using-alpha-lab
How to use the Alpha Lab client in Wayfinder Paths for discovering alpha insights — scored tweets, chain flows, top APYs, and delta-neutral opportunities.
Best use case
using-alpha-lab is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
How to use the Alpha Lab client in Wayfinder Paths for discovering alpha insights — scored tweets, chain flows, top APYs, and delta-neutral opportunities.
Teams using using-alpha-lab should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/using-alpha-lab/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How using-alpha-lab Compares
| Feature / Agent | using-alpha-lab | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
How to use the Alpha Lab client in Wayfinder Paths for discovering alpha insights — scored tweets, chain flows, top APYs, and delta-neutral opportunities.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
## What you need to know (TL;DR) **Alpha Lab = Scored alpha insight feed** ```python from wayfinder_paths.core.clients.AlphaLabClient import ALPHA_LAB_CLIENT # Search all insights (sorted by score) await ALPHA_LAB_CLIENT.search() # Filter by type await ALPHA_LAB_CLIENT.search(scan_type="twitter_post", min_score=0.7) # Text search await ALPHA_LAB_CLIENT.search(search="ETH funding", limit=10) # List available scan types await ALPHA_LAB_CLIENT.get_types() ``` **MCP quick access:** ``` research_search_alpha(limit="20") # Top 20 insights research_search_alpha(scan_type="twitter_post", limit="10") # Top 10 tweets research_search_alpha(query="ETH", limit="10") # Search "ETH" research_search_alpha(created_after="2026-03-06T00:00:00Z", limit="20") # Today's insights research_get_alpha_types() # List scan types ``` Tool args: `research_search_alpha(query, scan_type, created_after, created_before, limit)` - `query`: text search, `_` for none (default `_`) - `scan_type`: filter by type, `all` for none (default `all`) - `created_after` / `created_before`: ISO 8601 datetime bounds, `_` to skip (default `_`) - `limit`: max results (default `"20"`, max `"200"`) **Critical gotchas:** - Client returns data directly (not tuples) — `data = await ALPHA_LAB_CLIENT.search()` - Scores are 0-1 floats (1 = most insightful) - MCP tool sorts by score descending (highest first); use Python client for custom sort/pagination - Max 200 results per call; use `offset` for pagination (Python client only) ## When to use - "What's the latest alpha?" / "Show me today's insights" - "Any interesting tweets about ETH?" - "What chain flows are happening?" - "What are the highest-scored insights?" **Alpha Lab is self-contained for alpha requests.** It already includes `delta_lab_top_apy` and `delta_lab_best_delta_neutral` scan types, so do NOT also query Delta Lab separately. Only use Delta Lab directly when the user asks for raw rates, historical timeseries, or detailed screening — not for "today's alpha". ## How to use - [rules/what-is-alpha-lab.md](rules/what-is-alpha-lab.md) - What Alpha Lab is and its scan types - [rules/high-value-reads.md](rules/high-value-reads.md) - Core queries and MCP URIs - [rules/response-structures.md](rules/response-structures.md) - Response shapes - [rules/gotchas.md](rules/gotchas.md) - Common mistakes
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