analytics-reporter
Specialist data analyst that calculates trends, generates summary statistics, builds charts, tracks KPIs, performs RFM segmentation, and produces structured business intelligence reports from CSV, Excel, SQL query results, or raw datasets. Use when the user asks for data analysis, dashboard creation, KPI tracking, statistical summaries, customer segmentation, marketing attribution, churn analysis, or data-driven business recommendations.
90
88%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
92%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 a strong, well-crafted description that clearly articulates specific capabilities and provides explicit trigger guidance via a 'Use when...' clause with numerous natural keywords. Its main weakness is the breadth of scope, which could cause overlap with other analytics or visualization skills in a large skill library. The description uses proper third-person voice and avoids vague language.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'calculates trends, generates summary statistics, builds charts, tracks KPIs, performs RFM segmentation, and produces structured business intelligence reports.' Also specifies input types (CSV, Excel, SQL query results, raw datasets). | 3 / 3 |
Completeness | Clearly answers both 'what' (calculates trends, generates summary statistics, builds charts, tracks KPIs, performs RFM segmentation, produces BI reports) and 'when' with an explicit 'Use when...' clause listing multiple trigger scenarios. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'data analysis', 'dashboard creation', 'KPI tracking', 'statistical summaries', 'customer segmentation', 'marketing attribution', 'churn analysis', 'data-driven business recommendations', plus file types like CSV, Excel, SQL. These are terms users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | While it has a clear data analysis niche, the breadth of the description is quite wide—terms like 'data analysis', 'business recommendations', and 'dashboard creation' could overlap with general analytics skills, BI tools, or visualization-specific skills. The RFM segmentation and marketing attribution terms add some distinctiveness, but the scope is broad enough to risk conflicts. | 2 / 3 |
Total | 11 / 12 Passed |
Implementation
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with strong workflow clarity, good progressive disclosure via reference files, and highly actionable content including executable code and a concrete report template. The main weakness is minor verbosity — Step 3 largely duplicates the reference file information from Step 2, and some descriptive prose could be trimmed. Overall, it's a solid skill that effectively guides Claude through a complex multi-step analytical process.
Suggestions
Merge Step 3's reference file descriptions into the Step 2 decision table to eliminate redundancy and save tokens.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Generally efficient but has some redundancy — Step 3 essentially restates the reference file table from Step 2. The validation function is useful but the surrounding prose could be tighter. Some phrases like 'fully executable, copy-paste-ready code with clear input/output contracts' are filler. | 2 / 3 |
Actionability | Provides executable Python code for data validation, a concrete decision table mapping user requests to analysis types, specific reference files to load, SQL/Python technique callouts (NULLIF), and a complete report template with example formatting. Guidance is specific and directly usable. | 3 / 3 |
Workflow Clarity | Clear 4-step sequence with explicit validation gates in Step 1 that specify when to stop and notify the user (missing columns, >20% nulls, n<30, date inconsistencies). The workflow has a logical progression from validation → selection → execution → reporting, with quality checks carried through to the final report template. | 3 / 3 |
Progressive Disclosure | Well-structured overview with a decision table that routes to specific reference files (analytics/kpi-dashboard.sql, analytics/rfm-segmentation.py, etc.) — all one level deep. Inline content is reserved for the validation step and general summary case, while specialized analyses are appropriately delegated to separate files. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- OpenRoster-ai/awesome-agents
- Commit
010799b
- Reviewed
Table of Contents
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