trend-reddit

name	trend-reddit
title	Reddit Trend Harvester Playbook
description	Platform-specific intel for harvesting Reddit trends across niche subreddits — upvote-velocity ranking, harvest URLs per-sub (hot/rising/top/new), mod-rules pre-flight that prevents removed posts, per-sub culture map for tech / AI / startup niches, title patterns and dead patterns in 2026. Activates inside an octoweb:trend session whenever the user names Reddit.
license	Apache-2.0
compatibility	Octoweb browser access. Logged-out works for most surfaces; logged-in needed for personalized feeds.
capabilities	octoweb memory-read memory-write
domains	octoweb
rules	["session(trend) content(reddit)","session(trend) content(subreddit)","match(\\breddit\\s+(trend|trends|harvest|brief|post)\\b)","match(\\b(harvest|scan|analyze)\\s+reddit\\b)","match(\\br/[a-zA-Z0-9_]+\\b)"]

Overview

This skill carries the platform-specific mechanics the octoweb:trend agent needs to harvest Reddit — current ranking signals (upvote velocity + comment depth + flag tax), per-sub harvest URLs, the mod-rules pre-flight that prevents wasted recommendations, per-sub culture map for the AI / dev / startup niches, title patterns, dead patterns, timing. The agent owns the shared DNA loop; this skill plugs the Reddit parameters in.

Mental model

Reddit is not one audience. Each subreddit has its own ranker quirks, allowed formats, mod culture, and reader expectations. A title that crushes in r/Entrepreneur dies on r/MachineLearning. The hot ranker rewards first-hour velocity heavily — the first 60 minutes decide whether a post hits the sub's top or dies in new. The brief must be per-sub, not pan-Reddit. Mod-rules pre-flight is non-negotiable — recommending a post that violates a sub's rules wastes the user's submission and risks bans.

Rules

Current ranking signals (2026)

Signal	Effect
Upvotes per hour, first 4h	Primary signal. >100/h in a 100k sub = climbing, >500/h = breakout
Comment-to-upvote ratio	Discussion signal. >10% high engagement, >20% controversial-or-deep
Upvote ratio (% upward)	Quality signal. 90% / 200 upvotes outranks 60% / 400
OP comment in first 30 min	~2× comment-count multiplier on average
Crossposts to multiple subs (organic)	Amplification candidate
Flagged or shadow-removed	Visible as [removed] / [deleted] — surface as teaching example

Harvest surfaces per sub (run in parallel)

For EACH target sub:

Surface	URL	Yields
Hot	https://www.reddit.com/r/<sub>/hot/	What's surfacing now
Rising	https://www.reddit.com/r/<sub>/rising/	Early-climb signal; small upvotes, fast velocity
Top — day	https://www.reddit.com/r/<sub>/top/?t=day	Last-24h winners
Top — week	https://www.reddit.com/r/<sub>/top/?t=week	7-day winners — recurring DNA
New (sample)	https://www.reddit.com/r/<sub>/new/	What's being posted now (saturation check)
Rules	https://www.reddit.com/r/<sub>/about/rules/	Mod restrictions — mandatory pre-flight

Plus pan-Reddit:

• Topic search: https://www.reddit.com/search/?q=<topic>&t=week
• Crossover: https://www.reddit.com/r/all/top/?t=day

Cap parallel tabs at 8–12. Run multiple harvest passes if more subs needed.

If a feed lazy-loads slowly, scroll incrementally and wait for posts to render before extracting — stay on www.reddit.com.

Mod-rules pre-flight (mandatory)

For every target sub, navigate to /r/<sub>/about/rules/ (or sidebar) BEFORE making recommendations. Flag:

• Self-promotion ratios (9:1 rule is common)
• AI-generated content disclosure requirements
• Required post tags / flair
• Restricted post types (no link posts, no image posts)
• Weekly thread requirements ("ask all questions in the weekly thread")
• New-account / low-karma posting limits

If the user's planned angle clearly violates a sub's rules, do not recommend that sub for that angle. Say so explicitly in the brief.

Scoring rubric (Reddit-specific signals)

Virality axis 0–5:

• Upvotes per hour in first 4h — primary signal
• Comment-to-upvote ratio
• Upvote ratio (visible on post page)
• OP-comment density in first hour
• Crosspost reach when present

Niche-fit axis 0–5:

• Sub-fit — does the user's angle match this sub's actual culture?
• Topic-fit — direct / adjacent / format-transplant / off

Score per sub, not pan-Reddit. A 4×4 in r/MachineLearning matters more than 5×2 in r/all.

