Topic Researcher — Outlier Content Idea Finder

Find scored, deduplicated topic ideas for short-form video content. Multi-angle research surfaces outlier ideas for review before any scripts get written.

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Content Filter — ENFORCED ON ALL TOPICS

Every topic MUST pass this filter before being included. No exceptions.

Niche Scope

YOUR NICHE: YOUR_NICHE_DESCRIPTION

Only surface topics that fit within your configured niche above. If no niche is configured, ask the user what their content niche is before proceeding.

Topic Quality Gate

Every topic must be:

1. Clearly definable — You can explain the entire workflow in under 60 seconds.
2. Solves a real problem — Backed by a real person's complaint/frustration from research. No hypothetical "wouldn't it be cool if" topics.
3. Buildable on camera — The viewer can watch you build or use it in a screen recording. If it can't be demonstrated, it's not a topic.
4. Niche-aligned — Fits within the configured niche scope above.

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Opening

When invoked with no arguments or just /topic-researcher:

What kind of topics are you looking for?

I can:

• Find topics — outlier content ideas scored for viral potential
• Industry-specific — topics for specific verticals or audiences
• Trending tools — what's blowing up on Product Hunt, X, Reddit right now

Just say "find me 10 topics" or "topics about [your niche]" to get started.

If $ARGUMENTS is provided, parse it for:

• COUNT — how many topics (default: 10)
• INDUSTRY — specific vertical to focus on (optional)
• ANGLE — specific research angle to emphasize (optional)

Skip the greeting and start researching immediately.

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Step 1: Multi-Angle Research

Run 6 research angles to surface diverse topic ideas. Don't just search one query — cast a wide net.

Research Angles

#	Angle	What to Search
1	Pain Points	Common tasks in the niche that are manual, time-consuming, or frustrating
2	Builds / Demos	Things people are building, creating, or demonstrating in this space
3	Case Studies / ROI	Real results, time saved, money saved, before/after transformations
4	Experiments	"I tried X" posts — experiments and tests with real outcomes
5	Competitor Creators	Other creators making content in this niche — what's getting engagement
6	Industry-Specific	Niche-specific workflows, tools, or use cases (rotate sub-niches)

Execution

Batch 1 — last30days.py broad sweep (angles 1-3):

python3 ~/.claude/skills/last30days/scripts/last30days.py "{NICHE KEYWORDS}" --emit=compact 2>&1

Batch 2 — Targeted WebSearch (angles 4-6):

Run 3 WebSearch calls:

1. Experiments: "{niche keywords}" results OR "I tried" OR experiment workflow (blocked_domains: reddit.com, x.com, twitter.com)
2. Competitor Creators: "{niche keywords}" creator content viral tips (blocked_domains: reddit.com, x.com, twitter.com)
3. Industry-Specific: "{niche keywords}" {INDUSTRY OR sub-niche} use case workflow (blocked_domains: reddit.com, x.com, twitter.com)

If the user specifies an industry filter, weight angle 6 heavily — run 2 industry-specific searches instead of 1.

Post-Research Filter

After gathering raw results, apply the Content Filter (above) to every potential topic. Discard anything that doesn't fit the configured niche. Reframe surviving topics through the niche lens where possible.

Extract from Results

For each potential topic, extract:

• Topic idea — one-line video concept
• Angle — which of the 6 angles it came from
• Tool — specific tool involved (if any)
• Pain — the problem it solves
• Proof — specific numbers, stats, before/after data
• Source — Reddit thread, X post, article URL
• Engagement — upvotes, likes, comments, views

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Step 2: Deduplication

Before scoring, check both Notion databases to flag topics already covered.

Query Script Library

Read ~/.config/notion-content/config.json to get script_library_db_id. If present:

Use notion-search with:

• data_source_url: collection://{script_library_db_id}
• Search for broad topic keywords
• Extract: Script Title + Topic from results

If config missing, skip silently.

Query Inspiration Library

Read ~/.config/notion-content/config.json to get inspiration_data_source_id. If present:

Use notion-search with:

• data_source_url: collection://{inspiration_data_source_id}
• Search for broad topic keywords
• Extract: Title + Topic Tags from results

If config missing, skip silently.

Flagging

Compare each discovered topic against both databases:

• If a topic substantially overlaps with an existing script or inspiration entry, mark it COVERED
• Do NOT auto-discard covered topics — let the user decide
• A topic is "covered" if the same tool + same pain point + same angle already exists

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Step 3: Outlier Scoring

Score each topic on 5 dimensions (1-5 stars each). Only present topics scoring 3+ stars overall.

Dimension	Weight	5-star =	1-star =
Engagement	30%	Multiple high-engagement posts (Reddit 100+, X 500+)	Single low-engagement mention
Specificity	25%	Named tool + named use case + specific numbers	Generic vague topic
Freshness	15%	Trending in last 7 days	30+ days old, widely covered
Proof Availability	15%	Screenshot-ready demo, specific stats, before/after	Abstract, no visual proof
Competition Gap	15%	No major creators covering this angle	Already saturated

Scoring Formula

Weighted Score = (Engagement x 0.30) + (Specificity x 0.25) + (Freshness x 0.15) + (Proof x 0.15) + (Competition Gap x 0.15)

Round to one decimal. Convert to star display (e.g., 4.2 = four filled stars + partial).

