A reddit marketing degree, taken literally. 8 courses. 1 registrar. 1 live grader. No PDFs.

No, it is not an accredited degree. It is a descriptive frame for the curriculum S4L hardcodes inside scripts/engagement_styles.py. The file defines 8 named engagement styles (critic, storyteller, pattern_recognizer, curious_probe, contrarian, data_point_drop, snarky_oneliner, recommendation), one registrar (the STYLES dict), and a grader (get_dynamic_tiers) that re-ranks the styles by live avg_upvotes from the local posts table. Every Reddit reply drafted by the pipeline must declare which 'course' it took, that declaration is logged, and the next drafter reads those logs to decide which courses to emphasize. The frame 'degree' captures the structural fact that this is coursework with prerequisites, forbidden electives, and a live rubric, not a tactical checklist.

/Users/matthewdi/social-autoposter/scripts/engagement_styles.py. Open it and the entire syllabus is the top of the file: STYLES dict at lines 15-86, PLATFORM_POLICY at lines 104-125, MIN_SAMPLE_SIZE = 5 at line 129, _fetch_style_stats at lines 132-159, get_dynamic_tiers at lines 162-211, and get_styles_prompt at lines 216-284 (which is what the drafter actually reads). Every piece of behavior described on this page traces back to one of those spans. Nothing is abstracted into a third-party service.

A statistical test would be more correct and more expensive. The trade-off here is deliberate: 5 is a low bar that kills two failure modes cheaply. First, it blocks a single viral post from anointing a whole style as 'dominant.' Second, it survives cold start, where a platform has 0-3 posts of any kind and a z-score would be meaningless. With n=5 as the floor, every style gets at least 5 real attempts before its avg_upvotes is treated as a signal. That buys enough samples to be non-accidental without introducing scipy as a dependency. The choice lives at line 129 as a bare constant, not a config variable, because raising it further would starve the explore pool and lowering it would reward noise.

Reddit replies with a single question read as 'AMA bait' or 'survey post' and consistently underperform. The same opener on Twitter or LinkedIn reads as professional curiosity and performs normally. This is not a measurement from the posts table; it is a hardcoded brand rule, the kind of rule that exists because it was learned once painfully and never needs to be re-learned. PLATFORM_POLICY['reddit']['never'] = ['curious_probe'] enforces it at the prompt layer: the drafter never sees curious_probe as an option for a Reddit reply. Even if the posts table later showed curious_probe's avg_upvotes climbing, the ban would still apply. Rules gate results.

The content rule in get_content_rules('reddit') is explicit: 'Go BIMODAL: either 1 punchy sentence (<100 chars, highest avg upvotes) or 4-5 sentences of real substance. AVOID the 2-3 sentence dead zone.' The pattern comes from observing that 2-3 sentence Reddit replies look like they were copy-edited by marketing: too long to be casual, too short to be substantive. They read as 'a pro wrote this' and get filtered by readers' spam detectors. One sentence reads as native. Five reads as an expert actually helping. Three reads as an ad. The rule is enforced only in prompting; there is no programmatic length gate, so bimodal compliance is measured in the drafter's output, not the pipeline layer.

The query filters WHERE status='active' AND engagement_style IS NOT NULL AND our_content IS NOT NULL AND LENGTH(our_content) >= 30 AND upvotes IS NOT NULL. Anything archived, deleted, or soft-closed (status != 'active') falls out of the grade. Posts with no recorded upvote data are invisible to the grader. There is no time-decay weight, which is deliberate: a style that stopped working six months ago is still worth remembering as 'rare,' because the mechanism of why it failed usually still applies. If a post is truly no longer representative, the operator flips its status. Everything else sits in the grade forever or until upvote counts are refreshed by scrape_reddit_views.py.

Two ways. One, it only exists in the reply pipeline (REPLY_STYLES = VALID_STYLES | {'recommendation'} at line 92), not the posting pipeline. You cannot open a brand-new Reddit thread in the recommendation style. Two, it carries an explicit cap: the styles prompt instructs the drafter to use it in at most 20% of replies. The reason is structural: recommendation is the only style that links to a product (from config.json). Overusing it shifts the account from contributor to advertiser in Reddit's implicit ranking, which is the fastest way to lose the ability to post at all. The 20% cap is a prompt-level instruction, not a hard guard. Prompt-level is sufficient because the drafter's own output is logged, and a pattern of recommendation-heavy replies would show up in the posts.engagement_style column and be visible in any audit.

That varies by content_angle and changes whenever upvote data changes. Style leaderboards are read live per run via _fetch_style_stats, so any claim like 'storyteller dominates Reddit' would be wrong the moment the posts table updates. What is stable: curious_probe never appears (banned), recommendation is capped at 20% (cap), 'pleaser/validator' style (marked explicitly in the styles prompt as lowest engagement) is avoided across all platforms. Which of storyteller, data_point_drop, pattern_recognizer, contrarian, or critic wins on any given week depends on the content_angle's subreddits and their current voting patterns. A guide that publishes a fixed ranking is lying about the mechanism.

Yes. The STYLES dict is language, not software. Treat it as a taxonomy for your own comments: tag each reply with one of the 8 style names in a spreadsheet, record upvotes after 48 hours, compute your own rolling avg per style, forbid curious_probe on Reddit, cap recommendations at 20%, never produce 2-3 sentence replies, and never start with 'exactly,' 'yeah totally,' '100%,' or 'that's smart.' The whole point of publishing the source is that the rubric survives outside the pipeline. The pipeline only buys speed.

A prerecorded course is a frozen rubric. It teaches 8 tactics in a fixed order and never re-ranks them. If storyteller-style replies stopped working on r/Entrepreneur tomorrow, the course would still recommend them. S4L's grader recomputes the rank on every run from your own posts table, which means the curriculum decays fast and updates fast. Second, the Udemy/Coursera courses almost never name a style and almost never name a forbidden one. Their mental model is 'here is what works,' ours is 'here are 8 hypotheses, these 1-2 are currently supported, these are explore, this set is banned for brand reasons.' The degree-frame is more honest about what is known and what is still being tested.

Adopt MIN_SAMPLE_SIZE = 5 for yourself. Pick any way you segment your Reddit comments (by style, by subreddit, by opener, by time of day). Refuse to call any segment 'the winner' until you have at least 5 comments in that segment. That one rule kills most of the bad generalizations people make after two or three lucky comments. The rest of the curriculum (named styles, platform bans, 20% recommendation cap, bimodal length) matters more as you scale, but the 5-sample floor matters from day one.