Per-sub culture map (verify each at runtime — rules drift quarterly)

Sub	Culture	What wins	What gets removed
r/MachineLearning	Academic, gatekept	Paper discussion, novel results, deep technical. Tag with [R] / [D] / [P] / [N]	Marketing, AGI hype, no-paper "discussion"
r/LocalLLaMA	Practitioner, hardware-aware	Model benchmarks, quantization tricks, hardware setups, local-runtime tips	SaaS marketing, closed-model hype with no local angle
r/programming	Skeptical, language-agnostic	Blog posts with depth, war stories, "I read the source of X"	Listicles, "10 tools every dev needs", AI slop
r/startups	Bootstrappers + funded	Honest revenue posts, MRR breakdowns, lessons from failure	"I built X in 3 hours" wrappers, low-effort idea validation
r/SaaS	Indie SaaS, transparent	Real metrics, churn experiments, pricing experiments	Fake success stories, growth-hack listicles
r/Entrepreneur	Mixed quality, hustle-friendly	Specific operator playbooks	Generic motivation, "how I made $1M in 30 days"
r/ChatGPT	Casual, mainstream	Cool prompts, weird outputs, image gens	Technical depth goes ignored
r/ClaudeAI	Small, technical-curious	Workflow comparisons, system prompt tactics, Claude-specific tips	Open-and-shut "Claude vs X" posts
r/singularity	Speculative	Big-picture takes, frontier model commentary	Hands-on technical posts

Title patterns that work

1. Specific result + setup — "Ran [X] on [Y]: here's the [Z] result"
2. Contested claim — "I think [X] is wrong. Here's my [data/setup/code]"
3. Honest failure — "[X] didn't work. Here's what I tried and what broke"
4. Novel artifact — "[New thing] I built / paper I read — sharing for discussion"
5. Targeted question with context — "How do you handle [specific thing]? Here's my current approach and where it fails"

Dead title patterns

• ALL CAPS or excessive punctuation
• "[Question for the community]" / "[Help]" / "[Question]" prefixes
• Vague titles ("Help" / "Anyone else?")
• Marketing slogans ("The [adj] way to [verb]")
• "Am I the only one who..." engagement bait
• ShowHN-style "I built X" without specifics — wrong platform tell

Timing

• US-skewed subs: Tue–Thu 9–11 AM ET and 7–9 PM ET. Weekend mornings for hobby subs.
• EU/global subs (r/MachineLearning, r/programming): morning ET captures EU evening. Avoid 2–6 AM ET.
• Posting late = new-queue bury. Velocity must hit in the first 30 minutes.

Saturated-take detection per sub

Always run a /new/ sample on each target sub. If 5+ recent posts hit the same angle in the last 48h and most are underperforming, mark the angle saturated for that sub.

Examples

Example 1: Per-sub recommendation with mod-clearance

Bad — pan-Reddit recommendation, no mod check:

Submit a post about your new RAG framework to r/MachineLearning and r/SaaS.

Good — per-sub, mod-cleared, format-prescribed:

Target: r/LocalLLaMA (412k subscribers)
Mod-rules flags checked: no AI-content disclosure required; self-promo allowed if technical depth is genuine; flair required (choose "Discussion" or "Resources").
Format mix this week: text 62% / link 28% / image 10%.

Angle: "Quantization tradeoffs we measured for {{niche model}} at 4-bit vs 8-bit on consumer GPUs"
- Format: text post with embedded chart screenshot
- Flair: Resources
- Title pattern: specific result + setup
- Why it's open: 3 quantization posts in last week, all anecdotal; gap is rigorous measurement
- Mod-clearance: passes
- Survival probability: high — OP-engagement readiness is critical (be ready to answer hardware questions in first hour)

DO NOT submit to r/MachineLearning — mod rules require paper / arxiv link for the [R] flair, anecdote-based posts get removed.

Example 2: Flagged post as teaching example

r/programming — "AI will replace developers in 2 years"
3.2k upvotes, 487 comments, 62% upvote ratio. Now [removed by mods].
Reason inferred: low-effort hot take, no technical content, breaks rule 1 (must be programming content).
Lesson: this sub will surface low-effort hot takes briefly via upvote velocity, then remove them. Do not target this angle.

Checklist

Before returning the Reddit section of the brief:

• Every recommended sub had its rules / sidebar checked at runtime
• Every cited post has subreddit, title (verbatim), upvotes, upvote ratio, comments, OP karma band, URL, age
• Removed / flagged posts surfaced when visible — they teach what the sub rejects
• Scored per sub, not pan-Reddit
• Format mix per sub noted (text / link / image / video percentages this week)
• Per-sub culture map applied — recommendations match sub's actual norms
• (Opt-in mode only) Title bank entries each fit one of the title patterns from this skill
• Dead-title-pattern list applied — no recommended title uses ALL CAPS, vague help asks, engagement bait, or marketing slogans
• (Opt-in mode only) OP-engagement plan included — what top comments to anticipate in first hour
• (Opt-in mode only) Crosspost order recommended if relevant (smaller niche sub first for velocity, then larger)
• All background tabs closed

Composition / References

• Pairs with social-reddit (content domain) for writing the actual submission body from the brief.
• Use the agent's universal output schema.