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

—

Does it follow best practices?

Impact

97%

1.31x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Quality

Content

77%

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

The content is highly actionable with concrete examples and a clear optimization workflow, but it is verbose in explaining Twitter's algorithm internals and keeps everything in a single large file rather than splitting detailed reference material into separate files.

Suggestions

Tighten or move the "How It Works: Twitter's Algorithm Architecture" section into a separate reference file, keeping only the strategy-relevant essentials inline.

Split the detailed "Engagement Signals Tracked" and per-signal trigger lists into a references/ file referenced one level deep, reducing the inline token load.

Trim redundant restatements (e.g. the "When to Ask for Algorithm Optimization" section largely repeats the frontmatter and "When to Use This Skill").

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient strategy guidance, but it pads substantial space explaining Twitter's algorithm architecture (Real-graph, SimClusters, TwHIN, Tweepcred, the feed pipeline) in prose that could be tightened; not a level-1 wall of definitions, but not lean either.	2 / 3
Actionability	Delivers concrete, executable guidance: named per-signal strategies, before/after "Example Optimization" pairs, and a 4-step optimization procedure, all specific and copy-ready for an instruction skill.	3 / 3
Workflow Clarity	The "How to Optimize Your Tweets" section sequences Step 1 (core message) through Step 4 (check negatives) explicitly; tweet editing is a judgment task rather than a destructive/batch operation, so the missing-feedback-loop cap does not apply.	3 / 3
Progressive Disclosure	No bundle files exist and the ~320-line body is monolithic, with the algorithm-architecture and signal-tracking sections kept inline that would be better split into one-level-deep reference files; headers provide some structure but content that should be separate is not externalized.	2 / 3
	Total	10 / 12 Passed

Description

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.

The description is specific and clearly distinct, but lacks an explicit "Use when..." trigger clause and only partially covers the natural terms users would say when they need this skill.

Suggestions

Add an explicit trigger clause, e.g. "Use when the user wants to maximize tweet reach, fix an underperforming tweet, or optimize drafts for Twitter/X engagement."

Broaden trigger terms to include natural phrasings users actually say, such as "go viral", "tweet performance", "thread", or "boost tweet engagement".

Dimension	Reasoning	Score
Specificity	Names multiple concrete actions — "Analyze and optimize tweets" and "Rewrite and edit user tweets" — tied to a clear domain, matching the anchor that lists several specific actions.	3 / 3
Completeness	It clearly answers "what" the skill does, but there is no explicit "Use when..." trigger clause; per the guidelines a missing explicit trigger caps completeness at 2.	2 / 3
Trigger Term Quality	Includes relevant natural terms ("tweets", "reach", "engagement", "visibility") but misses common variations a user would actually say (e.g. "go viral", "tweet performance", "thread"), so coverage is partial rather than complete.	2 / 3
Distinctiveness Conflict Risk	The Twitter-specific framing ("Twitter's open-source algorithm", tweet optimization) carves out a clear niche unlikely to trigger for unrelated skills.	3 / 3
	Total	10 / 12 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
Commit: 366e45b

Reviewed: about 22 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.