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<!-- Source: Extracted from agents/investigation/lore-analyst.md,
npx claudepluginhub vinhnxv/rune --plugin rune<!-- Source: Extracted from agents/investigation/lore-analyst.md, agents/investigation/goldmask-coordinator.md --> Common patterns for Goldmask investigation agents (Impact + Wisdom + Lore layers). The Goldmask pipeline synthesizes findings across three layers: | Layer | Agents | Purpose | |-------|--------|---------| | Impact | 5 tracers (API, business-logic, data-layer, config-dependency, eve...
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Common patterns for Goldmask investigation agents (Impact + Wisdom + Lore layers).
The Goldmask pipeline synthesizes findings across three layers:
| Layer | Agents | Purpose |
|---|---|---|
| Impact | 5 tracers (API, business-logic, data-layer, config-dependency, event-message) | WHAT changed and WHERE it ripples |
| Wisdom | wisdom-sage | WHY code was written (git archaeology, developer intent) |
| Lore | lore-analyst | Risk scores from quantitative git history analysis |
The goldmask-coordinator merges all three layers into a unified GOLDMASK.md report.
1. TaskList() → find available tasks
2. Claim task: TaskUpdate({ taskId, owner, status: "in_progress" })
3. Read task context (diff spec, file list, plan references)
4. Execute analysis protocol (layer-specific)
5. Write findings to output_path
6. Mark complete: TaskUpdate({ taskId, status: "completed" })
7. Send Seal to team-lead
The lore-analyst computes per-file risk scores using quantitative git metrics:
| Metric | Weight | What It Measures |
|---|---|---|
| Churn rate | 0.30 | Change frequency (commits/month) |
| Defect correlation | 0.25 | Fix-commit density |
| Author concentration | 0.20 | Bus factor (ownership spread) |
| Recency | 0.15 | Time since last change |
| Complexity proxy | 0.10 | File size as complexity estimate |
| Tier | Score Range | Meaning |
|---|---|---|
| CRITICAL | 80-100 | Highest risk — frequent changes, many fixes, few owners |
| HIGH | 60-79 | Elevated risk |
| MEDIUM | 40-59 | Moderate risk |
| LOW | 20-39 | Lower risk |
| STALE | 0-19 | Rarely touched — may indicate abandonment |
Output: risk-map.json with per-file scores, tiers, and co-change clusters.
The goldmask-coordinator merges layer outputs using a 3D priority formula:
priority = 0.40 * impact + 0.35 * caution + 0.25 * risk
# GOLDMASK — Cross-Layer Impact Analysis
## Priority Matrix
| File | Impact | Caution | Risk | Priority | Category |
## Collateral Damage Assessment
{Files affected indirectly by changes}
## Swarm Detection
{Areas where multiple findings converge}
## Top Findings
{Prioritized by 3D formula}