Opportunity Scout#

Hunt for real demand signals — not news, not trends, but people expressing pain, frustration, and unmet needs that represent building opportunities.

Skill Directory#

All paths below are relative to this skill's directory.

• scripts/configure.py — manage niches, keywords, sources, schedule
• scripts/scan_sources.py — generate search queries and process results
• scripts/score_signals.py — score and rank findings
• scripts/digest.py — generate prioritized markdown digest
• scripts/history.py — track signals over time, detect trends
• references/signal-types.md — what counts as a demand signal (read when scoring)
• references/source-guide.md — how to configure sources effectively
• assets/config.example.json — example niche configurations

Data Files#

All state lives in the skill directory:

• config.json — active configuration (created by configure.py)
• history.json — signal history log (created by history.py)
• findings/ — raw and scored finding files per scan

Workflow#

First-Time Setup#

1. Run configure.py --init to create config.json from the example, or:

   • configure.py --add-niche "AI tools for small business" --keywords "wish,need,looking for,alternative to,frustrated"
   • configure.py --add-source reddit:r/SaaS,reddit:r/smallbusiness,hackernews
   • configure.py --set-schedule daily

Running a Scan#

Execute these steps in order:

1. Generate queries: Run scan_sources.py --generate-queries to get optimized search queries. It prints JSON with query strings.

2. Execute searches: For each query, call the web_search tool. Collect all results into a JSON array and save to a temp file.

3. Ingest results: Run scan_sources.py --ingest <results.json> to parse raw search results into standardized findings. Outputs findings JSON.

4. Score findings: Run score_signals.py <findings.json> to score each finding on signal strength, engagement, freshness, competition, and recurrence. Outputs scored JSON.

5. Update history: Run history.py --update <scored.json> to log findings and detect trend patterns (persistent, emerging, fading).

6. Generate digest: Run digest.py <scored.json> to produce the markdown report. Use --output <path> to save to a specific location (e.g., Obsidian vault). Use --max-results 20 to limit output.

Quick Scan (Single Command Summary)#

For a rapid scan of a single niche without full config:

1. Run scan_sources.py --quick "developer tools for AI agents" to get queries
2. Execute web_search for each query
3. Pipe results through score and digest

Reading References#

• Before scoring or evaluating signals manually, read references/signal-types.md for the taxonomy of demand signals and how to distinguish real demand from noise.
• When helping users configure sources, read references/source-guide.md.

Cron Integration#

Set schedule in config.json via configure.py --set-schedule daily|weekly. When triggered by cron, run the full scan workflow above. Save digest to the user's preferred output location (default: skill directory findings/).

Key Design Principles#

• Demand, not news: Every finding should express unmet need, frustration, or a gap. Filter aggressively — 10 strong signals beat 100 weak ones.
• Batch queries: Combine niche + keywords into fewer, broader queries rather than one query per keyword. Respect rate limits.
• Track over time: Signals that persist across scans are more valuable than one-offs. Use history.py to surface persistent demand and fading trends.
• Score honestly: High engagement + low competition + recurring = strong opportunity. Don't inflate scores — the user needs signal, not noise.