Matt Van Horn
5ca4829be4
Research topics across Reddit + X from the last 30 days using OpenAI and xAI APIs. Features: - Auto model selection (GPT-5.x, Grok-3) - Popularity-aware scoring (relevance + recency + engagement) - Reddit thread enrichment with real metrics - Near-duplicate detection - Multiple emit modes (compact, json, context, path) - 24h caching with --refresh bypass - NUX for API key setup - 87 passing unit tests Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
278 lines
8.7 KiB
Python
278 lines
8.7 KiB
Python
"""Output rendering for last30days skill."""
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import json
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from pathlib import Path
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from typing import List, Optional
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from . import schema
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OUTPUT_DIR = Path.home() / ".local" / "share" / "last30days" / "out"
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def ensure_output_dir():
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"""Ensure output directory exists."""
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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def render_compact(report: schema.Report, limit: int = 15) -> str:
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"""Render compact output for Claude to synthesize.
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Args:
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report: Report data
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limit: Max items per source
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Returns:
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Compact markdown string
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"""
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lines = []
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# Header
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lines.append(f"## Research Results: {report.topic}")
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lines.append("")
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lines.append(f"**Date Range:** {report.range_from} to {report.range_to}")
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lines.append(f"**Mode:** {report.mode}")
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if report.openai_model_used:
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lines.append(f"**OpenAI Model:** {report.openai_model_used}")
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if report.xai_model_used:
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lines.append(f"**xAI Model:** {report.xai_model_used}")
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lines.append("")
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# Coverage note
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if report.mode == "reddit-only":
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lines.append("*Tip: Add xAI key for X coverage and better triangulation.*")
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lines.append("")
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elif report.mode == "x-only":
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lines.append("*Tip: Add OpenAI key for Reddit coverage and better triangulation.*")
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lines.append("")
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# Reddit items
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if report.reddit:
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lines.append("### Reddit Threads")
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lines.append("")
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for item in report.reddit[:limit]:
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eng_str = ""
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if item.engagement:
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eng = item.engagement
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parts = []
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if eng.score is not None:
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parts.append(f"{eng.score}pts")
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if eng.num_comments is not None:
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parts.append(f"{eng.num_comments}cmt")
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if parts:
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eng_str = f" [{', '.join(parts)}]"
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date_str = f" ({item.date})" if item.date else " (date unknown)"
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conf_str = f" [date:{item.date_confidence}]" if item.date_confidence != "high" else ""
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lines.append(f"**{item.id}** (score:{item.score}) r/{item.subreddit}{date_str}{conf_str}{eng_str}")
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lines.append(f" {item.title}")
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lines.append(f" {item.url}")
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lines.append(f" *{item.why_relevant}*")
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# Top comment insights
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if item.comment_insights:
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lines.append(f" Insights:")
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for insight in item.comment_insights[:3]:
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lines.append(f" - {insight}")
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lines.append("")
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# X items
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if report.x:
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lines.append("### X Posts")
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lines.append("")
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for item in report.x[:limit]:
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eng_str = ""
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if item.engagement:
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eng = item.engagement
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parts = []
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if eng.likes is not None:
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parts.append(f"{eng.likes}likes")
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if eng.reposts is not None:
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parts.append(f"{eng.reposts}rt")
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if parts:
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eng_str = f" [{', '.join(parts)}]"
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date_str = f" ({item.date})" if item.date else " (date unknown)"
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conf_str = f" [date:{item.date_confidence}]" if item.date_confidence != "high" else ""
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lines.append(f"**{item.id}** (score:{item.score}) @{item.author_handle}{date_str}{conf_str}{eng_str}")
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lines.append(f" {item.text[:200]}...")
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lines.append(f" {item.url}")
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lines.append(f" *{item.why_relevant}*")
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lines.append("")
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return "\n".join(lines)
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def render_context_snippet(report: schema.Report) -> str:
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"""Render reusable context snippet.
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Args:
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report: Report data
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Returns:
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Context markdown string
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"""
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lines = []
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lines.append(f"# Context: {report.topic} (Last 30 Days)")
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lines.append("")
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lines.append(f"*Generated: {report.generated_at[:10]} | Sources: {report.mode}*")
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lines.append("")
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# Key sources summary
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lines.append("## Key Sources")
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lines.append("")
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all_items = []
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for item in report.reddit[:5]:
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all_items.append((item.score, "Reddit", item.title, item.url))
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for item in report.x[:5]:
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all_items.append((item.score, "X", item.text[:50] + "...", item.url))
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all_items.sort(key=lambda x: -x[0])
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for score, source, text, url in all_items[:7]:
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lines.append(f"- [{source}] {text}")
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lines.append("")
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lines.append("## Summary")
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lines.append("")
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lines.append("*See full report for best practices, prompt pack, and detailed sources.*")
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lines.append("")
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return "\n".join(lines)
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def render_full_report(report: schema.Report) -> str:
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"""Render full markdown report.
