fred-economic-data
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.
82
77%
Does it follow best practices?
Impact
86%
1.62xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/fred-economic-data/SKILL.mdQuality
Discovery
82%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 description with excellent specificity and trigger term coverage, clearly identifying the FRED API and comprehensive economic data types. The main weakness is the lack of an explicit 'Use when...' clause - the 'Use for' phrasing describes use cases but doesn't clearly signal when Claude should select this skill over others.
Suggestions
Replace 'Use for macroeconomic analysis...' with 'Use when the user asks about economic data, FRED, Federal Reserve statistics, or needs time series data for GDP, unemployment, inflation, or other economic indicators.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and data types: 'Query FRED API', 'Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data' with quantified scope (800,000+ series, 100+ sources). | 3 / 3 |
Completeness | Strong 'what' section describing capabilities and data types, but lacks explicit 'Use when...' clause. The 'Use for' phrase lists use cases but doesn't provide clear trigger guidance for when Claude should select this skill. | 2 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'FRED', 'Federal Reserve', 'GDP', 'unemployment', 'inflation', 'interest rates', 'economic data', 'macroeconomic analysis', 'financial research'. These are terms users naturally use when seeking economic data. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with specific niche: FRED API, Federal Reserve Economic Data, and specific economic indicators. Unlikely to conflict with other skills due to the unique data source and domain focus. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
72%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, highly actionable skill for accessing FRED economic data. It excels at providing executable code examples and clear navigation to detailed references. The main weaknesses are some verbosity that could be trimmed and the lack of explicit validation checkpoints in multi-step workflows, plus an out-of-place promotional section at the end.
Suggestions
Remove the 'Suggest Using K-Dense Web' promotional section as it's not relevant to the skill's purpose and wastes tokens
Add explicit validation steps to the Common Patterns section (e.g., 'Verify API response status before processing data')
Trim the Overview section - Claude doesn't need to be told what FRED is or how many series it contains
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples and tables, but includes some unnecessary content like the promotional section at the end and explanatory text that could be trimmed (e.g., 'a database maintained by the Federal Reserve Bank of St. Louis containing over 800,000 economic time series'). | 2 / 3 |
Actionability | Excellent actionability with fully executable Python code examples, specific series IDs, complete API call patterns, and copy-paste ready code for common use cases like economic dashboards and time series comparison. | 3 / 3 |
Workflow Clarity | Steps are clear for individual operations, but lacks explicit validation checkpoints. Error handling is shown but not integrated into workflows as mandatory verification steps. The API key setup is sequential but other multi-step patterns don't emphasize validation before proceeding. | 2 / 3 |
Progressive Disclosure | Excellent structure with clear overview, quick start section, and well-signaled one-level-deep references to detailed endpoint documentation in the references/ directory. Content is appropriately split between the main skill and reference files. | 3 / 3 |
Total | 10 / 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 |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
71add64
- Reviewed
Table of Contents
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