---

name: fred-navigator description: Navigate FRED categories and series using fredapi, supporting natural-language queries with intent recognition and double validation. metadata: short-description: FRED navigation and lookup

FRED Navigator

Purpose

Provide a reliable workflow to navigate FRED categories and series, with support for:

1. Direct category_id
2. Direct series_id
3. Natural-language query → intent recognition → double validation

Inputs

• category_id: FRED category id
• series_id: FRED series id
• query: natural language request
• limit: number of candidates to return (default 5)
• api_key: read from environment FRED_API_KEY only

Required Resources

• references/fred_categories_tree.json
• references/fred_categories_flat.json
• Optional: references/category_paths.json (precomputed)
• Optional: references/synonyms.json
• Helper script: scripts/fred_query.py
• Path builder: scripts/build_paths.py

Optional Resource Structure Notes

• references/category_paths.json format:
  • { "category_id": { "id": <int>, "name": "<str>", "path": "<str>" }, ... }
• references/synonyms.json format:
  • { "concept": ["alias1", "alias2", ...], ... }

Workflow

1. Category Exploration

1. Load fred_categories_tree.json for hierarchical browsing.
2. If user provides category_id, validate it exists.
3. If user provides category_name, fuzzy match against flat names and return candidates.

2. Series Discovery

1. Use search_by_category(category_id) to list available series.
2. Prefer scripts/fred_query.py category <id> for consistent output.
3. Return key columns:
   • id, title, frequency, units, seasonal_adjustment, last_updated.

3. Series Retrieval

1. Use get_series(series_id) for time series.
2. Use get_series_info(series_id) for metadata.
3. Prefer scripts/fred_query.py series <id> and scripts/fred_query.py series-info <id>.
4. Provide:
   • data head/tail
   • missing counts
   • latest value and date

4. Natural Language Query

4.1 Intent Identification (Top-K)

1. Use the IDE agent (Codex) to interpret the natural-language intent.
2. Select the single best-matching category.
3. If confidence is low, ask the user to confirm the category before proceeding.
4. Use references/category_paths.json and references/synonyms.json as supporting context if available.

4.2 Double Validation

Structural validation

• Candidate must exist in fred_categories_tree.json.
• Pass if at least one:
  • children non-empty
  • search_by_category(id) returns >= 1 series
  • Prefer scripts/fred_query.py check-category <id> for a quick check

Semantic validation (agent)

• Compare query with candidate name/path.
• Return pass/fail or numeric relevance score.

4.3 Decision

• If structural + semantic validation both pass → accept category.
• Otherwise:
  • return Top-5 candidates
  • ask user to choose one explicitly

Failure Handling

• Always provide Top-5 candidates when uncertain.
• Never proceed to series retrieval if category validation fails.

Notes

• Do not hardcode API keys.
• Keep heavy reference data in references/, not in this file.
• When running Python functions for querying, execute them inside the sandbox environment.

Maintenance

• Update workflow or constraints: edit SKILL.md.
• Update category data: replace files in references/.
• Improve natural-language matching: add or edit references/synonyms.json (key → list of related terms).
• Regenerate precomputed paths (optional): run scripts/build_paths.py.
• Add helper scripts (optional): place in scripts/ and document usage here.