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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.

53

Quality

67%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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

Quality

Content

50%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 immediately actionable — executable code, correct auth pattern, well-specified output schemas — but it is heavily padded with repetitive variants of one prompt pattern and lacks any validation or error-handling guidance for responses. Moving variant recipes to reference files and adding a response-parsing/verification step would address the main weaknesses.

Suggestions

Collapse the eight near-identical function templates into one canonical example plus a compact table of prompt variations (stock, crypto, comparative, timeline, batch, alerts) — the duplicated client-call boilerplate is pure token overhead.

Add response validation: parse and check the returned JSON (e.g. json.loads with a retry/repair step on failure), since LLM outputs are not guaranteed to match the requested schema.

Move specialized recipes (stock, crypto, alerts, timeline) into a references/ file and keep SKILL.md as a lean quick-start plus best practices, one level deep.

DimensionReasoningScore

Conciseness

The body contains eight near-identical function templates (basic, detailed, comparative, timeline, stock, crypto, batch, alerts) that each repeat the same client-chat boilerplate with only the prompt string changed — several hundred lines of padded redundancy that could be one example plus a table of prompt shapes. This matches anchor 2 (noticeably verbose, several padded sections) rather than 3, since the volume of duplication goes beyond minor tightening.

2 / 5

Actionability

The Quick Start is copy-paste executable with auth, model name, and a full JSON output schema, and every function template is runnable. Minor gaps keep it at anchor 4 rather than 5: functions are annotated "-> dict" but return the raw string response content, and there is no parsing/validation of the model's JSON output.

4 / 5

Workflow Clarity

The skill presents individual one-shot recipes with no sequencing or validation checkpoints — no response-format verification, error handling, or retry guidance — and batch operations ("Analyze sentiment for multiple topics efficiently") run without any verification step, capping this at 3 per the batch-operations guideline. It is above anchor 2 because each recipe is internally coherent and unambiguous to execute.

3 / 5

Progressive Disclosure

Sections are clearly headed and external references are listed at the end, but ~300 lines of API-variant templates that belong in a separate reference file are inlined in SKILL.md with no bundle files at all. This matches anchor 3 (some structure, content that should be separate is inline) rather than 4, where most content would be appropriately placed.

3 / 5

Total

12

/

20

Passed

Description

70%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 is concise, uses third person, and clearly answers both what the skill does and when to use it. Its main weakness is that the trigger phrases restate the single core capability rather than expanding the range of concrete actions or situational cues.

Suggestions

Differentiate the trigger clause from the capability statement, e.g. "Use when the user asks how people feel about a topic, wants market/social mood tracked, or needs sentiment scored for stocks or crypto on X."

Add one or two natural trigger variations users would actually type, such as "public opinion" synonyms or "social media mood".

DimensionReasoningScore

Specificity

"Real-time sentiment analysis on Twitter/X using Grok" names the domain and one core action; the trigger phrases "tracking market mood" and "measuring public opinion" are restatements of that same capability rather than distinct actions. This matches anchor 3 (domain plus 1-2 concrete actions, not comprehensive) rather than 4, which requires several separate specific actions.

3 / 5

Completeness

Both what ("Real-time sentiment analysis on Twitter/X using Grok") and when ("Use when analyzing social sentiment, tracking market mood, or measuring public opinion on topics") are explicit. The when-clause largely restates the what rather than adding concrete situational cues, so it fits anchor 4 (when could be more specific) rather than 5, while clearly exceeding anchor 3 (when missing or weakly implied).

4 / 5

Trigger Term Quality

"analyzing social sentiment", "tracking market mood", "measuring public opinion", and "Twitter/X" are natural phrases users would say when needing this skill. Common variations like "how people feel about X" or domain-specific triggers (stock/crypto sentiment) are missing, matching anchor 4 (good coverage, a few natural terms missing) rather than 5.

4 / 5

Distinctiveness Conflict Risk

The description carves a clear niche (X/Twitter sentiment via Grok) with triggers unlikely to fire for unrelated skills, matching anchor 4 (mostly distinct, minor overlap risk). It is not a 5 because generic "sentiment" and "public opinion" triggers carry some overlap risk with general sentiment-analysis skills.

4 / 5

Total

15

/

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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