Matt Van Horn
2ff9b6f6c1
- _compute_text_similarity() now checks outcome names with bidirectional substring matching (0.85) and token overlap (0.7), not just event titles - Collect outcomes from ALL active markets per event, filter to >1% price - Reorder outcome_prices to surface topic-matching outcome before top-3 truncation - Add SKILL.md "Prediction Markets" synthesis section with structural/long-term market preference, domain examples, citation format, and narrative weaving - Add Polymarket to citation priority list between HN and Web - Update stats box template to show up to 5 market odds - Fix render.py "vol24h" label to "volume" - Add NCAA seed fixture event for outcome-only matching tests - 82 polymarket tests pass (14 new) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
154 lines
5.1 KiB
Markdown
154 lines
5.1 KiB
Markdown
# One-Shot Research Mode
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Research ANY topic across Reddit, X, YouTube, Polymarket, and the web. Surface what people are actually discussing, recommending, betting on, and debating right now.
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## Parse User Intent
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Before doing anything, parse the user's input for:
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1. **TOPIC**: What they want to learn about
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2. **TARGET TOOL** (if specified): Where they'll use the prompts
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3. **QUERY TYPE**:
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- **PROMPTING** — "X prompts", "prompting for X" → copy-paste prompts
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- **RECOMMENDATIONS** — "best X", "top X" → list of specific things
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- **NEWS** — "what's happening with X" → current events
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- **GENERAL** — anything else → broad understanding
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**Do NOT ask about target tool before research.** Run research first, ask after.
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**Display your parsing** before calling tools:
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```
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I'll research {TOPIC} across Reddit, X, YouTube, Polymarket, and the web.
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Parsed intent:
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- TOPIC = {TOPIC}
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- TARGET_TOOL = {TARGET_TOOL or "unknown"}
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- QUERY_TYPE = {QUERY_TYPE}
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Research typically takes 2-8 minutes. Starting now.
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```
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---
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## Step 0.5: Resolve X Handle (if topic could have an X account)
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If TOPIC looks like it could have its own X account - **people, creators, brands, products, tools, companies** (e.g., "Dor Brothers", "Nano Banana Pro", "Seedance"), do ONE quick WebSearch:
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```
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WebSearch("{TOPIC} X twitter handle site:x.com")
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```
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Extract their X handle from results (look for `x.com/{handle}` URLs or "@handle" mentions). **Verify it's the real account, not a parody/fan page** - check for verified status, official website links, consistent naming. If found, pass as `--x-handle={handle}` (no @). Skip if TOPIC is a generic concept (not an entity), already has @, uses `--quick`, or no official account exists.
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---
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## Research Execution
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**Step 1: Run the research script (FOREGROUND)**
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```bash
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python3 "${SKILL_ROOT}/scripts/last30days.py" "$ARGUMENTS" --emit=compact --store 2>&1 # Add --x-handle=HANDLE if resolved
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```
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Use a **timeout of 300000** (5 minutes). The `--store` flag persists findings for watchlist/briefing integration.
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The script auto-detects: API keys, Bird CLI, yt-dlp, web search backends.
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**Read the ENTIRE output.** It contains Reddit, X, YouTube, AND web sections.
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---
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**Step 2: WebSearch (supplement)**
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After the script finishes, use your WebSearch tool for additional coverage.
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Choose queries based on QUERY_TYPE:
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- **RECOMMENDATIONS**: `best {TOPIC} recommendations`, `{TOPIC} list examples`
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- **NEWS**: `{TOPIC} news 2026`, `{TOPIC} announcement update`
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- **PROMPTING**: `{TOPIC} prompts examples 2026`, `{TOPIC} techniques tips`
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- **GENERAL**: `{TOPIC} 2026`, `{TOPIC} discussion`
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Rules:
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- **USE THE USER'S EXACT TERMINOLOGY**
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- EXCLUDE reddit.com, x.com, twitter.com (covered by script)
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- Do NOT output "Sources:" list
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---
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## Synthesis
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**Judge Agent rules:**
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1. Weight Reddit/X HIGHER (engagement signals)
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2. Weight YouTube HIGH (views + transcript content)
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3. Weight web LOWER (no engagement data)
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4. Identify cross-source patterns (strongest signals)
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5. Extract top 3-5 actionable insights
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6. **Prediction markets are high-signal when relevant** - real money on outcomes cuts through opinion. Prefer structural/long-term markets (championship > regular season, regime change > near-term deadline). When the topic is an outcome in a multi-outcome market, call out that specific outcome's odds and movement. Weave odds into narrative: "Polymarket has X at Y% (up/down Z%)"
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**Ground synthesis in ACTUAL research, not pre-existing knowledge.**
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### Citation Rules
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- Cite sparingly: 1-2 sources per topic
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- Priority: @handles > r/subreddits > YouTube channels > web sources
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- Use publication names, never raw URLs
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- Lead with people, not publications
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---
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## Display Results
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**1. "What I learned"** (format depends on QUERY_TYPE)
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**If RECOMMENDATIONS** — show specific items with sources:
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```
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Most mentioned:
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[Name] - {n}x mentions
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Use Case: [what it does]
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Sources: @handle1, r/sub, blog.com
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```
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**If PROMPTING/NEWS/GENERAL** — show synthesis:
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```
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What I learned:
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**{Topic 1}** — [1-2 sentences, per @handle or r/sub]
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KEY PATTERNS:
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1. [Pattern] — per @handle
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2. [Pattern] — per r/sub
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```
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**2. Stats box** (calculate from actual output):
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```
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---
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All agents reported back!
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|- Reddit: {N} threads | {N} upvotes | {N} comments
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|- X: {N} posts | {N} likes | {N} reposts
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|- YouTube: {N} videos | {N} views | {N} with transcripts
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|- Polymarket: {N} markets | {summary of up to 5 most relevant market odds}
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|- Web: {N} pages (supplementary)
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|- Top voices: @{handle1}, @{handle2} | r/{sub1}, r/{sub2}
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---
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```
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**CRITICAL: Omit any source line that returned 0 results.** Do NOT show "0 threads", "0 stories", "0 markets", or "(no results this cycle)". If a source found nothing, DELETE that line entirely.
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**3. Invitation** with 2-3 specific follow-up suggestions based on research.
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---
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## Follow-Up
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After research, you are an **EXPERT** on this topic.
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- **QUESTION** → Answer from research (no new searches)
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- **GO DEEPER** → Elaborate from findings
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- **CREATE/PROMPT** → Write ONE prompt using research insights
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- **Different topic** → Run new research
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When writing prompts, match the FORMAT the research recommends (JSON, structured, etc.).
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