twitter-algorithm-optimizer
Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
Install with Tessl CLI
npx tessl i github:ComposioHQ/awesome-claude-skills --skill twitter-algorithm-optimizer61
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
67%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description effectively communicates specific capabilities around Twitter optimization with concrete actions and a clear methodology. However, it lacks explicit trigger guidance ('Use when...') and misses some natural keyword variations users might employ. The description would benefit from adding when-clauses and broader trigger terms.
Suggestions
Add a 'Use when...' clause with trigger terms like 'optimize my tweet', 'improve engagement', 'make this go viral', 'Twitter algorithm'
Include keyword variations users naturally say: 'X' (Twitter's new name), 'post', 'thread', 'viral', 'social media engagement'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Analyze and optimize tweets', 'Rewrite and edit user tweets', 'improve engagement and visibility', with clear methodology reference to 'Twitter's open-source algorithm insights' and 'recommendation system ranks content'. | 3 / 3 |
Completeness | Clearly answers 'what' (analyze/optimize/rewrite tweets for engagement) but lacks an explicit 'Use when...' clause or equivalent trigger guidance to indicate when Claude should select this skill. | 2 / 3 |
Trigger Term Quality | Contains relevant keywords like 'tweets', 'Twitter', 'engagement', 'reach', 'visibility', but missing common variations users might say like 'X', 'post', 'viral', 'thread', or 'social media'. | 2 / 3 |
Distinctiveness Conflict Risk | Clear niche focused specifically on Twitter/tweet optimization using algorithm insights - distinct from general social media or writing skills, with specific platform and methodology focus. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides genuinely actionable optimization guidance with excellent concrete examples showing before/after tweet transformations. However, it suffers from severe verbosity—explaining Twitter's algorithm architecture in exhaustive detail that could be condensed or moved to reference files. The lack of progressive disclosure and validation steps for measuring optimization success limit its effectiveness.
Suggestions
Reduce the 'How It Works: Twitter's Algorithm Architecture' section to a brief summary and move detailed explanations to a separate ALGORITHM_REFERENCE.md file
Cut explanatory prose that describes what Claude already understands (e.g., what engagement signals are, why communities matter) and keep only the actionable patterns
Add a validation step to the workflow: 'After posting, track engagement metrics for 24-48 hours and compare against your baseline to verify optimization worked'
Split examples into a separate EXAMPLES.md file and keep only 1-2 representative examples in the main skill
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~350+ lines with extensive explanations of Twitter's algorithm architecture that Claude could infer or doesn't need spelled out in such detail. Concepts like 'engagement signals' and 'community resonance' are over-explained with redundant examples. | 1 / 3 |
Actionability | Provides concrete before/after tweet examples with clear optimization patterns. The step-by-step optimization process and specific signal triggers (likes, replies, retweets) give executable guidance that can be directly applied. | 3 / 3 |
Workflow Clarity | The 4-step optimization process is clearly sequenced, but lacks validation checkpoints. There's no feedback loop for testing whether optimizations actually improved performance or how to iterate based on results. | 2 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files. All content is inline despite being lengthy enough to warrant splitting into separate reference documents (e.g., algorithm architecture, examples, best practices). | 1 / 3 |
Total | 7 / 12 Passed |
Validation
87%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 14 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
Total | 14 / 16 Passed | |
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.