Skill Compose is an open-source platform for building and running AI agents that use modular skills. It is intended for creating skill-powered agents without workflow graphs or a command-line interface, and the catalogue skills are examples of those agent capabilities.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dp-archive/archive --skill twitter-algorithm-optimizergit clone --depth 1 https://github.com/dp-archive/archiveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dp-archive/archive/twitter-algorithm-optimizer)<a href="https://agentmods.dev/skills/dp-archive/archive/twitter-algorithm-optimizer"><img src="https://agentmods.dev/badge/skills/dp-archive/archive/twitter-algorithm-optimizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dp-archive/archive/twitter-algorithm-optimizer"><img src="https://agentmods.dev/badge/skills/dp-archive/archive/twitter-algorithm-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.02779 |
| Opus 5.5 | $0.00016 | $0.01112 |
| Sonnet 5.5 | $0.00008 | $0.00556 |
| Haiku 4.5 | $0.00004 | $0.00278 |
Grade A, and why
twitter-algorithm-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- twitter-algorithm-optimizer — 100% identical, 6 lines differ
- twitter-algorithm-optimizer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Twitter Algorithm Optimizer
When to Use This Skill
Use this skill when you need to:
- Optimize tweet drafts for maximum reach and engagement
- Understand why a tweet might not perform well algorithmically
- Rewrite tweets to align with Twitter's ranking mechanisms
- Improve content strategy based on the actual ranking algorithms
- Debug underperforming content and increase visibility
- Maximize engagement signals that Twitter's algorithms track
What This Skill Does
- Analyzes tweets against Twitter's core recommendation algorithms
- Identifies optimization opportunities based on engagement signals
- Rewrites and edits tweets to improve algorithmic ranking
- Explains the "why" behind recommendations using algorithm insights
- Applies Real-graph, SimClusters, and TwHIN principles to content strategy
- Provides engagement-boosting tactics grounded in Twitter's actual systems
How It Works: Twitter's Algorithm Architecture
Twitter's recommendation system uses multiple interconnected models:
Core Ranking Models
Real-graph: Predicts interaction likelihood between users
- Determines if your followers will engage with your content
- Affects how widely Twitter shows your tweet to others
- Key signal: Will followers like, reply, or retweet this?
SimClusters: Community detection with sparse embeddings
- Identifies communities of users with similar interests
- Determines if your tweet resonates within specific communities
- Key strategy: Make content that appeals to tight communities who will engage
TwHIN: Knowledge graph embeddings for users and posts
- Maps relationships between users and content topics
- Helps Twitter understand if your tweet fits your follower interests
- Key strategy: Stay in your niche or clearly signal topic shifts
Tweepcred: User reputation/authority scoring
- Higher-credibility users get more distribution
- Your past engagement history affects current tweet reach
- Key strategy: Build reputation through consistent engagement
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 327 lines · 41 tokens per session scan A c02a4b7ca7f2
twitter-algorithm-optimizer is a skill published in the GitHub repository dp-archive/archive (1,105 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 41 tokens to every session and 2,779 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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