Scan

Pre-screen with bulk metrics:

• Call {{MCP_TOOL_FOR_METRICS}} for all subjects at once
• Extract quality indicators: {{QUALITY_INDICATORS e.g. "relevance_score, freshness, engagement"}}
• Exclude subjects that fail minimum thresholds:
  • {{THRESHOLD_1 e.g. "quality_score < 3 → exclude"}}
  • {{THRESHOLD_2 e.g. "freshness < minimum → exclude"}}
• Record exclusion reasons for the scan note

Deep analysis of survivors:

• For each subject that passed pre-screening:
  • Call {{MCP_TOOL_FOR_DETAIL}} to get detailed data
  • Evaluate against process entry criteria
  • Compute: {{COMPUTED_METRICS e.g. "fit_score, expected_outcome, effort_estimate"}}

Score and rank candidates:

• Rank by {{RANKING_CRITERIA e.g. "quality_score DESC, effort ASC"}}
• Include: subject, key metrics, fit assessment, recommended action

Write scan note via obsidian:obsidian-markdown:

• File: knowledge-base/scans/{{today}}_{{process_name}}.md
• Frontmatter:

  type: scan
  scan_date: {{today}}
  process: "[[{{process_name}}]]"
  candidates_found: {{count}}
  list_source: {{list_name}}
  

• Body:
  • Candidates table: ranked list with key metrics
  • Excluded table: subjects that failed screening with reasons
  • Conditions: environmental context at time of scan

Display results to the user:

• Top candidates with key metrics
• Summary of excluded subjects
• Recommendation based on process rules