churn-analysis-helper
Churn Analysis Helper - Auto-activating skill for Data Analytics. Triggers on: churn analysis helper, churn analysis helper Part of the Data Analytics skill category.
33
0%
Does it follow best practices?
Impact
97%
1.03xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/12-data-analytics/churn-analysis-helper/SKILL.mdQuality
Discovery
0%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 is an extremely weak description that essentially only provides a skill name and category label without any substantive content. It fails on all dimensions: no concrete actions are listed, no natural trigger terms are included beyond the skill's own name (duplicated), there is no 'when to use' guidance, and it is not distinctive enough to be reliably selected from a pool of skills.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Analyzes customer churn rates, identifies at-risk segments, builds retention cohort analyses, and generates churn prediction models from subscription or usage data.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user mentions churn, customer attrition, retention analysis, subscriber loss, cancellation rates, or cohort analysis.'
Remove the duplicated trigger term and expand with varied natural keywords users would actually say, such as 'customer turnover', 'why customers leave', 'retention metrics', 'churn rate', and 'attrition'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description provides no concrete actions whatsoever. It only names itself ('Churn Analysis Helper') and its category ('Data Analytics') without describing what it actually does—no mention of specific analyses, metrics, outputs, or data operations. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no explicit 'Use when...' clause or equivalent trigger guidance. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are the skill's own name repeated twice ('churn analysis helper'). There are no natural user keywords like 'customer retention', 'attrition rate', 'subscriber loss', 'churn prediction', or 'cohort analysis' that a user would naturally say. | 1 / 3 |
Distinctiveness Conflict Risk | The description is extremely generic—'Data Analytics' could overlap with countless other skills. The only distinguishing element is the word 'churn' but without any specifics about what churn analysis entails, it provides minimal differentiation. | 1 / 3 |
Total | 4 / 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 is an empty placeholder that provides no actual guidance for churn analysis. It consists entirely of meta-descriptions and trigger phrases, with no executable code, concrete steps, analytical frameworks, or domain-specific knowledge. It would be completely unhelpful to Claude in performing any churn analysis task.
Suggestions
Add concrete SQL examples for calculating churn metrics (e.g., churn rate query, cohort retention query, customer lifetime value calculation).
Define a clear workflow: identify churn definition → extract relevant data → calculate metrics → visualize results → interpret findings, with specific code at each step.
Include at least one complete, executable example (e.g., a Python/SQL pipeline that computes monthly churn rate from a sample schema with columns like user_id, signup_date, last_active_date).
Remove all meta-description sections ('When to Use', 'Example Triggers', 'Capabilities') and replace with actual analytical content—churn definitions, common patterns, and visualization recommendations.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual actionable content. Every section restates the same vague idea—'churn analysis helper'—without adding substance. | 1 / 3 |
Actionability | There is zero concrete guidance—no SQL queries, no code examples, no specific analytical steps, no definitions of churn metrics, no data schemas. The skill describes rather than instructs, offering only vague promises like 'provides step-by-step guidance' without actually providing any. | 1 / 3 |
Workflow Clarity | No workflow, steps, or sequence of any kind is present. The skill claims to provide 'step-by-step guidance' but contains none. There are no validation checkpoints or any process description. | 1 / 3 |
Progressive Disclosure | The content is a flat, repetitive block with no meaningful structure. Sections exist but contain no real content differentiation. There are no references to detailed files, examples, or supplementary materials. | 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 | |
- Commit
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- Reviewed
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