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Args:
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report: Report data
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Returns:
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Full report markdown
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"""
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lines = []
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# Title
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lines.append(f"# {report.topic} - Last 30 Days Research Report")
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lines.append("")
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lines.append(f"**Generated:** {report.generated_at}")
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lines.append(f"**Date Range:** {report.range_from} to {report.range_to}")
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lines.append(f"**Mode:** {report.mode}")
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lines.append("")
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# Models
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lines.append("## Models Used")
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lines.append("")
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if report.openai_model_used:
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lines.append(f"- **OpenAI:** {report.openai_model_used}")
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if report.xai_model_used:
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lines.append(f"- **xAI:** {report.xai_model_used}")
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lines.append("")
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# Reddit section
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if report.reddit:
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lines.append("## Reddit Threads")
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lines.append("")
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for item in report.reddit:
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lines.append(f"### {item.id}: {item.title}")
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lines.append("")
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lines.append(f"- **Subreddit:** r/{item.subreddit}")
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lines.append(f"- **URL:** {item.url}")
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lines.append(f"- **Date:** {item.date or 'Unknown'} (confidence: {item.date_confidence})")
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lines.append(f"- **Score:** {item.score}/100")
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lines.append(f"- **Relevance:** {item.why_relevant}")
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if item.engagement:
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eng = item.engagement
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lines.append(f"- **Engagement:** {eng.score or '?'} points, {eng.num_comments or '?'} comments")
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if item.comment_insights:
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lines.append("")
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lines.append("**Key Insights from Comments:**")
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for insight in item.comment_insights:
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lines.append(f"- {insight}")
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lines.append("")
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# X section
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if report.x:
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lines.append("## X Posts")
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lines.append("")
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for item in report.x:
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lines.append(f"### {item.id}: @{item.author_handle}")
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lines.append("")
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lines.append(f"- **URL:** {item.url}")
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lines.append(f"- **Date:** {item.date or 'Unknown'} (confidence: {item.date_confidence})")
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lines.append(f"- **Score:** {item.score}/100")
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lines.append(f"- **Relevance:** {item.why_relevant}")
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if item.engagement:
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eng = item.engagement
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lines.append(f"- **Engagement:** {eng.likes or '?'} likes, {eng.reposts or '?'} reposts")
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lines.append("")
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lines.append(f"> {item.text}")
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lines.append("")
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# Placeholders for Claude synthesis
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lines.append("## Best Practices")
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lines.append("")
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lines.append("*To be synthesized by Claude*")
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lines.append("")
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lines.append("## Prompt Pack")
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lines.append("")
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lines.append("*To be synthesized by Claude*")
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lines.append("")
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return "\n".join(lines)
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def write_outputs(
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report: schema.Report,
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raw_openai: Optional[dict] = None,
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raw_xai: Optional[dict] = None,
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raw_reddit_enriched: Optional[list] = None,
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):
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"""Write all output files.
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Args:
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report: Report data
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raw_openai: Raw OpenAI API response
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raw_xai: Raw xAI API response
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raw_reddit_enriched: Raw enriched Reddit thread data
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"""
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ensure_output_dir()
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# report.json
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with open(OUTPUT_DIR / "report.json", 'w') as f:
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json.dump(report.to_dict(), f, indent=2)
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# report.md
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with open(OUTPUT_DIR / "report.md", 'w') as f:
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f.write(render_full_report(report))
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# last30days.context.md
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with open(OUTPUT_DIR / "last30days.context.md", 'w') as f:
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f.write(render_context_snippet(report))
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# Raw responses
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if raw_openai:
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with open(OUTPUT_DIR / "raw_openai.json", 'w') as f:
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json.dump(raw_openai, f, indent=2)
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if raw_xai:
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with open(OUTPUT_DIR / "raw_xai.json", 'w') as f:
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json.dump(raw_xai, f, indent=2)
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if raw_reddit_enriched:
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with open(OUTPUT_DIR / "raw_reddit_threads_enriched.json", 'w') as f:
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json.dump(raw_reddit_enriched, f, indent=2)
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def get_context_path() -> str:
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"""Get path to context file."""
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return str(OUTPUT_DIR / "last30days.context.md")
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