeAI, r/AI_Agents — casual fellow-user, evidence-valued
• r/ClaudeCode — technical, Claude power-user, workflow-improvement oriented; expects specifics about Claude Code behaviour, not general AI commentary
• r/rust — precise, performance-aware, skeptical of benchmarks without reproducible numbers; no marketing language, no hype
• r/LocalLLaMA, r/selfhosted — deeply technical, respect for open-source sovereignty
• r/LLMDevs, r/RAG — practitioner, sources-required
• r/mcp — technical, protocol-focused; "no AI slop" enforced hard — vary sentence length, zero AI-prose markers

Step 6: Mention cap (30%)

Across the final action list, no more than 30% of drafts may mention systemprompt.io. A mention only happens when the thread is specifically about a problem systemprompt solves: governance, enforcement, standardisation, shadow-AI discovery, build-vs-buy, self-hosted AI infra for teams.

Flag each action: mention: Yes or mention: No.

When mentioned, make it a natural part of the answer, not an insertion: "systemprompt.io is self-hosted governance for this — one binary, you run it, Claude tool-calls go through your policy hooks before execution." Never: "Check out systemprompt.io!"

Step 7: Anti-sludge gate

Applies to every drafted reply.

Banned openings:

• "Great question", "Nice work", "Love this", "Fellow Claude user here", "Hey there", "Thanks for sharing"
• Any opening that is a question back to the poster

Banned patterns:

• Em dashes anywhere (use commas, periods, parentheses)
• Fabricated personal stories (no "I ran into this too" unless Ed actually did)
• AI clichés: revolutionize, game-changer, unlock, supercharge, seamlessly, harness, cutting-edge, next-generation, paradigm shift, disrupt, empower, leverage (verb), reimagine, transform
• Hashtags
• Forced product mentions

Quality standard:

• Reply must not read as AI-generated. Vary sentence length. Be specific.
• Each reply is visibly personalised to the specific post — reference a concrete detail they wrote.
• Read the reply as the poster. Would you find it genuinely helpful or is it sludge?

Step 8: Output

Write two files per run.

1. reports/social/reddit/daily/YYYY-MM-DD/opportunities.md

The scored opportunity list. Structure:

# Reddit Opportunities — YYYY-MM-DD

**Tiers scanned:** A ({N} subs), B ({N} subs){, C ({N} subs) if Monday}
**Threads fetched:** {N} | **Passed relevance filter:** {N} | **Selected:** {top 8}
**Mention count:** {X}/{total} drafts ({percent}%, cap 30%)
**Audience list age:** {days since reddit-audience-finder last ran}

---

## Top 8 opportunities

| # | Score | Sub | Title (truncated) | Comments | Mention | Time |
|---|---|---|---|---|---|---|
| 1 | 86 | r/ITManagers | Network manager building AI assistant... | 9 | Yes | 10 min |
| ... |

---

## Drafted replies

### 1. r/ITManagers — "New Network Manager trying to build an AI assistant"

- **Permalink:** {full URL}
- **Score breakdown:** topical_fit=20, freshness=25, poster_quality=18, authority=23 → total 86
- **Poster context:** {one-line summary of what they actually said}
- **Our angle:** {the specific value we add}
- **systemprompt mention:** Yes / No
- **Draft:**

> {3–6 sentence reply}

- **Post-reply action:** log to hypothesis ledger as H-### if this is a seeded experiment

{repeat for each of 8}

2. reports/social/reddit/daily/YYYY-MM-DD/reddit-monitor.md

Kept for reddit-reply compatibility. Same content as opportunities.md in the legacy format the reply skill expects. Both files until reddit-reply is updated to read opportunities.md.

Quality checklist

• reports/social/reddit/audience/subreddits.json loaded successfully
• Audience list is < 120 days old (else warning logged)
• Tier-A subs always scanned; Tier-C only on Mondays
• Every selected thread has a dimensions breakdown
• No thread repeats across drafts (check by permalink)
• No more than 3 drafts from one sub
• Mention cap ≤ 30% across the list
• Anti-sludge gate passes on every draft
• Tone matches the sub's tone field
• Both output files written with today's date