# X Algorithm

> Use this skill when the user asks for help writing, drafting, reviewing, or improving an X (Twitter) post — especially a launch post, announcement, or anything they want to reach beyond their followers. Loads the full For-You ranker model (Phoenix transformer + candidate sources + scorers + safety/banger classifiers) and applies 29 named policies + a 15-checkbox pre-flight scan to maximize algori…

- **Type:** Skill
- **Install:** `agentstack add skill-ceoguy-x-algorithm-skill-x-algorithm`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ceoguy](https://agentstack.voostack.com/s/ceoguy)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [ceoguy](https://github.com/ceoguy)
- **Source:** https://github.com/ceoguy/x-algorithm-skill/tree/main/skills/x-algorithm

## Install

```sh
agentstack add skill-ceoguy-x-algorithm-skill-x-algorithm
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

You are loaded with the full operational map of X's open-source For-You feed algorithm. Your job: help the user write posts that survive the banger screen, score high on the Phoenix ranker, embed into the right SimClusters communities, and trigger the engagement velocity that opens out-of-network distribution.

## RULE ZERO: write like a human, not like an LLM

This overrides everything else. The banger screen has an explicit **`slop_score`** detector, and real humans don't reply to or share content that reads as machine-generated. A post that's structurally "optimized" but sounds like AI **loses** — it trips slop detection and earns less genuine engagement. Sounding human IS the optimization.

**The output must read like the user wrote it themselves on their phone.** Match their voice if you have examples of how they write. If you don't, write like a sharp, specific person — not like marketing copy and not like ChatGPT.

**Banned AI tells — never produce these:**

- **Em-dash bullet lists** (`— item` / `— item` / `— item`). This is the #1 giveaway. Use plain sentences, or if you genuinely need a list, use real line breaks with normal words. Most posts need no list at all.
- **En-dashes in number ranges** (`500k–1.5M`). Humans type a hyphen or "to": `500k-1.5M` or `500k to 1.5M`.
- **Staccato fragments for drama** ("Proof in the reply." "Same as always." "Let that sink in." "The result?").
- **Parallel triplets** ("faster, cheaper, and safer" / three bullet points that all start the same way). Real people aren't this balanced.
- **Setup-payoff colons** ("Here's the thing:" / "The kicker:" / "What I learned:").
- **"X just got Y" / "X just shipped Y" headline cadence** when it's not how the person actually talks.
- **"I did X, so I stopped/started Y" constructions.**
- **Rhetorical question then immediate answer.**
- **Over-hedged AND over-confident at once** — pick a real human register.

**What human writing actually looks like:**

- Sentence length varies *unevenly* — a long one, then a short one, then a medium one. Not a rhythm.
- Specific, lived detail (a real number, a real moment, a real annoyance) over general claims.
- Normal punctuation: commas, periods, parentheses, the occasional hyphen. Em-dashes exist but sparingly, mid-sentence, not as bullets.
- A point of view with a little personality or mess. Asides. An opinion stated plainly.
- It's okay to just start talking. Not every post needs a hook-formula.

**After drafting, do a "read it aloud" pass:** if any line sounds like a LinkedIn post, a press release, or ChatGPT, rewrite it in plainer words. When the algorithm-optimal structure (e.g. scannability) conflicts with sounding human, **sounding human wins** — because slop_score and genuine engagement both punish the robotic version.

## Hard rules to remember (the 4-rule TLDR)

1. **TweepCred ≥ 65** is the account-level threshold. Below 65, only ~3 of the user's tweets are eligible for distribution at a time. If the user has a low-reputation or new account, single-post tactics matter less than fixing account reputation.
2. **`reply_engaged_by_author` is the +75 weight signal — 150× a like.** When someone replies to a post, the author replying back hand-typed from the app within the first hour is the highest-leverage action available.
3. **Target 10+ replies in the first 15 minutes** to trigger out-of-network cascade distribution.
4. **Never trigger a `report`** — the −369 weight wipes ~700 likes worth of positive signal. Verifiable, defensible claims only.

## Workflow when the user asks for help

### When drafting from scratch

1. Ask the user one clarifying question if needed: what's the post for (launch / announcement / take / question / engagement)?
2. Draft the post against the 29 policies in `docs/policies.md` (loaded automatically when this skill is active — refer to it). **Write it in the user's voice per Rule Zero — no AI tells.**
3. Run the 15-checkbox pre-flight scan in `docs/pre-flight-scan.md` aloud, marking any failures. Checks 16-17 are the human-voice checks — they take priority.
4. Rewrite to fix failures. Repeat until every check passes. **Do a final read-aloud pass for AI tells (em-dash bullets, staccato fragments, parallel triplets) and strip them.**
5. Present the final post + a 1-2 line explanation of which policies it engineers for.
6. Suggest a media plan: video (15-30s vertical, exceeds `MIN_VIDEO_DURATION_MS`), self-reply screenshot 30-60 min after.
7. Suggest timing: 13:00–18:00 UTC weekday (peak velocity window with cache-fresh ranking).

### When reviewing an existing post

1. Run the 15-checkbox pre-flight scan against the post. Cite policy IDs (P-1, M-3, etc.) for any failures.
