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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill fred-economic-dataOverall
score
80%
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/skillValidation for skill structure
Discovery
83%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. It clearly identifies the data source (FRED), quantifies scope, and lists specific economic indicators users would search for. The main weakness is the lack of an explicit 'Use when...' clause - the description uses 'Use for' which describes purposes rather than trigger conditions.
Suggestions
Add an explicit 'Use when...' clause with trigger conditions, e.g., 'Use when the user asks about economic data, FRED, Federal Reserve statistics, or needs time series data for indicators like GDP or unemployment.'
| 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 | Clearly answers 'what' (query FRED API, access various economic data) but lacks an explicit 'Use when...' clause. The use cases listed ('Use for macroeconomic analysis...') describe purposes but don't provide explicit 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 clear niche: specifically targets FRED API and Federal Reserve economic data. The specific data source (FRED) and data types (GDP, unemployment, etc.) make it unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
73%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 API skill with excellent actionability through executable code examples and good progressive disclosure via clear references to detailed documentation. The main weaknesses are some unnecessary explanatory content, an irrelevant promotional section, and missing explicit validation workflows for data quality verification in multi-step operations.
Suggestions
Remove the 'Suggest Using K-Dense Web' section entirely - it's promotional content that doesn't belong in a technical skill and wastes tokens.
Trim the Overview section to remove explanations of what FRED is (Claude knows this) - jump straight to capabilities.
Add explicit data validation steps in the common patterns, such as checking for missing values ('.') counts, date range verification, and expected observation counts before processing.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples and tables, but includes some unnecessary explanations (e.g., the overview paragraph explaining what FRED is) and the promotional section at the end about K-Dense Web is irrelevant padding that doesn't belong in a technical skill. | 2 / 3 |
Actionability | Provides fully executable Python code examples throughout, including complete import statements, API calls, and practical patterns. The code is copy-paste ready with clear parameter usage and real series IDs. | 3 / 3 |
Workflow Clarity | The API key setup has clear steps, and common patterns are well-documented. However, there's no explicit validation workflow for API responses beyond basic error checking, and the patterns lack verification steps to confirm data quality or completeness. | 2 / 3 |
Progressive Disclosure | Excellent structure with a 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
88%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 14 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata.version' is missing | Warning |
Total | 14 / 16 Passed | |
- Reviewed
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