# X Algorithm Optimizer > Optimize tweets and X posts for reach using the open-sourced X (Twitter) For You feed algorithm. Rewrite drafts, diagnose why a post underperformed, and explain engagement, ranking, and shadowban mechanics from the actual xai-org/x-algorithm source — candidate sourcing, filters, Phoenix scoring, and visibility filtering. - Skill: `kleenpulse/x-algorithm-optimizer` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add kleenpulse/x-algorithm-optimizer` - Raw SKILL.md: https://api.skillmd.com/api/skills/kleenpulse/x-algorithm-optimizer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: Apache-2.0 (references xai-org/x-algorithm) - Author: kleenpulse (https://skillmd.com/u/kleenpulse) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kleenpulse/x-algorithm-optimizer --- # X Algorithm Optimizer Grounded in the August 2026 source release: `github.com/xai-org/x-algorithm`. **This supersedes the 2023 `twitter/the-algorithm` model.** Real-graph, TwHIN, Tweepcred, UTEG, tweet-mixer and the search-index candidate source are **not** in the current system and must not be cited. SimClusters is the only named component that survived. ## When to Use - Optimizing a draft post or thread for reach - Diagnosing why a post underperformed - Planning a posting strategy for a niche or launch - Explaining X distribution mechanics to someone Do not use for: LinkedIn/Medium/blog content, general copy editing, or tone work unrelated to distribution. ## Epistemic Rule (non-negotiable) Separate three tiers in every analysis. Never blur them. 1. **In the code** — pipeline stages, filter names, constants, struct fields. State these as fact. 2. **Inferred from the code** — tactical advice that follows from a mechanism but isn't itself in the source. Label it as inference. 3. **Folklore** — "post at 9am", "the first hour decides everything", "links get suppressed". Say plainly when something is not visible in the released code. **The trained weight values are NOT published.** `ScoringWeights` is a public struct with ~40 fields loaded via `params.get()` — no defaults in source. Anyone who tells you a like is worth "0.5" or a repost "20x" is inventing it. Never rank the signals numerically. Rank them *structurally*: which are modeled, which are penalized, which gate distribution entirely. ## How the Feed Actually Works ~500M daily posts → ~1,500 candidates → **35 shipped** (`RESULT_SIZE`), with `TOP_K_CANDIDATES_TO_SELECT = 50`. ### Stage 1 — Candidate sourcing (three parallel sources) | Source | What it does | What it means for you | |---|---|---| | `thunder` | In-memory index of recent posts from accounts the viewer follows | Your followers are the only guaranteed audience | | `phoenix` retrieval | Embeds viewer and post as vectors, returns nearest posts | Out-of-network reach is **semantic**, not keyword or hashtag | | `simclusters` | Clusters accounts by who engages with what | Community coherence is a retrieval mechanism, not a metaphor | There is no keyword search index feeding the For You feed. **Hashtags are not a retrieval path.** Topical clarity matters because it moves your embedding, not because it matches a string. ### Stage 2 — Pre-scoring filters (a post killed here is never ranked) Applied in order: `DropDuplicates` → `CoreDataHydration` → `Age` → `SelfTweet` → `OONRetweetReply` → `PreviouslySeen` → `MutedKeyword` → `AuthorSocialgraph`. Three of these are strategically load-bearing: - **`AgeFilter` — `MAX_POST_AGE = 48 * 60 * 60`.** Hard 48-hour window. A post has two days of candidate life, full stop. Evergreen content does not accrue reach; it must be reposted as new. - **`OONRetweetReplyFilter`** drops reposts *and replies* from accounts the viewer doesn't follow. **Replies essentially cannot reach out-of-network.** This is the single biggest structural fact for content shape. - **`PreviouslySeenPostsFilter`** — one impression per viewer. No second bite. ### Stage 3 — Phoenix scoring A transformer predicts probabilities for ~40 distinct actions. `Final Score = Σ (weight_i × P(action_i))`. Modeled positives: `favorite`, `reply`, `retweet`, `quote`, `share`, `share_via_dm`, `share_via_copy_link`, `click`, `open_link`, `profile_click`, `photo_expand`, `video_open`, `vqv`, `quoted_click`, `quoted_vqv`, `dwell`, `cont_dwell_time`, `cont_click_dwell_time`, `follow_author`, `post_unexplored`. Modeled negatives: `not_interested`, `block_author`, `mute_author`, `report`, **`not_dwelled`**. Five mechanisms in `ranking_scorer.rs` that change how you write: - **`share_via_dm` and `share_via_copy_link` are separately modeled.** Private sharing is a first-class ranked signal. Content worth sending to one person counts, and it never shows up in your public metrics. - **`not_dwelled` is a negative.