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.
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.
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
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
Specific result + setup — "Ran [X] on [Y]: here's the [Z] result"
Contested claim — "I think [X] is wrong. Here's my [data/setup/code]"
Honest failure — "[X] didn't work. Here's what I tried and what broke"
Novel artifact — "[New thing] I built / paper I read — sharing for discussion"
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.