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Step 3.5: Deep Research — 5 Angles Per Topic (AUTOMATIC)

This step runs automatically after scoring. For every topic that scores 3+ stars, run deep research to surface 5 distinct script angles. This makes each topic card significantly richer and gives the user real options for how to approach the script.

Why This Step Exists

One topic can become 5 completely different videos depending on the angle. Deep research finds the best angle by looking at what's actually resonating online for each topic.

Execution

For each topic that passed scoring, run 2 targeted WebSearch calls:

1. Angle discovery: "{topic keywords}" script OR video OR content angle approach 2026 (blocked_domains: reddit.com, x.com, twitter.com)
2. Proof mining: "{tool from topic}" results OR "saved me" OR "before and after" OR case study (blocked_domains: reddit.com, x.com, twitter.com)

To keep speed reasonable: Run searches for all topics in parallel (not sequentially). If there are 10 topics, that's 20 WebSearch calls — run them all at once.

The 5 Angle Framework

For each topic, generate exactly 5 angles using this framework. Not every angle will be strong for every topic — that's fine. Rate each angle 1-3 strength dots (three dots = strongest) so the user can see which ones have the most potential.

Angle	What It Is	Best When
1. Proof-First	Lead with the result/number. Show the outcome before explaining how.	Strong stats, dollar amounts, or time-saved numbers exist in research
2. Step-by-Step Tutorial	Walk through the build/workflow on camera.	The workflow is clearly definable and can be demoed in under 60 seconds
3. Challenge / Experiment	Frame it as a test with an unknown outcome.	There's a bold claim to test, or a common assumption to challenge
4. Myth-Buster / Contrarian	Challenge the conventional wisdom.	There's a popular belief that the research contradicts
5. Industry / Niche Remix	Narrow the audience to a specific sub-niche.	The topic can be applied to a specific industry with tailored examples

Angle Card Format

For each topic, append the 5 angles below the Script Preview:

Angles:
1. ●●● PROOF-FIRST: "I replaced my $3K/mo agency with a free tool — here are the results"
   → Key proof: [specific stat]. Screen: [what you'd show]. Why strong: [1 sentence].
2. ●● TUTORIAL: "How to set up your own audit in 4 minutes"
   → Steps: [3-step summary]. Screen: [what you'd show]. Why strong: [1 sentence].
3. ●●● CHALLENGE: "I gave this tool 7 days to outperform my agency. Here's who won."
   → Setup: [the challenge framing]. Payoff: [what the result reveals]. Why strong: [1 sentence].
4. ● MYTH-BUSTER: "Your agency doesn't want you to see this free tool"
   → Myth: [the assumption]. Reality: [what research shows]. Why strong: [1 sentence].
5. ●● NICHE REMIX: "If you run a restaurant, this tool was made for you"
   → Niche: [industry]. Tailored proof: [industry-specific stat]. Why strong: [1 sentence].

Enrichment from Deep Research

Use the WebSearch results to fill in each angle with real data:

• Proof-First — find the strongest stat, dollar amount, or time-saved number
• Tutorial — find existing tutorials or walkthroughs to identify the clearest 3-step flow
• Challenge — find "I tried X" posts or experiment-style content for framing inspiration
• Myth-Buster — find contrarian takes or common misconceptions about the topic
• Niche Remix — find industry-specific applications, stats, or case studies

If deep research surfaces a new angle that doesn't fit the 5-framework but is clearly strong (e.g., a trending meme format, a news hook, a celebrity endorsement), add it as a Bonus Angle with a note on why it's worth considering.

Strength Rating Guide

• ●●● = Strong proof exists, clear visual demo, high engagement signal from research
• ●● = Decent framing but proof is weaker or demo is less visual
• ● = Possible but would need more creative work to make it land

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Step 4: Topic Cards

Present topics as ranked cards, highest score first.

Card Format

TOPIC #1 ★★★★★ (4.8)
Title: [one-line video title idea]
Angle: [proof-first / pain point / tutorial / experiment / case study]
Tool: [specific tool if applicable]
Workflow: [1-2 sentence step-by-step — what you'd build or do on camera]
Pain: [problem it solves]
Proof: [specific number/stat from research]
Source: [Reddit thread, X post, etc.]
Why it works: [1 sentence]
Status: NEW or COVERED

Script Preview:
- [Beat 1: Hook — what you'd say/show in the first 3 seconds]
- [Beat 2: Problem — the pain point you're calling out]
- [Beat 3: Solution — what you build or demo on screen]
- [Beat 4: Proof — the specific result or number that lands]
- [Beat 5: CTA — what you tell them to do next]

The Script Preview gives a quick feel for what the final video would actually be. Each bullet is a "beat" — roughly one scene or moment in the video. This helps the user decide whether the topic has enough substance to become a full script before approving it.

