Skip to content

SPEC.md

Latest commit

 

History

History
77 lines (59 loc) · 3.32 KB

SPEC.md

File metadata and controls

77 lines (59 loc) · 3.32 KB

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. OpenAI auth can come from OPENAI_API_KEY or Codex login credentials.

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 API keys from ~/.config/last30days/.env and Codex auth from ~/.codex/auth.json
  • 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