Connect agent-performance-analyzer to AgentDB for trend, recall, and regression memory

[Copilot]

This change wires agent-performance-analyzer into AgentDB and updates its operating instructions to use AgentDB as a first-class analysis backend. It targets the requested outcomes: faster trend deltas, semantic recall of similar incidents, and persistence of resolved patterns for regression detection.

• Workflow integration

  • Adds the shared AgentDB MCP import to the analyzer workflow:
    • shared/mcp/agentdb.md

• AgentDB analysis contract

  • Defines a consistent metrics/profile record shape for ingestion.
  • Directs the agent to compute score deltas/trends from AgentDB queries before scanning all daily files.
  • Adds concrete semantic recall query targets (e.g., token budget exhaustion, duplicate issue creation, PR rejection spikes).

• Pattern persistence for future regression detection

  • Specifies persisted resolved-pattern fields:
    • pattern_id, pattern_name, resolution, resolved_at, workflows_affected, regression_signals
  • Clarifies regression signal examples to standardize future detection logic.

imports:
  - uses: shared/meta-analysis-base.md
    with:
      toolsets: [default, actions, repos]
  - shared/reporting.md
  - shared/mcp/agentdb.md
  - shared/observability-otlp.md

[Copilot]

Pull request overview

Wires the agent-performance-analyzer workflow to the shared AgentDB MCP, and documents how the agent should ingest metrics, query for trends/semantic recall, and persist resolved patterns for regression detection.

Changes:

• Adds the shared/mcp/agentdb.md import to the analyzer workflow.
• Documents a standard record shape for ingested metrics/profiles and AgentDB query targets.
• Specifies fields for persisting resolved patterns and example regression signals.

Show a summary per file

File	Description
.github/workflows/agent-performance-analyzer.md	Imports AgentDB MCP and adds instructions for ingestion, queries, semantic recall, and pattern persistence.

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