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.

Quality

92%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

The canonical home for this skill is fred-economic-data in foryourhealth111-pixel/Vibe-Skills

Quality

Content

85%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable and well-structured with clean progressive disclosure into verified reference files, and the workflow is unambiguous for an API-access skill. Its main weakness is conciseness — the endpoint-category sections and example patterns duplicate content already covered by the references.

Suggestions

Trim the per-endpoint 'API Endpoint Categories' subsections to a one-line pointer plus the most-used example, since full endpoint lists already live in references/*.md.

Consider moving the four 'Common Patterns' (economic snapshot, time series comparison, release calendar, regional analysis) into a separate examples file or scripts/fred_examples.md, keeping SKILL.md as an overview.

Remove the duplicated 'Reference Documentation' list near the end since each endpoint section already states 'See references/<x>.md'.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient with executable code and tables, but the per-endpoint category sections and the four 'Common Patterns' restate information already delegated to references, so it could be tightened. It does not lecture concepts Claude already knows (so not a 1), yet it is not lean enough for a 3.	2 / 3
Actionability	Provides fully executable, copy-paste-ready code in both the FREDQuery class and direct-requests styles, plus concrete tables of series IDs, transformation values, and frequency codes.	3 / 3
Workflow Clarity	For a single-purpose API-querying skill the actions are unambiguous and well sequenced, with an explicit error-handling section and retry/backoff noted; no destructive multi-step workflow is missing a checkpoint.	3 / 3
Progressive Disclosure	SKILL.md is a clear overview with well-signaled, one-level-deep references (series.md, categories.md, releases.md, tags.md, sources.md, geofred.md, api_basics.md), all of which exist as real files, and detail is appropriately split out of the main file.	3 / 3
	Total	11 / 12 Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, well-triggered, and complete, naming concrete capabilities and explicit use contexts in third person. It clearly distinguishes the FRED-data niche and is unlikely to conflict with other skills.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('Query FRED... API', 'Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data') rather than vague language, matching the comprehensive-action anchor.	3 / 3
Completeness	Explicitly answers both what ('Query FRED... API for... time series') and when ('Use for macroeconomic analysis, financial research...'), satisfying the explicit-trigger requirement for the top score.	3 / 3
Trigger Term Quality	Includes natural terms users actually say — macroeconomic indicators like 'GDP, unemployment, inflation, interest rates' and use-cases like 'macroeconomic analysis, financial research, policy studies, economic forecasting' — with good coverage.	3 / 3
Distinctiveness Conflict Risk	The FRED/Federal Reserve niche is distinct and specific; its triggers (economic time series, macroeconomic indicators) are unlikely to fire for unrelated skills, and the voice is third person.	3 / 3
	Total	12 / 12 Passed

Validation

87%

Warnings & errors only

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
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/databases/fred-economic-data/SKILL.md
Commit: 52845c3

Reviewed: about 10 hours ago

Table of Contents

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