xai-sentiment

Real-time sentiment analysis on Twitter/X using Grok. Use when analyzing social sentiment, tracking market mood, or measuring public opinion on topics.

Quality

74%

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Securityby

Low

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tessl review fix ./skills/xai-sentiment/SKILL.md

Quality

Content

65%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.

Highly actionable copy-paste code with good topical coverage, but it is verbose due to repeated boilerplate and lacks workflow sequencing, validation checkpoints, and progressive disclosure into separate reference files.

Suggestions

Factor the repeated client-call boilerplate and JSON schema patterns into a shared helper or a single parameterized example to reduce duplication across the eight sentiment functions.

Add a validation/verification step to batch_sentiment (e.g., sanity-checking returned scores and sample sizes) so the batch path has an explicit checkpoint.

Move the detailed per-domain recipes (stock, crypto, timeline, comparative) into a reference file under references/ and keep SKILL.md as a concise overview that links out to it.

Dimension	Reasoning	Score
Conciseness	The body avoids explaining concepts Claude already knows, but eight near-identical functions repeat the same client-call boilerplate and inline JSON schema specs, which could be tightened by factoring shared structure.	3 / 5
Actionability	Every section provides fully executable, copy-paste-ready Python code covering basic, detailed, comparative, timeline, stock, crypto, batch, and alert use cases.	5 / 5
Workflow Clarity	The content is a catalog of standalone recipes rather than a sequenced workflow, and the batch_sentiment operation includes no validation or verification checkpoint, capping this dimension.	3 / 5
Progressive Disclosure	Section headers provide reasonable structure, but ~340 lines of detailed function examples are inlined in SKILL.md with no bundle reference files; the only references are external URLs.	3 / 5
	Total	14 / 20 Passed

Description

83%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.

A strong, concise description that clearly states the skill's purpose and provides an explicit 'Use when' trigger clause with natural keyword phrases. Minor gains are possible by adding synonyms and sharpening separation from the related financial sentiment skills.

Dimension	Reasoning	Score
Specificity	Names the domain ('sentiment analysis on Twitter/X using Grok') and three concrete actions ('analyzing social sentiment, tracking market mood, or measuring public opinion'), falling just short of comprehensive coverage.	4 / 5
Completeness	Explicitly states both what it does ('Real-time sentiment analysis on Twitter/X using Grok') and when to use it ('Use when analyzing social sentiment, tracking market mood, or measuring public opinion on topics').	5 / 5
Trigger Term Quality	Natural user phrases like 'social sentiment', 'market mood', and 'public opinion' are present, but common synonyms such as 'Twitter sentiment' or 'X sentiment' are missing.	4 / 5
Distinctiveness Conflict Risk	The Twitter/X + Grok niche is distinct, but there is minor overlap risk with closely related skills like stock-sentiment and crypto-sentiment that cover sub-domains of this same capability.	4 / 5
	Total	17 / 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: fernandezbaptiste/Skrillz
Path: skills/xai-sentiment/SKILL.md
Commit: 93ed392

Reviewed: 1 day ago

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