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.

1.31x

Quality

61%

Does it follow best practices?

Impact

97%

1.31x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/all-skills/skills/twitter-algorithm-optimizer/SKILL.md

Quality

Content

56%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 skill is actionable with concrete example rewrites and a clear optimization workflow, but it is a verbose, monolithic document that repeats guidance across several sections and inlines reference material that should be split into bundle files. Tightening and externalizing would materially improve it.

Suggestions

Consolidate the redundant framing sections ('When to Use', 'What This Skill Does', 'When to Ask') into a single concise trigger section to cut substantial length.

Move the detailed algorithm architecture (Real-graph, SimClusters, TwHIN, Tweepcred, feed pipeline) into a references/ file, keeping only an actionable summary in SKILL.md with a clearly signaled link.

Trim the explanatory 'how each model works' padding; assume Claude can act on the strategy and signal lists without the conceptual background.

Dimension	Reasoning	Score
Conciseness	The ~317-line body is noticeably verbose with overlapping sections ('When to Use', 'What This Skill Does', 'Optimization Strategies', 'How to Optimize', 'Best Practices', 'Common Pitfalls', 'When to Ask') that restate the same guidance, plus background explanation of how each model works.	2 / 5
Actionability	Concrete before/after (❌/✅) tweet rewrites, per-signal trigger lists, and a 4-step optimization process give mostly executable guidance for this instruction-only skill, with only minor gaps.	4 / 5
Workflow Clarity	'How to Optimize Your Tweets' lays out a clear 4-step sequence (Identify → Map → Optimize → Check Negatives); no destructive/batch operations are present so no validation cap applies, though explicit checkpoints are light.	4 / 5
Progressive Disclosure	Headers give good in-file structure, but with no bundle files and no external references, the detailed algorithm architecture and example library that belong in separate files are all inlined in one monolithic document.	3 / 5
	Total	13 / 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 states concrete capabilities clearly in third person and occupies a distinct niche, but it lacks an explicit 'Use when...' trigger clause, capping completeness. Adding a concrete usage-trigger sentence would raise it to the top band.

Suggestions

Append an explicit trigger clause, e.g. 'Use when a user wants to maximize a tweet's reach, debug an underperforming tweet, or rewrite a draft for better engagement.'

Add a couple of natural synonyms users say ('Twitter post', 'X post', 'go viral', 'tweet thread') to broaden trigger-term coverage.

Tighten 'maximum reach using Twitter's open-source algorithm insights' — it borders on an over-claim; keep it concrete and verifiable.

Dimension	Reasoning	Score
Specificity	Names the domain (tweets) and several concrete actions — 'Analyze and optimize tweets', 'Rewrite and edit user tweets to improve engagement and visibility' — giving broad but not fully enumerated coverage.	4 / 5
Completeness	The 'what' is clear and concrete, but there is no explicit 'Use when...' clause or equivalent trigger guidance, which caps completeness at 3 per the judging guidelines.	3 / 5
Trigger Term Quality	Natural user-facing terms like 'optimize tweets', 'maximum reach', 'engagement', 'rewrite' are present, though a few common synonyms (e.g. 'Twitter/X post', 'go viral') are missing.	4 / 5
Distinctiveness Conflict Risk	The Twitter-algorithm-optimization niche is distinct with specific triggers, with only minor overlap risk against generic writing/editing skills.	4 / 5
	Total	15 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: davepoon/buildwithclaude
Path: plugins/all-skills/skills/twitter-algorithm-optimizer/SKILL.md
Commit: a9fa86d

Reviewed: about 18 hours ago

Table of Contents

Discovery Implementation Validation

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.