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Matt Van Horn 994a4ab2ca feat(polymarket): add Polymarket prediction markets as 6th research source

Search Polymarket's free Gamma API for relevant prediction markets on any
topic. Uses smart multi-query expansion to cast a wider net (e.g., "Arizona
Basketball" also searches "Arizona"), merges and dedupes by event ID, and
shows price movement context ("up 22.5% this week"). No API key required.

Also hides sources with zero results from the stats output (all sources).

54 new tests, all passing. Full pipeline integration with scoring, dedupe,
cross-source linking, and rendering.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-02-25 22:27:19 -08:00

3.2 KiB

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last30days Skill Specification

Overview

last30days is a Claude Code skill that researches a given topic across Reddit and X (Twitter) using the OpenAI Responses API and xAI Responses API respectively. It enforces a strict 30-day recency window, popularity-aware ranking, and produces actionable outputs including best practices, a prompt pack, and a reusable context snippet.

The skill operates in three modes depending on available API keys: reddit-only (OpenAI key), x-only (xAI key), or both (full cross-validation). It uses automatic model selection to stay current with the latest models from both providers, with optional pinning for stability.

Architecture

The orchestrator (last30days.py) coordinates discovery, enrichment, normalization, scoring, deduplication, and rendering. Each concern is isolated in scripts/lib/:

env.py: Load and validate API keys from ~/.config/last30days/.env
dates.py: Date range calculation and confidence scoring
cache.py: 24-hour TTL caching keyed by topic + date range
http.py: stdlib-only HTTP client with retry logic
models.py: Auto-selection of OpenAI/xAI models with 7-day caching
openai_reddit.py: OpenAI Responses API + web_search for Reddit
xai_x.py: xAI Responses API + x_search for X
reddit_enrich.py: Fetch Reddit thread JSON for real engagement metrics
hackernews.py: Hacker News search via Algolia API (free, no auth)
polymarket.py: Polymarket prediction market search via Gamma API (free, no auth)
normalize.py: Convert raw API responses to canonical schema
score.py: Compute popularity-aware scores (relevance + recency + engagement)
dedupe.py: Near-duplicate detection via text similarity
render.py: Generate markdown and JSON outputs
schema.py: Type definitions and validation

Embedding in Other Skills

Other skills can import the research context in several ways:

Inline Context Injection

## Recent Research Context
!python3 ~/.claude/skills/last30days/scripts/last30days.py "your topic" --emit=context

Read from File

## Research Context
!cat ~/.local/share/last30days/out/last30days.context.md

Get Path for Dynamic Loading

CONTEXT_PATH=$(python3 ~/.claude/skills/last30days/scripts/last30days.py "topic" --emit=path)
cat "$CONTEXT_PATH"

JSON for Programmatic Use

python3 ~/.claude/skills/last30days/scripts/last30days.py "topic" --emit=json > research.json

CLI Reference

python3 ~/.claude/skills/last30days/scripts/last30days.py <topic> [options]

Options:
  --refresh           Bypass cache and fetch fresh data
  --mock              Use fixtures instead of real API calls
  --emit=MODE         Output mode: compact|json|md|context|path (default: compact)
  --sources=MODE      Source selection: auto|reddit|x|both (default: auto)

Output Files

All outputs are written to ~/.local/share/last30days/out/:

report.md - Human-readable full report
report.json - Normalized data with scores
last30days.context.md - Compact reusable snippet for other skills
raw_openai.json - Raw OpenAI API response
raw_xai.json - Raw xAI API response
raw_reddit_threads_enriched.json - Enriched Reddit thread data

3.2 KiB Raw Blame History