xai-stock-sentiment

Real-time stock sentiment analysis using Twitter/X data via Grok. Use when analyzing stock ticker sentiment, tracking retail investor mood, or gauging market reaction to events.

Quality

74%

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Securityby

Low

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tessl review fix ./skills/xai-stock-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 with executable code across many sentiment use cases, but the body is verbose due to repeated function patterns and lacks both a sequenced workflow with validation and any progressive disclosure into reference files.

Suggestions

Collapse the eight near-identical functions into one parameterized helper plus a small table of prompt templates, drastically cutting repetition and token cost.

Add validation/error-recovery guidance for batch calls (e.g., watchlist monitoring) — retry on rate limits, sanity-check returned JSON before acting on alerts.

Move the bulk of the function library and JSON schemas into a references/ file (e.g., FUNCTIONS.md), keeping SKILL.md as a concise overview that links to it.

Dimension	Reasoning	Score
Conciseness	Mostly code rather than concept-explanation, but the eight near-identical function bodies (same client.chat.completions.create pattern with large embedded JSON templates) are heavily repetitive and could be collapsed into a single parameterized helper.	3 / 5
Actionability	Provides fully executable, copy-paste-ready Python covering the common cases (single stock, multi-stock comparison, earnings, sector, unusual activity, watchlist), with a runnable Quick Start example.	5 / 5
Workflow Clarity	The content is a collection of independent utilities rather than a sequenced workflow, and batch operations like watchlist monitoring lack any validation or error-recovery checkpoints, capping this dimension at 3.	3 / 5
Progressive Disclosure	Sections are clearly headed, but the 380+ line monolithic body inlines eight function definitions and large JSON schemas that would be better placed in a separate reference file; no bundle files exist to offload detail.	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, well-structured description that clearly states the capability and provides explicit trigger guidance with concrete use cases. Minor overlap risk with general sentiment skills is the only weakness.

Dimension	Reasoning	Score
Specificity	Names the domain ('Real-time stock sentiment analysis using Twitter/X data via Grok') and lists several concrete actions ('analyzing stock ticker sentiment, tracking retail investor mood, or gauging market reaction to events'), with only minor coverage gaps.	4 / 5
Completeness	Explicitly answers both what it does and when to use it with a clear 'Use when...' clause and concrete trigger phrases, matching the top anchor.	5 / 5
Trigger Term Quality	Includes natural phrases users would say ('stock ticker sentiment', 'retail investor mood', 'market reaction to events', 'Twitter/X data'), though a few common synonyms are missing.	4 / 5
Distinctiveness Conflict Risk	The stock/financial sentiment niche via Grok is mostly distinct, but there is minor overlap risk with a general 'xai-sentiment' skill referenced in the body.	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-stock-sentiment/SKILL.md
Commit: 93ed392

Reviewed: 1 day ago

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