** A scroll-past is not neutral — it's modeled damage. This is why volume posting is genuinely costly now. - **Click-dwell low-favorability penalty** — `multiplier = (fav / baseline).powf(alpha).max(floor).min(cap)`, damping `click_dwell_time` when favorites come in low relative to baseline. **This is an explicit anti-clickbait term.** A hook that earns the click but not the approval gets its dwell credit cut. - **`bidirectional_follow_reply_weight_boost` / `..._dwell_weight_boost`** — mutual follows get an additive boost on the reply and dwell weights, applied to original posts. **Mutuals are a code-level multiplier.** Building genuine two-way relationships is a ranking strategy, not just etiquette. - **`post_unexplored`** — a novelty term, optionally multiplicative: `base_dwell_time * (1.0 + post_unexplored * alpha)`. Freshness of *idea*, not just timestamp. Dwell-regret scoring modes (`dwell_regret_sigmoid`, `gated_dwell_regret`) compare a post's engagement to cohort averages via a centered ratio. You are scored **against comparable posts**, not on an absolute scale. **Candidate isolation:** during inference candidates cannot attend to each other — each post is scored against viewer context alone. Your score does not depend on what else is in the batch. There is no "competing with a viral post for the slot" at scoring time. Do not give timing advice that assumes there is. ### Stage 4 — Score adjustments - **Author diversity decay:** `(1.0 - floor) * decay^k + floor`, where `k` counts your repeats within the ranked list. Posting many times in a short window means each subsequent post is scored lower **for the same viewer**. Real, and it caps burst posting. - **Out-of-network discount:** a sub-1.0 factor on posts from unfollowed accounts — and per the README, **on replies and reposts even from followed creators**. - **New-author boost:** authors under an impression threshold get an uplift toward a target position (`author_cold_start.rs`). New accounts have a real, temporary tailwind. - **`NEW_USER_OON_WEIGHT_FACTOR = 0.00001`** (for viewers with `< NEW_USER_MIN_FOLLOWING = 5`). A new *viewer's* feed is essentially their follows only. Content aimed at brand-new users cannot arrive out-of-network. ### Stage 5 — VMRanker diversity pass `vm_ranker` reorders scored posts with a **determinantal point process** over their embeddings, trading score for dissimilarity between neighbors. The top-scoring feed is deliberately not the shipped feed. **Being the seventh near-identical take on a trending topic is penalized by construction** — semantic differentiation is worth more than incremental quality on a crowded subject. ### Stage 6 — Visibility filtering (separate service, separate rules) Returns **ALLOW / INTERSTITIAL / DROP** per post-viewer pair. It decides *whether*, never *where*. Post-selection filters `VFFilter`, `AncillaryVFFilter`, and `DedupConversationFilter` apply after order is fixed. Critically: **some rules apply only to out-of-network recommendations** — high-recall spam catching for strangers while followers see the identical post. That asymmetry is the honest technical core of the "shadowban" question: not a score nudge, a label-driven rule. Per-account labels are surfaced in `under-the-hood/`. Label sources feeding it: - `grox` — spam, adult, violent-media classifiers at publish time - `agatha` — offline jobs labeling an account by **how others respond to its posts: blocks, reports, spam reports** - `bdsm` — action-sequence analysis for inauthentic/abusive behavior patterns - `user-cred-v2` — PageRank over the follow graph and engagement edges → per-account score (the structural successor to Tweepcred; do not call it Tweepcred) `agatha` is why engagement-through-outrage is a losing trade: the block and report rates it aggregates are account-level and persistent, and they route into a system that can gate you out-of-network entirely — where all your growth lives. ## Optimization Playbook ### Content shape (from the filters) 1. **Original posts are the growth vehicle. Replies are relationship maintenance.** `OONRetweetReplyFilter` means replies effectively don't travel out-of-network. Reply-guy strategy builds mutuals (real boost) but not reach. 2. **Threads: the hook post carries the distribution.** Continuations are replies and inherit the same limitation. Front-load the whole value proposition in post one; never let it be a teaser. 3. **48 hours, then it's dead.