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xai-crypto-sentiment

Real-time cryptocurrency sentiment analysis using Twitter/X via Grok. Use when analyzing crypto sentiment, tracking whale activity, or gauging market fear/greed.

62

Quality

78%

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/xai-crypto-sentiment/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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 delivers copy-paste-ready, executable prompt templates with a clear Quick Start, but it is padded by heavy duplication of the API-call scaffold and buries ~350 lines of specialized templates inline with no reference files to split them. A single documented call helper plus per-case prompts, split into reference files with a JSON-parsing/validation note, would address its main weaknesses.

Suggestions

Factor the repeated client.chat.completions.create scaffold into one documented helper (e.g., ask_grok(prompt)) and show each analysis as just its prompt template, cutting hundreds of duplicated lines.

Add a JSON parsing/validation step — parse the response with json.loads (or use response_format) and handle malformed output — so the advertised -> dict returns match reality and the main failure mode is covered.

Split the specialized analyses (altseason, token, DeFi, NFT, whale, FOMO/FUD) into a references/ file linked one level deep from SKILL.md, keeping only Quick Start and the influencer list inline.

DimensionReasoningScore

Conciseness

The body avoids concept explanations Claude already knows, but eight functions each repeat the identical `client.chat.completions.create(model="grok-4-1-fast", messages=[...])` scaffold with only the inner JSON prompt differing (~350 lines where one helper plus per-case prompts would do). That is noticeable redundancy that could be tightened, matching anchor 3 more than 4.

3 / 5

Actionability

Every example is complete, runnable Python with correct f-string brace escaping and a working example call. Minor gaps keep it from 5: functions are annotated `-> dict` but return the raw string (no `json.loads` or `response_format` guidance), and there is no error handling for malformed JSON or API failures.

4 / 5

Workflow Clarity

Quick Start gives an unambiguous setup-to-call sequence (env var, client init, call), and each function is a single clear action. Missing a 5 because there are no validation checkpoints — no instruction to parse or verify the returned JSON before using it, leaving the main failure mode (invalid JSON from the model) unaddressed.

4 / 5

Progressive Disclosure

Section headers and a References list give structure, but the file is a ~440-line monolith with no bundle files at all; the specialized prompt templates (altseason, token, DeFi, NFT, whale, FOMO/FUD) are exactly the content that belongs in separate one-level-deep reference files. Matches anchor 3: some structure, but content that should be separate is inline.

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 description: concrete actions, natural trigger terms, and an explicit 'Use when...' clause answering both what and when. It sits just below the top anchors on specificity and trigger coverage only because several capabilities from the body (altseason detection, FOMO/FUD, NFT/DeFi sentiment) are unrepresented.

DimensionReasoningScore

Specificity

"Real-time cryptocurrency sentiment analysis", "tracking whale activity", "gauging market fear/greed" list several concrete actions in the crypto domain. Not a 5 because coverage has minor gaps — the body covers altcoin-season detection, FOMO/FUD analysis, and NFT/DeFi sentiment, none of which the description hints at.

4 / 5

Completeness

The description explicitly answers what ("Real-time cryptocurrency sentiment analysis using Twitter/X via Grok") and when ("Use when analyzing crypto sentiment, tracking whale activity, or gauging market fear/greed") with concrete trigger phrases, mirroring the anchor-5 example structure.

5 / 5

Trigger Term Quality

"crypto sentiment", "whale activity", "market fear/greed", "cryptocurrency", "Twitter/X" are natural phrases a user would say. Not a 5 because common synonyms like "Crypto Twitter", "CT", "bullish/bearish", "FOMO/FUD", or specific coin names ("Bitcoin") are absent.

4 / 5

Distinctiveness Conflict Risk

The crypto/Twitter/Grok niche is clearly carved out and distinguishable, but the body lists a general "xai-sentiment" skill, and "gauging market fear/greed" could overlap with it — minor overlap risk with a closely related skill rather than minimal.

4 / 5

Total

17

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
fernandezbaptiste/Skrillz
Reviewed

Table of Contents

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