analyzing-text-sentiment
Execute this skill enables AI assistant to analyze the sentiment of text data. it identifies the emotional tone expressed in text, classifying it as positive, negative, or neutral. use this skill when a user requests sentiment analysis, opinion mining, or emoti... Use when analyzing code or data. Trigger with phrases like 'analyze', 'review', or 'examine'.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill analyzing-text-sentiment35
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillAgent success when using this skill
Validation for skill structure
Discovery
42%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description suffers from internal contradictions - it starts describing sentiment analysis for text but then pivots to 'analyzing code or data' with overly generic triggers. The truncated text ('emoti...') suggests incomplete content, and the generic trigger terms would cause frequent false matches with other analytical skills.
Suggestions
Remove the contradictory 'Use when analyzing code or data' clause and replace with sentiment-specific triggers like 'Use when user asks about sentiment, emotional tone, opinion analysis, or wants to understand if text is positive/negative'
Replace generic triggers ('analyze', 'review', 'examine') with domain-specific terms like 'sentiment', 'emotional tone', 'opinion mining', 'positive/negative feedback', 'customer reviews'
Fix the truncated description and use consistent third-person voice throughout (change 'enables AI assistant to analyze' to simply 'Analyzes sentiment of text data')
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (sentiment analysis) and some actions (analyze sentiment, identify emotional tone, classify as positive/negative/neutral), but the description is muddled with contradictory information about 'analyzing code or data' which undermines specificity. | 2 / 3 |
Completeness | Has a 'what' (sentiment analysis of text) and attempts a 'when' clause with trigger phrases, but the 'when' is contradictory - it mentions both sentiment/opinion mining AND 'analyzing code or data' which are different use cases, creating confusion. | 2 / 3 |
Trigger Term Quality | Includes some relevant terms like 'sentiment analysis', 'opinion mining', and generic triggers like 'analyze', 'review', 'examine', but these generic terms are too broad and the description conflates text sentiment with code/data analysis, reducing trigger quality. | 2 / 3 |
Distinctiveness Conflict Risk | High conflict risk due to extremely generic triggers ('analyze', 'review', 'examine') that would match almost any analytical task, plus the contradictory scope mentioning both sentiment analysis and code/data analysis would cause conflicts with multiple other skills. | 1 / 3 |
Total | 7 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill content is a template-style placeholder that provides no actionable guidance for performing sentiment analysis. It explains concepts Claude already understands, lacks any executable code or specific tools/libraries, and contains generic boilerplate sections that add no value. The skill would need to be completely rewritten with actual implementation details.
Suggestions
Replace conceptual explanations with executable Python code using a specific library (e.g., TextBlob, VADER, or transformers) showing how to actually perform sentiment analysis
Remove generic sections like 'How It Works', 'When to Use', and boilerplate 'Instructions' - replace with concrete, copy-paste ready examples
Add specific model recommendations with installation commands (e.g., `pip install textblob`) and actual code snippets that produce sentiment scores
If this skill requires external resources or advanced techniques, create separate reference files and link to them clearly from a concise overview
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive padding explaining concepts Claude already knows (what sentiment analysis is, how it works conceptually). The 'Overview', 'How It Works', and 'When to Use' sections are largely redundant filler that don't add actionable value. | 1 / 3 |
Actionability | No executable code, no specific commands, no actual implementation details. The examples describe what the skill 'will do' abstractly rather than showing how to actually perform sentiment analysis. References to 'pre-trained models' without specifying which ones or how to use them. | 1 / 3 |
Workflow Clarity | The 'Instructions' section is completely generic boilerplate ('Invoke this skill when trigger conditions are met'). No actual workflow steps, no validation checkpoints, no concrete process for performing sentiment analysis. | 1 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files or resources. The 'Resources' section mentions 'Project documentation' and 'Related skills' without any actual links or file references. Content is poorly organized with redundant sections. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Reviewed
Table of Contents
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