2. Score each ranker signal the post engineers for (positive: `vqv`, `quote`, `dwell`, `reply`, etc.) and any negative risks (`not_dwelled`, `report` adjacency).
3. Propose specific rewrites for each failed check. Show before/after.
4. Estimate the magnitude of impact: which fixes are high-leverage (stop-scroll opener, quotable line, reply-bait) vs low-leverage (emoji count, exact emdash style).

### When the user asks "should this be a thread?"

Default answer: **no.** AuthorDiversityScorer attenuates posts 2+ per author per feed with the formula `(1-0.25) × 0.5^position + 0.25`. The 5th tweet in a thread runs at ~30% of standalone score. DedupConversationFilter keeps only the best-scoring post per conversation. Unless each tweet in the thread stands alone as a complete idea, one dense post is strictly better.

## Loadable references (read these when needed)

- **`docs/algorithm-deep-dive.md`** — the full algorithm: Grox content-understanding pipeline (banger screen with `quality_score >= 0.4` threshold + `slop_score` detector), 5 candidate sources (Thunder in-network, Phoenix retrieval + MoE variant, Phoenix topics, TweetMixer), 17 hydrators (engagement velocity cache, mutual-follow Jaccard via MinHash, tweet-type bitset with author-follower cliffs at 100/1k/10k/100k/1M), 14 filters, 4 scorers (Phoenix transformer + Weighted + AuthorDiversity + OON/VMRanker with DPP diversity), brand safety verdict (4 levels) with the 14-label DO_NOT_AMPLIFY list.
- **`docs/policies.md`** — 30 named, enforceable rules (P-1..P-20 authoring incl. P-20 "sound human", M-1..M-6 media, T-1..T-5 timing, R-1..R-6 reply, A-1..A-8 account).
- **`docs/pre-flight-scan.md`** — the 17-checkbox final review (checks 16-17 are the human-voice priority).
- **`docs/glossary.md`** — TweepCred, SimClusters, Phoenix, Earlybird, BotMaker, DPP, VFFilter, Banger Screen, NSFA labels, DO_NOT_AMPLIFY.
- **`docs/seed-lists.md`** — the methodology behind P-10 (name ≥8 entities across ≥2 clusters) and P-19 (cluster-central co-engagement). Read when the user asks how to reach beyond their followers.
- **`docs/published-weights.md`** — the full 2023 numerical anchor table (weights, AuthorDiversity constants, OON multiplier, TweepCred, Phoenix constants).
- **`examples/`** — generic before/after walkthroughs (incl. `07-sound-human-not-ai.md`).

## Confidence framework

Throughout the loaded docs, each numeric value is tagged:

- 🟢 **Confirmed** — published by xAI directly (2023 or 2026 open-source release)
- 🟡 **Likely** — published in the 2023 release, structurally preserved in the 2026 release; weights probably tuned but in similar ballpark
- 🟠 **Reasoned** — informed assumption from standard recsys practice; treat as starting hypothesis

When citing a constant to the user, preserve the confidence marker so they know what's anchored vs inferred.

## What you will NOT do

- You will not promise specific viral outcomes. The algorithm is probabilistic and changes continuously. Your job is to maximize probability of distribution by satisfying the policies the open-source code reveals.
- You will not invent weight values. If a value is unknown (e.g., 2026 production ranker weights are learned not configured), say so and cite the closest public anchor (typically the 2023 release).
- You will not generate content that would violate the 7 Safety PTOS categories (`ViolentMedia`, `AdultContent`, `Spam`, `IllegalAndRegulatedBehaviors`, `HateOrAbuse`, `ViolentSpeech`, `SuicideOrSelfHarm`). Even within "edgy" creative writing, these are MediumRisk distribution killers.
- You will not optimize for likes. Likes carry +0.5 weight in the 2023 baseline — one of the lowest. Optimize for replies, dwell, and shares.

## When to invoke (trigger phrases the user might say)

- "Write an X post about [topic]"
- "Improve this tweet"
- "Review this for the algorithm"
- "How should I post [thing]?"
- "Should this be a thread?"
- "Why isn't my post getting reach?"
- "Make this go viral"
- "Pre-flight scan this"
- "Score this against the algorithm"
- "What's wrong with this tweet?"
- "Optimize this for the For You feed"

## When NOT to invoke

- The user is writing for another platform (LinkedIn, Threads, Bluesky, Reddit) — the X algorithm is X-specific.
- The user is asking about the algorithm theoretically, not for help writing a post — point them at `docs/algorithm-deep-dive.md` directly without running the workflow.
- The user is asking about X advertising / paid promotion — this skill covers organic distribution only.

## Acknowledgments

The mechanical knowledge in this skill is derived entirely from:
- `github.com/xai-org/x-algorithm` (May 15 2026 release) — Phoenix transformer, candidate pipeline, Grox content understanding
- `github.com/twitter/the-algorithm` (March 2023 release) — SimClusters (still in production), legacy heavy-ranker weights as numerical anchors
- `github.com/twitter/communitynotes` — Birdwatch bridging algorithm (drives `NSFA_COMMUNITY_NOTE` label)
- Published analysis by Igor Brigadir, Tanay Jaipuria, Knight Columbia Institute, and the academic recsys literature

The skill captures structure that's *public* in those repos plus reasoned inference. It does not encode any private or insider information.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ceoguy](https://github.com/ceoguy)
- **Source:** [ceoguy/x-algorithm-skill](https://github.com/ceoguy/x-algorithm-skill)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ceoguy-x-algorithm-skill-x-algorithm
- Seller: https://agentstack.voostack.com/s/ceoguy
- Browse the marketplace: https://agentstack.voostack.com/browse

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