What site:s4l.ai returns, and the 7-style engagement taxonomy none of those pages mention.
A site: search for s4l.ai surfaces three pages: the homepage, the under-the-hood walkthrough, and the CPC calculator. They tell you what S4L does. None of them tell you how S4L picks a voice for each comment. The answer is a file called scripts/engagement_styles.py, seven named styles, and a per-platform allow/deny list.
What a site: search actually returns
If you type site:s4l.ai into Google right now, three pages come back. They describe the product at three altitudes: pitch, mechanism, and pricing proxy. That is the whole public surface area, which is why this guide exists: everything below the surface (style selection, platform rules, per-post logging) lives in the code, not on the site.
The 7 engagement styles, straight from the file
Each style has a description, an example opening, a map of where it tends to land, and a one-line constraint (the note field). A comment can only belong to one style.
critic
Points out what is missing, flawed, or naive in the thread. Reframes the problem. Never just nitpick; must offer a non-obvious insight.
storyteller
First-person narrative with concrete details (numbers, dates, names). Leads with a failure or a surprise, never pivots to a pitch.
pattern_recognizer
Names the pattern out loud. Authority through pattern recognition, not credentials. Good in engineering threads.
curious_probe
A single follow-up question about the most interesting detail, prefixed with 'curious because...'. One question only. Never multiple.
contrarian
A clear opposing position backed by experience. Must carry credible evidence, empty hot takes get destroyed.
data_point_drop
One specific, believable metric. The number does the talking. No links. Numbers must be believable, not impressive.
snarky_oneliner
Short, sharp, emotionally resonant. One sentence max. Validates a shared frustration. Banned on and in serious subs.
Source of truth: scripts/engagement_styles.py
The full file is ~270 lines. Here is the top of the STYLES dict and the bottom (snarky_oneliner), because snarky_oneliner is the one style with a platform-level ban.
Per-platform style rules
The PLATFORM_WEIGHTS dict decides which styles dominate, which are secondary, and which are hard-banned for each network.
| Feature | Never list / notes | Dominant picks |
|---|---|---|
| curious_probe (never), everything else allowed | dominant: contrarian, storyteller, snarky_oneliner | |
| X / Twitter | all 7 styles allowed, no 'never' list | dominant: snarky_oneliner, critic, data_point_drop |
| snarky_oneliner (never) | dominant: storyteller, pattern_recognizer, critic | |
| GitHub | snarky_oneliner (never) | dominant: critic, pattern_recognizer, data_point_drop |
| Moltbook | all 7 allowed; agent voice ('my human') | dominant: storyteller, pattern_recognizer, critic |
The picker reads this dict before every comment. A style in never is stripped from the candidate set before the LLM is even prompted. This is why a reply will never read like a Reddit shitpost.
How the style picker sits in the pipeline
Inputs on the left, the style-aware generator in the middle, destinations on the right. The middle node is what every run-*.sh script calls before drafting.
thread + context → style-aware generator → platform
What actually gets logged
After a comment is posted, the pipeline writes a row to the Postgres posts table. The engagement_style column is required, not nullable. That is how the A/B test stays clean: no style, no insert.
The taxonomy, by the numbers
Counts pulled directly from scripts/engagement_styles.py.
of picks land on a dominant style
on a secondary style
on a rare style
cap on 'recommendation' style in reply pipelines
Floor rules every style inherits
The get_content_rules helper returns these constraints regardless of which style was picked. They exist to prevent the pleaser/validator voice that consistently lands at the bottom of the engagement charts.
Shared content floor
- No em dashes, commas and periods only
- Never say 'I built' or 'we built' unless recommending
- Never start with 'exactly', 'yeah totally', '100%', 'that's smart'
- Concrete numbers, dates, timeframes over vague claims
- Contractions, lowercase, fragments allowed (humans write like that)
- Reddit: no product names, no links in top-level comments
- : softer critic framing, no snark, no sarcasm
Run this on your product
S4L applies the same 7-style taxonomy to whichever product you point it at. You brief the angle, we pick the thread, the style, the reply, and log the row.
See pricing →Common questions about site:s4l.ai
What pages show up for site:s4l.ai right now?
Three pages are indexed: the homepage (s4l.ai), /under-the-hood (how the agent browses and posts), and /calculator (a CPC estimator that compares Google Ads cost per click to the implied cost per organic reply). This guide adds a fourth covering the engagement-style taxonomy none of those three describe.
Why does S4L have 7 engagement styles instead of one 'house voice'?
One voice across Reddit and X, GitHub, and Moltbook fails on most threads. A critic tone lands in r/Entrepreneur and dies in r/Meditation. A snarky one-liner is the top style on Twitter and is explicitly banned on . Splitting into 7 labeled styles lets S4L A/B test which style wins per platform, per subreddit, per project.
Where exactly are the 7 styles defined?
In scripts/engagement_styles.py in the social-autoposter repo. The file exposes a STYLES dict (7 entries, each with description, example, best_in map, and a note), a PLATFORM_WEIGHTS dict with dominant/secondary/rare/never tiers per platform, and get_styles_prompt / get_content_rules helpers that every pipeline (post_reddit, engage_reddit, run-twitter, run-, engage-, post_github, moltbook_post) calls to assemble the prompt.
Which styles are banned on which platform?
bans snarky_oneliner (explicit 'never' entry). GitHub bans snarky_oneliner. Reddit bans curious_probe (statements beat questions, 'anyone else experience this?' reads as low-effort). Twitter and Moltbook have no 'never' list. The rules live in PLATFORM_WEIGHTS['<platform>']['never'].
Does the style get logged per post?
Yes. Every row written to the Postgres posts table has an engagement_style column, and the INSERT statement in the workflow explicitly requires it to be non-null. There is also a separate recommendation style used only in reply pipelines, capped at ~20% of replies so that product mentions stay below the organic-engagement threshold.
How does S4L pick between the 7 styles for a given thread?
The picker reads the thread plus top comments, runs the platform-specific prompt block (get_styles_prompt) which biases toward dominant styles (~60%), secondary (~30%), and rare (~10%), and excludes anything in the platform's 'never' list. The LLM then picks one style and drafts the comment under that style's rules (length, first-word constraints, markdown allowed, links allowed).
Why is there a content rule against starting with 'exactly' or '100%'?
Those opening tokens correlate with the 'pleaser/validator' pattern that the engagement-style system explicitly flags as lowest-performing across all platforms. The shared get_content_rules helper forbids them, so every pipeline inherits the same floor.
Can a comment mix two styles?
No. Each post is tagged with exactly one engagement_style in the DB so the A/B signal stays clean. Blending styles would make the per-style engagement numbers un-attributable.
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