twitter-algorithm-optimizer
tessl i github:ComposioHQ/awesome-claude-skills --skill twitter-algorithm-optimizerAnalyze 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.
61%
Overall
Validation
Implementation
Activation
Validation
88%| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md line count is 327 (<= 500) | Pass |
frontmatter_valid | YAML frontmatter is valid | Pass |
name_field | 'name' field is valid: 'twitter-algorithm-optimizer' | Pass |
description_field | 'description' field is valid (213 chars) | Pass |
description_voice | 'description' uses third person voice | Pass |
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
compatibility_field | 'compatibility' field not present (optional) | Pass |
allowed_tools_field | 'allowed-tools' field not present (optional) | Pass |
metadata_version | 'metadata' field is not a dictionary | Warning |
metadata_field | 'metadata' field not present (optional) | Pass |
license_field | 'license' field is present: AGPL-3.0 (referencing Twitter's algorithm source) | Pass |
frontmatter_unknown_keys | No unknown frontmatter keys found | Pass |
body_present | SKILL.md body is present | Pass |
body_examples | Examples detected (code fence or 'Example' wording) | Pass |
body_output_format | Output/return/format terms detected | Pass |
body_steps | Step-by-step structure detected (ordered list) | Pass |
Total | 14 / 16 Passed |
Implementation
42%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 |
Activation
68%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 |
Reviewed
Table of Contents
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