git:20260903.e492851 to git:20260905.4909a33

- 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 in browser sessions whenever the user names Reddit."

- compatibility: "Octoweb browser access. Logged-out works for most surfaces; logged-in needed for personalized feeds."

- This skill carries the platform-specific mechanics the trend-harvesting 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.

- 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.

- ### 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.

- ```

- 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.