last30days

Files (51)

• marketplace.json · .claude-plugin/
• plugin.json · .claude-plugin/
• SKILL-original.md
• SKILL.md
• SPEC.md
• TASKS.md
• 2026-02-03-bird-cli-implementation.md · docs/plans/
• 2026-02-03-bird-cli-integration-design.md · docs/plans/
• 2026-02-06-feat-last30days-bird-cli-release-plan.md · docs/plans/
• 2026-02-06-feat-visible-query-parsing-display-plan.md · docs/plans/
• 2026-02-06-fix-last30days-v2-formatting-reddit-citations-plan.md · docs/plans/
• 2026-02-06-fix-skill-execution-fork-mode-plan.md · docs/plans/
• 2026-02-06-test-v1-vs-v2-comparison-plan.md · docs/plans/
• 2026-02-07-feat-smart-supplemental-search-plan.md · docs/plans/
• 2026-02-07-fix-x-search-query-construction-plan.md · docs/plans/
• pr-credits.md · docs/
• models_openai_sample.json · fixtures/
• models_xai_sample.json · fixtures/
• openai_sample.json · fixtures/
• reddit_thread_sample.json · fixtures/
• xai_sample.json · fixtures/
• feat-add-websearch-source.md · plans/
• fix-strict-date-filtering.md · plans/
• last30days.py · scripts/
• __init__.py · scripts/lib/
• bird_x.py · scripts/lib/
• cache.py · scripts/lib/
• dates.py · scripts/lib/
• dedupe.py · scripts/lib/
• entity_extract.py · scripts/lib/
• env.py · scripts/lib/
• http.py · scripts/lib/
• models.py · scripts/lib/
• normalize.py · scripts/lib/
• openai_reddit.py · scripts/lib/
• reddit_enrich.py · scripts/lib/
• render.py · scripts/lib/
• schema.py · scripts/lib/
• score.py · scripts/lib/
• ui.py · scripts/lib/
• websearch.py · scripts/lib/
• xai_x.py · scripts/lib/
• __init__.py · tests/
• test_cache.py · tests/
• test_dates.py · tests/
• test_dedupe.py · tests/
• test_models.py · tests/
• test_normalize.py · tests/
• test_openai_reddit.py · tests/
• test_render.py · tests/
• test_score.py · tests/

SKILL.md (first lines)

# last30days: Research Any Topic from the Last 30 Days

Research ANY topic across Reddit, X, and the web. Surface what people are actually discussing, recommending, and debating right now.

## CRITICAL: Parse User Intent

Before doing anything, parse the user's input for:

1. **TOPIC**: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
2. **TARGET TOOL** (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
3. **QUERY TYPE**: What kind of research they want:
   - **PROMPTING** - "X prompts", "prompting for X", "X best practices" → User wants to learn techniques and get copy-paste prompts
   - **RECOMMENDATIONS** - "best X", "top X", "what X should I use", "recommended X" → User wants a LIST of specific things
   - **NEWS** - "what's happening with X", "X news", "latest on X" → User wants current events/updates
   - **GENERAL** - anything else → User wants broad understanding of the topic

Common patterns:
- `[topic] for [tool]` → "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED
- `[topic] prompts for [tool]` → "UI design prompts for Midjourney" → TOOL IS SPECIFIED
- Just `[topic]` → "iOS design mockups" → TOOL NOT SPECIFIED, that's OK
- "best [topic]" or "top [topic]" → QUERY_TYPE = RECOMMENDATIONS
- "what are the best [topic]" → QUERY_TYPE = RECOMMENDATIONS

**IMPORTANT: Do NOT ask about target tool before research.**
- If tool is specified in the query, use it
- If tool is NOT specified, run research first, then ask AFTER showing results

**Store these variables:**
- `TOPIC = [extracted topic]`
- `TARGET_TOOL = [extracted tool, or "unknown" if not specified]`
- `QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | GENERAL]`

**DISPLAY your parsing to the user.** Before running any tools, output:

```
I'll research {TOPIC} across Reddit, X, and the web to find what's been discussed in the last 30 days.

Parsed intent:
- TOPIC = {TOPIC}
- TARGET_TOOL = {TARGET_TOOL or "unknown"}
- QUERY_TYPE = {QUERY_TYPE}

Research typically takes 2-8 minutes (niche topics take longer). Starting now.
```

If TARGET_TOOL is known, mention it in the intro: "...to find {QUERY_TYPE}-style content for use in {TARGET_TOOL}."

This text MUST appear before you call any tools. It confirms to the user that you understood their request.

---

## Research Execution

**Step 1: Run the research script**
```bash
python3 "${CLAUDE_PLUGIN_ROOT:-$HOME/.claude/skills/last30days}/scripts/last30days.py" "$ARGUMENTS" --emit=compact 2>&1
```

The script will automatically:
- Detect available API keys
- Run Reddit/X searches if keys exist
- Signal if WebSearch is needed

---

## STEP 2: DO WEBSEARCH WHILE SCRIPT RUNS

The script auto-detects sources (Bird CLI, API keys, etc). While waiting for it, do WebSearch.

For **ALL modes**, do WebSearch to supplement (or provide all data in web-only mode).

Choose search queries based on QUERY_TYPE:

**If RECOMMENDATIONS** ("best X", "top X", "what X should I use"):
- Search for: `best {TOPIC} recommendations`
- Search for: `{TOPIC} list examples`
- Search for: `most popular {TOPIC}`
- Goal: Find SPECIFIC NAMES of things, not generic advice

**If NEWS** ("what's happening with X", "X news"):
- Search for: `{TOPIC} news 2026`
- Search for: `{TOPIC} announcement update`
- Goal: Find current events and recent developments

**If PROMPTING** ("X prompts", "prompting for X"):
- Search for: `{TOPIC} prompts examples 2026`
- Search for: `{TOPIC} techniques tips`
- Goal: Find prompting techniques and examples to create copy-paste prompts

**If GENERAL** (default):
- Search for: `{TOPIC} 2026`
- Search for: `{TOPIC} discussion`
- Goal: Find what people are actually saying

For ALL query types:
- **USE THE USER'S EXACT TERMINOLOGY** - don't substitute or add tech names based on your knowledge
- EXCLUDE reddit.com, x.com, twitter.com (covered by script)
- INCLUDE: blogs, tutorials, docs, news, GitHub repos
- **DO NOT output "Sources:" list** - this is noise, we'll show stats at the end

**Options** (passed through from user's command):
- `--days=N` → Look back N days instead of 30 (e.g., `--days=7` for weekly roundup)
- `--quick` → Faster, fewer sources (8-12 each)
- (default) → Balanced (20-30 each)
- `--deep` → Comprehensive (50-70 Reddit, 40-60 X)

---

## Judge Agent: Synthesize All Sources

**After all searches complete, internally synthesize (don't display stats yet):**

The Judge Agent must:
1. Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)
2. Weight WebSearch sources LOWER (no engagement data)
3. Identify patterns that appear across ALL three sources (strongest signals)
4. Note any contradictions between sources
5. Extract the top 3-5 actionable insights

From AlekseiUL/heisenberg-team-GPT, MIT.