# X Open Source Algorithm Skill

> Score and optimize X/Twitter posts using the actual open source algorithm (xai-org/x-algorithm, Jan 2026). Built from the Phoenix scoring model, weighted scorer, and 19 engagement signal predictions from X's Grok-based recommendation system. Use this skill whenever the user wants to write, optimize, score, or improve any X/Twitter content including tweets, threads, articles, or replies. Also use…

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

## Install

```sh
agentstack add skill-attainmentlabs-x-open-source-algorithm-skill-x-open-source-algorithm-skill
```

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

## About

# X Algorithm Optimizer

Optimize X/Twitter content using the actual January 2026 open source algorithm from `xai-org/x-algorithm`.

## Source Material

This skill is built from three sources:

1. **The actual algorithm code** (Jan 2026): `~/Documents/Work/Social Media Guides/Platform Guides/x-algorithm-2026/`
2. **2026 practical guide**: `~/Documents/Work/David Brand/x-twitter-algorithm-2026.md`
3. **David Cyrus voice profile**: `~/Documents/Work/David Brand/david-cyrus-voice-profile.md` (for @mdavidcyrus posts)

Read `references/phoenix-scoring.md` for the full scoring model details.
Read `references/algorithm-signals.md` for the practical engagement hierarchy and tactics.

## How the Algorithm Actually Works (Jan 2026)

X's "For You" feed is powered by four components:

1. **Home Mixer** (orchestration): assembles the feed through a pipeline of sources, hydrators, filters, scorers, and selectors
2. **Thunder** (in-network): serves recent posts from accounts you follow via in-memory store
3. **Phoenix** (ranking + retrieval): Grok-based transformer that predicts engagement probabilities
4. **Candidate Pipeline** (framework): reusable pipeline stages that run in parallel

The critical path for content creators: Phoenix predicts 19 engagement probabilities for every post, and a Weighted Scorer combines them into a single ranking score.

## The Phoenix Scoring Model

Phoenix predicts these engagement probabilities for every candidate post:

**Positive signals (higher = more reach):**

| Signal | What triggers it | Impact |
|--------|-----------------|--------|
| P(favorite) | Likes | Primary distribution signal |
| P(reply) | Replies to the post | Triggers +75 author engagement bonus |
| P(repost) | Retweets | Strong amplification signal |
| P(quote) | Quote tweets | Engaged discussion signal |
| P(click) | Link/content clicks | Interest signal |
| P(profile_click) | Clicking author's profile | Curiosity/authority signal |
| P(video_quality_view) | Watching video >50% | Only applies to video posts |
| P(share) | Share button usage | High-intent distribution |
| P(share_via_dm) | Sharing via DM | Personal recommendation signal |
| P(share_via_copy_link) | Copying link to share | Off-platform sharing intent |
| P(dwell) | Stopping to read/view | Attention signal |
| P(dwell_time) | How long they stay (continuous) | Depth of engagement |
| P(follow_author) | Following the author | Strongest authority signal |

**Negative signals (higher = penalized):**

| Signal | What triggers it | Impact |
|--------|-----------------|--------|
| P(not_interested) | "Not interested" button | Moderate penalty |
| P(block_author) | Blocking the author | Severe penalty |
| P(mute_author) | Muting the author | Significant penalty |
| P(report) | Reporting the post | Most severe penalty |

**The weighted score formula:**
```
Final Score = sum(weight_i * P(action_i))
```
Positive actions have positive weights. Negative actions have negative weights.

The exact weight values are excluded from the open source release (in a private `params` module), but the 2026 practical data gives us the observed hierarchy:
- Likes: +30 points
- Retweets: +20 points
- Reply with author response: +75 bonus
- Reply alone: +13.5

## How to Use This Skill

### Step 1: Determine Format

Based on the 2026 algorithm data, content formats rank:

1. **X Articles** (algorithm-boosted, $1M monthly prize, highest dwell time)
2. **Video thread** (video + 3-5 tweets, 0.42% engagement rate)
3. **Video single post** (5x text engagement)
4. **Image/GIF thread** (150% more interactions than text)
5. **Text thread** (3x single tweet engagement)
6. **Image/GIF single** (0.08% engagement)
7. **Text single** (0.1% engagement)
8. **Link post Premium** (0.25%, heavily suppressed)
9. **Link post non-Premium** (0% engagement, invisible)

**Decision tree:**
- Deep analysis, 1000+ words? -> X Article
- Punchy take, 5-10 points? -> Thread (7 tweets optimal)
- Quick insight, single idea? -> Single tweet with image/video
- Linking to external content? -> Native content first, link in reply only

### Step 2: Analyze the Draft

For each draft post, evaluate against the Phoenix scoring signals:

