Research Scout

Research Scout

Find new information that challenges or extends current project knowledge.

Scope

This skill does one job:

1. Gather fresh signals from web search, Reddit, Hacker News, and Quora.
2. Cross-check against local docs so only novel or contradictory items survive.
3. Stage validated findings in memory/long-term-memory.md under ## new_learnings.

Commands

/research-scout run
/research-scout weekly-review
/research-scout status

• run: Execute the scout pipeline and append validated findings.
• weekly-review: Promote confirmed patterns and clear staging.
• status: Read current staged learnings and promoted patterns.

Execution Rules

1. Use these source classes every run:
   • Web search (Google News RSS or equivalent search feed)
   • Reddit (search RSS)
   • Hacker News (Algolia API)
   • Quora (search constrained to quora.com)
2. Keep only findings that are either:
   • New: not already represented in local docs
   • Contradictory: challenge an existing assumption or prior guidance
3. Discard duplicates and low-signal items.
4. Each accepted finding must include:
   • Timestamp (UTC ISO)
   • Source URL
   • One-line note on what changed or was added

Storage Contract

Write staged findings to:

• memory/long-term-memory.md → ## new_learnings

Entry format:

- [ ] 2026-04-25T18:00:00Z | source: https://example.com | note: Adds a new workflow for AI eval gating before rollout.

Weekly Promotion Contract

Weekly review promotes repeated/confirmed patterns into:

• memory/long-term-memory.md → ## promoted_patterns
• context-library/pm-os-learning-log.md → ## Research Scout Promoted Patterns

After promotion, clear ## new_learnings so it is a fresh staging queue.

Automation

Run through GitHub workflows:

• .github/workflows/research-scout-nightly.yml (3x nightly)
• .github/workflows/research-scout-weekly.yml (weekly promotion + clear)

Core script:

• scripts/research-scout/research_scout.py