docs: add custom agent prerequisites, preflight pattern, and tool-unavailable fallback

.github/aw/create-agentic-workflow.md

@@ -60,6 +60,17 @@ Use this mode for exploratory testing, persona walkthroughs, and "what workflow
 - Exit ad hoc evaluation mode only when the user explicitly asks to create, implement, or write the workflow file.
 - End by offering to turn the recommendation into `.github/workflows/<workflow-id>.md` if the user wants to proceed.
 
+### Preflight Check for Custom Agent Runtimes
+
+When the evaluation workflow depends on a custom agent tool (for example the `agentic-workflows` tool), run a preflight check before executing scenarios:
+
+1. **Probe the tool** — make a single minimal call (for example `"Are you available?"`) and confirm the response is non-empty, error-free, and contains a recognisable affirmative or workflow guidance signal.
+2. **On success** — proceed with evaluation scenarios normally.
+3. **On failure** — record `{ "status": "tool-unavailable", "reason": "<error>" }`, switch to direct design reasoning using `.github/aw/*.md` reference files, and label all derived recommendations as **inferred**.
+4. **Always publish a result** — emit the report regardless of tool availability; include a `Tooling Availability` note that lists which tools succeeded, which failed, and the exact errors observed.
+
+See [github-agentic-workflows.md](github-agentic-workflows.md#custom-agent-tool-prerequisites) for the full failure-mode table and fallback pattern.
+
 ## Design Checklist
 
 ### 1. Pick the workflow ID

.github/workflows/agent-persona-explorer.md

@@ -133,6 +133,18 @@ Store all scenarios in cache memory.
 
 **Token Budget Optimization**: Test a **representative subset of 3-4 scenarios** from the 6 generated above (not all) to reduce token consumption and ensure budget remains for Phase 5 publishing.
 
+### Preflight: Verify Agent Tool Availability
+
+Before running any scenario, probe the "agentic-workflows" custom agent with a minimal availability prompt such as "Are you available? Reply with yes or no.".
+
+- **If the probe succeeds** (non-empty, error-free response): proceed with scenario testing below.
+- **If the probe fails** (for example `Copilot CLI not installed`, timeout, or empty response):
+  1. Record `{ "status": "tool-unavailable", "reason": "<exact error>" }` in cache memory.
+  2. Derive the design recommendation using direct reasoning from the `.github/aw/*.md` reference files and label it **inferred**.
+  3. Skip the scenario-by-scenario agent invocations below and proceed directly to Phase 4 and Phase 5.
+  4. Set the average quality score to `N/A` (tool-unavailable) rather than a numeric rating.
+  5. Include a `### Tooling Availability` subsection in the published report that states which tools were probed, the outcome, and the exact error message.
+
 {{#if experiments.sub_agent_strategy == 'batch' }}
 Invoke the "agentic-workflows" custom agent **once** with all 3-4 selected scenarios presented together in a structured list:
 
@@ -238,7 +250,11 @@ Example:
 - **Agent**: [name]
 - **Personas This Run**: [3 persona names]
 - **Scenarios Tested**: [count - should be 3-4, selected from the 6 generated in Phase 2 (2 per persona × 3 personas)]
-- **Average Quality Score**: [X.X/5.0]
+- **Average Quality Score**: [X.X/5.0 or N/A (tool-unavailable)]
+
+### Tooling Availability
+- **agentic-workflows tool**: [available | unavailable — reason: "<exact error>"]
+- *(list any other tools probed)*
 
 ### Key Findings (3-5 bullet points max)
 [High-level insights - keep concise]