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last30days-skill/variants/open/references/research.md
T
Matt Van Horn 9ff00ce38b feat(open): create open variant SKILL.md and reference files
Router SKILL.md dispatches to mode-specific references:
- research.md: one-shot research with --store for persistence
- watchlist.md: add/remove/list/run topics
- briefing.md: daily/weekly briefing generation
- history.md: query/search accumulated findings
- context.md: agent memory template

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-14 23:46:35 -08:00

3.7 KiB

One-Shot Research Mode

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

Parse User Intent

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

  1. TOPIC: What they want to learn about
  2. TARGET TOOL (if specified): Where they'll use the prompts
  3. QUERY TYPE:
    • PROMPTING — "X prompts", "prompting for X" → copy-paste prompts
    • RECOMMENDATIONS — "best X", "top X" → list of specific things
    • NEWS — "what's happening with X" → current events
    • GENERAL — anything else → broad understanding

Do NOT ask about target tool before research. Run research first, ask after.

Display your parsing before calling tools:

I'll research {TOPIC} across Reddit, X, YouTube, and the web.

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

Research typically takes 2-8 minutes. Starting now.

Research Execution

Step 1: Run the research script (FOREGROUND)

python3 "${SKILL_ROOT}/scripts/last30days.py" "$ARGUMENTS" --emit=compact --store 2>&1

Use a timeout of 300000 (5 minutes). The --store flag persists findings for watchlist/briefing integration.

The script auto-detects: API keys, Bird CLI, yt-dlp, web search backends.

Read the ENTIRE output. It contains Reddit, X, YouTube, AND web sections.


Step 2: WebSearch (supplement)

After the script finishes, use your WebSearch tool for additional coverage.

Choose queries based on QUERY_TYPE:

  • RECOMMENDATIONS: best {TOPIC} recommendations, {TOPIC} list examples
  • NEWS: {TOPIC} news 2026, {TOPIC} announcement update
  • PROMPTING: {TOPIC} prompts examples 2026, {TOPIC} techniques tips
  • GENERAL: {TOPIC} 2026, {TOPIC} discussion

Rules:

  • USE THE USER'S EXACT TERMINOLOGY
  • EXCLUDE reddit.com, x.com, twitter.com (covered by script)
  • Do NOT output "Sources:" list

Synthesis

Judge Agent rules:

  1. Weight Reddit/X HIGHER (engagement signals)
  2. Weight YouTube HIGH (views + transcript content)
  3. Weight web LOWER (no engagement data)
  4. Identify cross-source patterns (strongest signals)
  5. Extract top 3-5 actionable insights

Ground synthesis in ACTUAL research, not pre-existing knowledge.

Citation Rules

  • Cite sparingly: 1-2 sources per topic
  • Priority: @handles > r/subreddits > YouTube channels > web sources
  • Use publication names, never raw URLs
  • Lead with people, not publications

Display Results

1. "What I learned" (format depends on QUERY_TYPE)

If RECOMMENDATIONS — show specific items with sources:

Most mentioned:

[Name] - {n}x mentions
Use Case: [what it does]
Sources: @handle1, r/sub, blog.com

If PROMPTING/NEWS/GENERAL — show synthesis:

What I learned:

**{Topic 1}** — [1-2 sentences, per @handle or r/sub]

KEY PATTERNS:
1. [Pattern] — per @handle
2. [Pattern] — per r/sub

2. Stats box (calculate from actual output):

---
All agents reported back!
|- Reddit: {N} threads | {N} upvotes | {N} comments
|- X: {N} posts | {N} likes | {N} reposts
|- YouTube: {N} videos | {N} views | {N} with transcripts
|- Web: {N} pages (supplementary)
|- Top voices: @{handle1}, @{handle2} | r/{sub1}, r/{sub2}
---

3. Invitation with 2-3 specific follow-up suggestions based on research.


Follow-Up

After research, you are an EXPERT on this topic.

  • QUESTION → Answer from research (no new searches)
  • GO DEEPER → Elaborate from findings
  • CREATE/PROMPT → Write ONE prompt using research insights
  • Different topic → Run new research

When writing prompts, match the FORMAT the research recommends (JSON, structured, etc.).