Persona Overview

• Agent: developer.instructions (agentic-workflows custom agent)
• Scenarios Tested: 7 (across 5 personas)
• Average Quality Score: 5.0/5.0
• Run: §23727015995

Personas & Scenarios Tested

Key Findings

• All 7 scenarios scored 5.0/5.0 — the agent consistently produced production-ready workflow configurations with no significant gaps across any dimension.
• Engine selection is consistently thoughtful: claude for reasoning-heavy analysis, codex for sequential log investigation, copilot for lighter classification/writing tasks.
• Security practices were uniformly excellent: every response used strict: true, scoped bash allowlists, wrote only via safe-outputs, and applied noop early-exit conditions.
• The most sophisticated response (cost anomaly) introduced a credential-isolation architecture where cloud API credentials live only in steps: pre-processing blocks and are never accessible to the agent.
• No weak responses found — the agent demonstrated high-signal responses across all workflow types and personas.

Top Patterns

1. Triggers: PR workflows use pull_request with [opened, synchronize, reopened/ready_for_review]. Scheduled workflows pair schedule: with workflow_dispatch: for manual re-runs. Event-driven workflows use workflow_run with an if: conclusion guard.
2. Tools: GitHub MCP always scoped to specific toolsets (never wildcard). Bash always uses an allowlist of read-only tools. repo-memory recommended for cross-run persistence (baselines, trend tracking). web-fetch used where external API lookups are needed.
3. Security constants: strict: true on every workflow; concurrency: cancel-in-progress: true on all PR workflows; hide-older-comments: true on PR comment outputs; max: limits on all safe-outputs; noop condition for empty/no-op runs.

Recommendations

1. Add paths: filter examples to the workflow creation guide (.github/aw/*.md) — show PR-triggered workflows using paths: to scope execution to relevant file types. This is a high-leverage optimization for reducing CI cost and agent token consumption.

2. Add a min-integrity decision table to the documentation — clarify when to use none (any contributor, fork PRs), low, and approved. The current behavior is correct but inconsistent across scenarios; explicit guidance would standardize it.

3. Add a repo-memory cold-start pattern to the prompt library — a reusable snippet showing how to handle the first run when no baseline/history file exists yet. This prevents agent confusion in week 1 of any memory-enabled scheduled workflow.

References:

• §23727015995

AI generated by Agent Persona Explorer · history