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:davepoon/buildwithclaude --skill twitter-algorithm-optimizer69
Quality
55%
Does it follow best practices?
Impact
97%
1.31xAverage score across 3 eval scenarios
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/all-skills/skills/twitter-algorithm-optimizer/SKILL.mdDiscovery
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 good technical grounding in the algorithm. However, it lacks an explicit 'Use when...' clause which limits Claude's ability to know exactly when to select this skill. The trigger terms could also be expanded to include modern terminology like 'X' and common user phrases.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks to improve a tweet, optimize for engagement, go viral, or mentions Twitter/X algorithm.'
Include additional trigger terms users might naturally say: 'X', 'post', 'viral', 'thread', 'impressions', 'social media optimization'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Analyze and optimize tweets', 'Rewrite and edit user tweets', 'improve engagement and visibility'. Also specifies the method: 'using Twitter's open-source algorithm insights' and 'based on how the recommendation system ranks content'. | 3 / 3 |
Completeness | Clearly answers 'what' (analyze, optimize, rewrite tweets for engagement) but lacks an explicit 'Use when...' clause. The 'when' is only implied through the actions described, not explicitly stated. | 2 / 3 |
Trigger Term Quality | Includes relevant keywords like 'tweets', 'Twitter', 'engagement', 'reach', 'visibility', but missing common variations users might say like 'X' (Twitter's new name), 'post', 'viral', 'thread', or 'social media'. | 2 / 3 |
Distinctiveness Conflict Risk | Clear niche focused specifically on Twitter/tweets and the recommendation algorithm. Distinct triggers like 'Twitter's open-source algorithm', 'recommendation system ranks content' make it unlikely to conflict with general writing or social media skills. | 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 tweet optimization guidance with excellent concrete examples, but is severely bloated with unnecessary explanations of Twitter's algorithm internals. The content would be far more effective at 1/3 the length, with architecture details moved to a reference file and the main skill focused on the optimization workflow and examples.
Suggestions
Cut the 'How It Works: Twitter's Algorithm Architecture' section to 10-15 lines max, or move to a separate ALGORITHM_REFERENCE.md file
Remove redundant explanations - each optimization strategy repeats similar concepts (e.g., 'community resonance' explained multiple times)
Add a validation step to the workflow: 'Post, measure engagement after 24h, compare to baseline, iterate'
Split into SKILL.md (quick reference + workflow) and EXAMPLES.md (detailed before/after optimizations)
| 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 strategies (ask questions, use community language, etc.) are directly actionable and copy-paste applicable. | 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 files (e.g., algorithm architecture, optimization strategies, examples). | 1 / 3 |
Total | 7 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 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.