Auto-Push to Notion

Immediately after presenting topic cards, push ALL topics to the Script Library as Ideas/Topics if Notion is configured. Do not wait for approval — push them right away so they appear on the board.

Read ~/.config/notion-content/config.json to get script_library_db_id. If present:

Use notion-create-pages with:

• data_source_id: value of script_library_db_id from config
• For each topic card, create a page with:
  • Script Title = the topic card Title
  • Status = Ideas/Topics
  • Topic = user-specified topic or niche
  • Notes = Compact summary: Score: {score} | Angle: {angle} | Tool: {tool} | Workflow: {workflow}
  • Pain Point Source = the Source URL/reference from the topic card
  • Page content = Script Preview (5 beats) + the 5 Angles from deep research, formatted as shown in Step 3.5

If config missing, skip silently and just present the cards.

Track the Notion page URL returned for each topic — you'll need it for approval/rejection.

Presentation

After pushing to Notion (or if Notion not configured), show the cards and summary:

{N} topics found. {M} new, {K} overlap with existing scripts.
All {N} topics pushed to Script Library → Ideas/Topics column.

What next?
- "approve 1, 3, 5" — keep these, remove the rest
- "approve all" — keep everything
- "reject 2, 4" — remove specific topics
- "modify 2" — adjust angle/framing before deciding
- "more topics" — run another research pass
- "save" — save these results to a file

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Step 5: Approval Workflow

Approve

When user says "approve 1, 3, 5" (or similar):

1. Confirm the selected topics
2. Delete rejected topics from Notion (if Notion configured) — use notion-update-page or the Notion API to archive/delete the pages for any topic NOT in the approved list
3. Save approved topics to memory — append to ~/.claude/content-system/approved-topics.md (create if it doesn't exist). Format:

   ## Approved {date}
   - **{Title}** — {Angle} | {Tool} | {Pain} | Score: {score}
   

   This file builds a pattern of what the user gravitates toward, reinforcing future topic generation.
4. Tell the user: "{N} approved, {M} removed from board."
5. Offer: "Ready to write scripts for these? Say 'write scripts' to start."

Approve All

When user says "approve all":

1. Keep all topics in Notion
2. Save all to memory file
3. Offer script handoff

Reject

When user says "reject 2, 4" (or similar):

1. Delete those pages from Notion (if configured) — remove from Script Library entirely
2. Do NOT save rejected topics to memory
3. Show updated count: "{N} remaining in Ideas/Topics."

Modify

When user says "modify 2" (or similar):

1. Show the topic card
2. Ask what to change (angle, tool, framing)
3. Update the card and re-score
4. Update the Notion page with the modified title/notes (if configured)
5. Add back to the approved list if user confirms

More Topics

When user says "more topics":

1. Run another research pass with different search queries
2. Deduplicate against already-shown topics AND existing Ideas/Topics in Notion
3. Present new cards and push new topics to Notion

Save

When user says "save":

1. Save all topic cards to ~/ai-content-system/output/topic-research-{YYYY-MM-DD}.md
2. Include scores, sources, and approval status
3. Tell the user the file path

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Memory Layer — Topic Preferences

The file ~/.claude/content-system/approved-topics.md acts as a reinforcement layer.

How It Works

• Every approved topic gets appended with its angle, tool, pain point, and score
• Every rejected topic is NOT saved — absence is signal
• Over time, this file builds a profile of what the user wants:
  • Which angles they prefer (proof-first vs tutorial vs experiment)
  • Which tools they gravitate toward
  • Which pain points resonate
  • What score threshold they actually approve at

Using the Memory

Before scoring topics, read ~/.claude/content-system/approved-topics.md if it exists. Use the patterns to:

1. Boost scores for topics matching approved patterns (preferred angles, tools, pain types)
2. Lower scores for topics similar to ones that were previously rejected (if a pattern emerges)
3. Mention the pattern to the user: "Based on your history, you tend to approve [proof-first builds]. Scored accordingly."

This makes each research pass smarter than the last.

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Step 6: Handoff to Content-Scripting

When user says "write scripts" after approving topics:

1. Format approved topics as pain points:

   • One-line summary of each topic
   • Source + engagement data
   • The specific solution

2. Read ~/.claude/skills/content-scripting/skill.md

3. Start at Step 3 (Outlier Checklist) with:

   • TOPIC = user-specified topic or niche
   • AUDIENCE = user-specified audience
   • Pain points = the approved topic cards formatted as pain points

4. Follow content-scripting from Step 3 onward — the research is already done.

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State Management

Track throughout the conversation:

RESEARCH_STATUS: [not started | in progress | complete]
TOPICS_FOUND: [count]
TOPICS_APPROVED: [list of approved topic numbers]
TOPICS_REJECTED: [list of rejected topic numbers]
TOPICS_COVERED: [list of covered topic numbers]
INDUSTRY_FILTER: [if specified]
NOTION_PAGES: [map of topic number → Notion page ID/URL — needed for deletion]

CRITICAL: Always track the Notion page URL/ID returned when creating pages. Without this, you cannot delete rejected topics.