Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
FRED-Navigator
Navigate FRED categories and series using fredapi, supporting natural-language queries with intent recognition and double validation.
skill openclawclawhub Free
Navigate FRED categories and series using fredapi, supporting natural-language queries with intent recognition and double validation.
โฌ 0 downloads โ
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- requirements.txt, README.md, SKILL.md, scripts/build_paths.py, scripts/fred_query.py, references/category_paths.json
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- kiszly
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Purpose
Provide a reliable workflow to navigate FRED categories and series, with support for: Direct category_id Direct series_id Natural-language query โ intent recognition โ 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", ...], ... }
1. Category Exploration
Load fred_categories_tree.json for hierarchical browsing. If user provides category_id, validate it exists. If user provides category_name, fuzzy match against flat names and return candidates.
2. Series Discovery
Use search_by_category(category_id) to list available series. Prefer scripts/fred_query.py category <id> for consistent output. Return key columns: id, title, frequency, units, seasonal_adjustment, last_updated.
3. Series Retrieval
Use get_series(series_id) for time series. Use get_series_info(series_id) for metadata. Prefer scripts/fred_query.py series <id> and scripts/fred_query.py series-info <id>. Provide: data head/tail missing counts latest value and date
4. Natural Language Query
4.1 Intent Identification (Top-K) Use the IDE agent (Codex) to interpret the natural-language intent. Select the single best-matching category. If confidence is low, ask the user to confirm the category before proceeding. 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 โ 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 โ list of related terms). Regenerate precomputed paths (optional): run scripts/build_paths.py. Add helper scripts (optional): place in scripts/ and document usage here.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Scripts1 Config1 Files
- SKILL.md
- README.md
- scripts/build_paths.py
- scripts/fred_query.py
- references/category_paths.json
- requirements.txt