agentdb-causal-explain

# Causal Explain Given a target memory (an episode, a failed test, an outgoing change), traverse the causal graph backwards through edges to surface the chain of preceding events that explain it. ## When to use - "Why did this fail?" - "What led to the regression?" - "Trace the dependency chain for X." - Incident postmortem — surface every step + decision that contributed. ## API ``` agentdb_causal_explain( targetMemoryId: <id> maxDepth?: 3 minConfidence?: 0.5 edgeWeights?: 'uplift' | 'confidence' | 'product' // ranking strategy ) ``` Returns a path or DAG of `(node, edge, node)` tuples ranked by combined `confidence × |uplift|`. Each step carries the relation (`caused`, `supersedes`, `depends-on`, etc.) so the explanation reads naturally. ## Output shape ``` Why did "deploy-2026-05-04 failed migration" happen? ┌─ skill[migrate-add-not-null-column] confidence 0.92 │ ─[supersedes]→ skill[v1: migrate-with-default] │ ─[caused]→ episode[long-running migration on 50M rows] │ ─[caused]→ episode[deploy-2026-05-04 failed migration] │ └─ adr[ADR-046: zero-downtime migration policy] confidence 0.78 ─[depends-on]→ skill[migrate-add-not-null-column] ``` ## Use the investigator agent For complex traces, dispatch the `agentdb-investigator` agent (this plugin) — it walks deeper, cross-references with hierarchical memory, and writes a postmortem-shaped report. ## Don't - Don't traverse with `maxDepth > 5` casually — graph fan-out gets exponential and the bandit's confidence weights don't compensate. - Don't ignore the `confidence` column. A high-uplift edge with confidence 0.3 is gossip, not evidence.