diff --git a/SPEC.md b/SPEC.md
deleted file mode 100644
index 8ee804b..0000000
--- a/SPEC.md
+++ /dev/null
@@ -1,77 +0,0 @@
-# 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
-```markdown
-## Recent Research Context
-!python3 ~/.claude/skills/last30days/scripts/last30days.py "your topic" --emit=context
-```
-
-### Read from File
-```markdown
-## Research Context
-!cat ~/.local/share/last30days/out/last30days.context.md
-```
-
-### Get Path for Dynamic Loading
-```bash
-CONTEXT_PATH=$(python3 ~/.claude/skills/last30days/scripts/last30days.py "topic" --emit=path)
-cat "$CONTEXT_PATH"
-```
-
-### JSON for Programmatic Use
-```bash
-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
diff --git a/TASKS.md b/TASKS.md
deleted file mode 100644
index 8f9272d..0000000
--- a/TASKS.md
+++ /dev/null
@@ -1,47 +0,0 @@
-# last30days Implementation Tasks
-
-## Setup & Configuration
-- [x] Create directory structure
-- [x] Write SPEC.md
-- [x] Write TASKS.md
-- [x] Write SKILL.md with proper frontmatter
-
-## Core Library Modules
-- [x] scripts/lib/env.py - Environment and API key loading
-- [x] scripts/lib/dates.py - Date range and confidence utilities
-- [x] scripts/lib/cache.py - TTL-based caching
-- [x] scripts/lib/http.py - HTTP client with retry
-- [x] scripts/lib/models.py - Auto model selection
-- [x] scripts/lib/schema.py - Data structures
-- [x] scripts/lib/openai_reddit.py - OpenAI Responses API
-- [x] scripts/lib/xai_x.py - xAI Responses API
-- [x] scripts/lib/reddit_enrich.py - Reddit thread JSON fetcher
-- [x] scripts/lib/normalize.py - Schema normalization
-- [x] scripts/lib/score.py - Popularity scoring
-- [x] scripts/lib/dedupe.py - Near-duplicate detection
-- [x] scripts/lib/render.py - Output rendering
-
-## Main Script
-- [x] scripts/last30days.py - CLI orchestrator
-
-## Fixtures
-- [x] fixtures/openai_sample.json
-- [x] fixtures/xai_sample.json
-- [x] fixtures/reddit_thread_sample.json
-- [x] fixtures/models_openai_sample.json
-- [x] fixtures/models_xai_sample.json
-
-## Tests
-- [x] tests/test_dates.py
-- [x] tests/test_cache.py
-- [x] tests/test_models.py
-- [x] tests/test_score.py
-- [x] tests/test_dedupe.py
-- [x] tests/test_normalize.py
-- [x] tests/test_render.py
-
-## Validation
-- [x] Run tests in mock mode
-- [x] Demo --emit=compact
-- [x] Demo --emit=context
-- [x] Verify file tree