analyzing-text-sentiment
tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill analyzing-text-sentimentExecute 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'.
Validation
81%| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 13 / 16 Passed | |
Implementation
7%This skill is a template-style placeholder with no actionable content. It extensively explains what sentiment analysis is (which Claude already knows) while providing zero executable code, no specific model recommendations, and no concrete implementation guidance. The entire document could be replaced with a few lines of actual Python code using a sentiment library.
Suggestions
Replace the conceptual explanations with executable Python code using a specific library (e.g., TextBlob, VADER, or transformers) showing actual sentiment analysis implementation
Remove the 'Overview', 'How It Works', and 'When to Use' sections entirely - Claude already understands sentiment analysis concepts
Add concrete output format examples showing actual sentiment scores and classifications (e.g., JSON schema with polarity, confidence scores)
Replace the generic 'Instructions' and 'Error Handling' placeholder sections with specific, actionable guidance or remove them entirely
| 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 concrete commands, no actual implementation details. The examples describe what the skill 'will do' abstractly rather than showing how to actually perform sentiment analysis. The 'Instructions' section is completely generic placeholder text. | 1 / 3 |
Workflow Clarity | The workflow described is vague ('process the text using a pre-trained sentiment analysis model') with no actual steps, no validation checkpoints, and no concrete guidance on what model to use or how to invoke it. The numbered steps are conceptual descriptions, not actionable procedures. | 1 / 3 |
Progressive Disclosure | The content has section headers providing some structure, but it's a monolithic document with no references to external files. The organization exists but contains too much inline content that could be trimmed rather than split out. | 2 / 3 |
Total | 5 / 12 Passed |
Activation
42%This description suffers from internal contradiction - it describes sentiment analysis capabilities but then provides generic triggers for 'code or data' analysis. The truncated text ('emoti...') suggests incomplete content, and the first-person framing ('enables AI assistant') is awkward. The generic trigger terms would cause frequent false matches with other analysis 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 feelings expressed in text'
Add natural trigger terms users would actually say: 'sentiment', 'feelings', 'tone', 'positive or negative', 'opinion', 'mood of the text'
Fix the truncated description and use consistent third-person voice: 'Analyzes sentiment of text data, identifying emotional tone as positive, negative, or neutral'
| 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 truncated and mixes unrelated triggers about 'code or data' which muddles the specificity. | 2 / 3 |
Completeness | Has a 'what' (sentiment analysis of text) and attempts a 'when' clause, but the 'Use when analyzing code or data' contradicts the sentiment-focused description, creating confusion rather than clarity about when to use this skill. | 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 would match many unrelated skills. Missing natural variations like 'feelings', 'tone', 'mood'. | 2 / 3 |
Distinctiveness Conflict Risk | The generic triggers 'analyze', 'review', 'examine' combined with 'code or data' would conflict with virtually any analysis-related skill. The sentiment-specific content is undermined by these overly broad trigger phrases. | 1 / 3 |
Total | 7 / 12 Passed |
Reviewed
Table of Contents
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