** Plan launches accordingly. Re-post evergreen material as new posts rather than resurfacing old ones. 4. **Quotes are ranked separately** (`quote`, `quoted_click`, `quoted_vqv`) and are not subject to the reply filter the way replies are. Quoting with substantive added value is the stronger amplification move. ### Signal shape (from the scorer) 5. **Write for the private share.** `share_via_dm` and `share_via_copy_link` are modeled. Ask: would someone send this to one specific person? That intent ranks and never appears in your like count. 6. **Kill the scroll-past.** `not_dwelled` is a modeled penalty. First line must stop the thumb or the post is net-negative. 7. **No naked curiosity hooks.** The click-dwell low-fav penalty explicitly punishes clicks that don't convert to approval. Deliver in-post; earn the click *and* the like. 8. **Cultivate mutuals deliberately.** The bidirectional-follow boost is in the source. Fifty real mutuals in your niche beat five thousand one-way followers. 9. **Be differentiable, not just correct.** The DPP pass penalizes semantic neighbors. On a crowded topic, take the angle no one else has rather than the best version of the common one. 10. **Novelty is modeled** (`post_unexplored`). New ideas are scored, not just new timestamps. ### Restraint (from the adjustments) 11. **Space your posts.** Author diversity decay is per-viewer and per-ranked-list. Burst posting devalues your own posts against each other. 12. **Protect account-level standing.** Blocks, reports, and mutes feed `agatha` and `bdsm` at the account level and can gate out-of-network distribution. Manufactured outrage borrows reach against your entire future. 13. **New accounts: exploit the cold-start boost, and expect a follower-only ceiling if your audience is itself new.** ### Do not claim - ❌ Numeric weights or ratios between signals — not published - ❌ "The first hour is critical" / velocity multipliers — **no such term is visible** in the released ranking code - ❌ Hashtag or keyword optimization — no keyword retrieval path into For You - ❌ Link penalties — `open_link` and `click` are modeled **positives**; no link demotion appears in the source - ❌ Optimal posting times — not in the code. Timing helps only through who is online to generate the signals. - ❌ Real-graph, TwHIN, Tweepcred, UTEG, tweet-mixer — retired or renamed ## Working Method **Step 1 — Classify the post.** Original / reply / quote / thread-head / repost. This alone determines the reach ceiling via the pre-scoring filters. State it first. **Step 2 — Audit the funnel.** Does it survive Age, OONRetweetReply, and the muted/blocked filters? A post that dies in filtering can't be fixed by better copy. **Step 3 — Map to modeled signals.** Which of the ~40 heads does this plausibly fire? Name them literally (`share_via_dm`, `profile_click`, `cont_dwell_time`). Then find the one you're leaving on the table. **Step 4 — Check the penalties.** Scroll-past risk (`not_dwelled`)? Clickbait shape (click-dwell low-fav)? Semantic duplicate of the timeline (DPP)? Third post this hour (author diversity)? Block/report risk (`agatha`)? **Step 5 — Rewrite, then explain each change by mechanism.** Every edit cites a real code path or gets labeled inference. No mechanism, no claim. ## Worked Example **Original:** > "We launched a new feature today. Check it out." **Analysis:** - Original post — full reach path available, good. - Survives all pre-scoring filters. - Fires almost nothing: no dwell hook, no private-share value, no `profile_click` pull. High `not_dwelled` risk. - "Check it out" with no in-post payload is the exact shape the click-dwell low-fav penalty is built to dampen. - Semantically near-identical to every launch post → the DPP pass buries it next to its neighbors. **Optimized:** > "Six months on the one thing users asked for most: PDF export. > > Report generation went from 40s to 4s. Live now. > > The hard part wasn't the rendering — it was that our layout engine assumed infinite page height. Here's what we changed:" **Why:** - Concrete numbers → `cont_dwell_time` and bookmark/DM-share intent (`share_via_copy_link`), not a bare announcement. - The specific failure detail is `post_unexplored` territory — a genuinely unexplored angle, and the thing that makes it semantically distinct under the DPP pass. - Value delivered in-post before any ask → no click-dwell penalty exposure. - Technical specificity moves the embedding toward the engineering cluster (`simclusters` / `phoenix` retrieval), rather than into the generic-launch neighborhood. - Zero block/report surface — no `agatha` cost. ## Source `github.com/xai-org/x-algorithm`, Apache-2.0, released August 2026. Model weights, exact coefficient values, and Grok-based policy-violation prediction are excluded from the release. When a question requires those, say so rather than guessing.