**Will this trigger positive signals?**
- [ ] Likely to get likes? (favorite_score) -> clear value, quotable insight
- [ ] Likely to get replies? (reply_score) -> provocative question, contrarian take, reply hook
- [ ] Likely to get retweets? (retweet_score) -> shareable, makes the sharer look smart
- [ ] Likely to get clicks? (click_score) -> curiosity gap, "what happened next"
- [ ] Likely to increase dwell time? (dwell_score) -> thread format, visual content, depth
- [ ] Likely to trigger profile visits? (profile_click_score) -> establishing authority, unique perspective
- [ ] Likely to trigger follows? (follow_author_score) -> demonstrating ongoing value

**Will this trigger negative signals?**
- [ ] Could trigger "not interested"? -> off-topic for your audience, unclear value
- [ ] Could trigger mutes? -> posting too frequently, repetitive content
- [ ] Could trigger blocks? -> aggressive, offensive, spam-like behavior

### Step 3: Optimize for the Algorithm

**The 30-Minute Rule:** The algorithm evaluates engagement velocity in the first 30 minutes. Front-load your best content.

**Premium multipliers:**
- In-network (followers): 4x visibility
- Out-of-network (non-followers): 2x visibility
- Overall: 10x reach advantage (Buffer analysis of 18.8M posts)

**Thread optimization:**
- First tweet: no number, use 🧵 emoji
- Start numbering from tweet 2: "2/7", "3/7"
- Each tweet under 200 characters (250 max)
- Line breaks between ideas
- 1-2 hashtags max, in final tweet only
- Links in final tweet or separate reply
- Optimal length: 7 tweets (5-10 range)

**What kills reach:**
- External links in main post (even Premium: 0.25% engagement)
- 3+ hashtags (engagement drops)
- No visual content (5x penalty vs video)
- Text-only posts (lowest format, 0.1%)
- Slow first-30-minute engagement

### Step 4: Score the Post

Rate each draft on a simplified Phoenix-aligned scale:

| Category | Score 1-10 | Weight |
|----------|-----------|--------|
| Reply potential (will people respond?) | _ | 30% |
| Repost potential (will people share?) | _ | 25% |
| Dwell time (will people stop and read?) | _ | 20% |
| Like potential (quick positive reaction?) | _ | 15% |
| Negative risk (could this backfire?) | _ | 10% |

**Weighted Score = (Reply * 0.3) + (Repost * 0.25) + (Dwell * 0.2) + (Like * 0.15) - (Negative * 0.1)**

Target: 7.0+ for posting. Below 6.0, rewrite.

### Step 5: Rewrite Suggestions

When suggesting rewrites, explain which Phoenix signal you're targeting:

**Example:**
- Original: "Claude's 1M context window is now available."
- Problem: Low reply_score (no reason to respond), low dwell_score (nothing to read)
- Rewrite: "Anthropic just gave Claude a million-token context window. I turned mine off. Here's why. 🧵"
- Why: Opens curiosity gap (click_score), demands engagement (reply_score), signals thread (dwell_score)

## Voice Reference

For @mdavidcyrus posts, read `~/Documents/Work/David Brand/david-cyrus-voice-profile.md` and apply:
- Direct, pragmatic, builder, contrarian
- Executive voice, not content creator
- Lead with insight, not story
- No em dashes, no AI-tell words
- No engagement bait ("Agree?", "Thoughts?")
- Statement energy, end on declarations

## Anti-patterns (from the algorithm code)

The algorithm has explicit **negative signal predictions**. These actions cause the scoring model to suppress your content:

1. **P(not_interested)** increases when: content is off-topic for your community (SimClusters mismatch), repetitive themes, unclear value proposition
2. **P(block_author)** increases when: aggressive tone, spam-like posting frequency, controversial content without substance
3. **P(mute_author)** increases when: over-posting (more than 5-7 daily), thread-bombing, excessive self-promotion
4. **P(report)** increases when: misleading claims, harassment, harmful content

The **Author Diversity Scorer** also penalizes seeing the same author too many times in one feed session. Each subsequent post from the same author gets a decaying multiplier. Posting quality over quantity matters.

## Output Format

When optimizing a post, provide:

1. **Format recommendation** (article / thread / single + why)
2. **Phoenix signal analysis** (which signals this will trigger, which it won't)
3. **Score** (weighted 1-10 scale above)
4. **Specific rewrites** (with signal rationale for each change)
5. **Posting strategy** (timing, first-30-min plan, reply strategy)

## Source & license

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

- **Author:** [attainmentlabs](https://github.com/attainmentlabs)
- **Source:** [attainmentlabs/x-open-source-algorithm-skill](https://github.com/attainmentlabs/x-open-source-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-attainmentlabs-x-open-source-algorithm-skill-x-open-source-algorithm-skill
- Seller: https://agentstack.voostack.com/s/attainmentlabs
- Browse the marketplace: https://agentstack.voostack.com/browse

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