Elena Chrysaki

Rewrites or drafts a piece of content (a post, caption, thread, article) to perform well on a specific platform's *actual* ranking algorithm — grounded in that platform's own documented signals (dwell-time thresholds, skip-probability discounting, comment-vs-like weighting, feed-truncation length, sequence modeling, etc.), not generic "post more, use hashtags" engagement tips. Use whenever the user wants to optimize a post for LinkedIn/Instagram/X/Threads/Reddit's algorithm specifically, asks to "rewrite this for the algorithm," wants to know what actually helps a post perform on a given platform, or references a platform's ranking mechanics while drafting content. Pulls directly from the social-media-app-researcher skill's algorithm-and-mindset.md research when it's available (check for a social-app-teardown-style research folder first), and falls back to fresh, cited research when it isn't — never invents a ranking signal. Not for generic copywriting/brand-voice work unrelated to a specific platform's ranking mechanics (that's a normal writing task), and not for actually publishing anything — this only drafts and rewrites; the user posts it themselves.

Produces an exhaustive, categorized research catalog of social media apps (Threads, LinkedIn, Instagram, X/Twitter, Reddit, or others named by the user) — every feature down to granular detail (e.g. the exact reasons offered by a "report post" flow), the ranking/discovery algorithm and the retention/growth mindset behind it, a full UX/UI teardown covering every screen layout, button, and animation with the psychological/design mechanism explaining why it works, a brand & marketing kit when the request has a marketing/branding/design-system angle (color/type/spacing design tokens ready to reuse — including machine-readable JSON/CSS/Tailwind exports, not just a markdown table — brand voice, growth/acquisition funnel mechanics, copywriting patterns), and a psychology & persuasion audit when the request has a behavioral/addiction/dark-pattern angle (Cialdini's six principles, the Hook Model trigger→action→reward→investment loop, and a named dark-patterns audit against Brignull's taxonomy, each mapped to real observed features, not asserted). Use this whenever the user wants to research, catalog, benchmark, or "reverse-engineer" a social media app's features, algorithm, design, brand identity, or psychological mechanics — including requests like "study how Instagram's feed algorithm works," "make a list of every feature Reddit has," "why does the like button animation feel so good," "what makes these apps addictive," "give me X's color palette and design tokens," "how does LinkedIn's brand voice work," "what's Reddit's growth/referral loop," "what dark patterns does Instagram use," "map X's persuasion techniques to Cialdini's principles," or when building a competing social/community product and wanting a reference of what to borrow. Not for posting/scheduling/drafting content on these platforms, checking brand sentiment or mentions (that's social listening), analyzing a user's own account analytics, or building new application functionality (an animation library, a feed component, a working clone) — those are separate tasks; this skill researches how the real platforms are built and why, and its only code-shaped output is a direct, structured export of already-researched design-token values (JSON/CSS/Tailwind), not new original implementation code. For actually rewriting/optimizing a piece of content against a platform's algorithm, see the companion `platform-content-optimizer` skill instead.