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.

Quality

59%

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Securityby

Passed

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tessl review fix ./twitter-algorithm-optimizer/SKILL.md

Quality

Content

52%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-structured and actionable with strong worked examples, but it is monolithic and verbose: the same advice recurs across multiple sections and the detailed algorithm architecture belongs in separate reference files rather than 320 inlined lines.

Suggestions

Move the 'How It Works: Twitter's Algorithm Architecture' deep-dive (Real-graph, SimClusters, TwHIN, Tweepcred, engagement signals, feed pipeline) into a separate references/ file and link to it from SKILL.md, leaving only a concise overview inline.

Eliminate the redundant restating of guidance across 'Optimization Strategies', 'How to Optimize Your Tweets', 'Best Practices', and 'Common Pitfalls' — consolidate to a single canonical treatment of each tactic.

Keep the worked examples and 4-step process as the core of SKILL.md and push the per-signal trigger lists (Likes/Replies/Retweets/Bookmarks) into a reference file to improve both conciseness and progressive disclosure.

Dimension	Reasoning	Score
Conciseness	The ~320-line body noticeably repeats the same guidance across 'Optimization Strategies', 'How to Optimize Your Tweets', 'Best Practices', and 'Common Pitfalls' (e.g., trigger replies, community resonance, avoid engagement bait each appear 3-4 times), which is padded rather than lean.	2 / 5
Actionability	Concrete ❌/✅ tweet rewrites, a defined 4-step optimization process, and three worked examples (developer, product launch, opinion) give mostly executable, actionable guidance for an instruction-only skill, with minor abstraction in the mapping steps.	4 / 5
Workflow Clarity	The 'How to Optimize Your Tweets' section lays out a clear Step 1-4 sequence (identify message, map to algorithm, optimize for signals, check negatives); because this is advisory rather than destructive/batch work, the missing validation checkpoints do not trigger the cap.	4 / 5
Progressive Disclosure	No bundle files exist and there are zero file references; detailed reference-grade material such as the full algorithm architecture (Real-graph, SimClusters, TwHIN, Tweepcred) and the six strategy deep-dives are inlined entirely in SKILL.md rather than split into one-level-deep reference files.	2 / 5
	Total	12 / 20 Passed

Description

66%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific and well-targeted to Twitter algorithm optimization, with good natural trigger terms and a clear niche. Its main weakness is the absence of an explicit 'when to use' clause, which caps completeness at 3.

Suggestions

Add an explicit 'Use when...' clause naming concrete trigger phrases (e.g., 'Use when drafting or revising tweets to maximize reach, or when a tweet underperforms and you want to understand why').

Include natural synonyms users say, such as 'X posts', 'social posts', or 'tweet engagement', to broaden trigger-term coverage toward a 5.

Tighten the redundant action verbs ('optimize', 'rewrite', 'edit') into a single comprehensive action list to push specificity toward 5.

Dimension	Reasoning	Score
Specificity	The description lists several concrete actions ('Analyze and optimize tweets', 'Rewrite and edit user tweets', 'improve engagement and visibility') grounded in a named domain, with only minor redundancy between 'optimize' and 'rewrite/edit' keeping it below a 5.	4 / 5
Completeness	It clearly answers the 'what' (analyze/optimize/rewrite tweets for reach and engagement) but contains no 'Use when...' clause or equivalent explicit trigger guidance, which per the rubric caps completeness at 3.	3 / 5
Trigger Term Quality	Natural terms like 'tweets', 'reach', 'engagement', and 'visibility' appear alongside 'Twitter's... algorithm', giving good keyword coverage, but common synonyms such as 'X', 'social posts', or platform-specific phrasing are missing.	4 / 5
Distinctiveness Conflict Risk	The Twitter algorithm-optimization niche is specific and distinct, with only minor overlap risk against general social-media or writing skills rather than the minimal conflict risk of a 5.	4 / 5
	Total	15 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: ComposioHQ/awesome-claude-skills
Path: twitter-algorithm-optimizer/SKILL.md
Commit: be2a406

Reviewed: 3 days ago

Table of Contents

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