Copy this folder when creating a new workshop event.
cp -R event-specific/_template event-specific/YYYY-MM-DD-<event-slug>Then update every placeholder with event-specific details.
Also add the new event to ../events.json so the repo audit can validate event entry points and required workshop files.
- Event name:
<EVENT_NAME> - Date:
<EVENT_DATE> - Audience:
<AUDIENCE> - Purpose and expected outcome:
<PURPOSE_AND_OUTCOME> - Accountable owner:
<ACCOUNTABLE_OWNER> - Facilitator(s):
<FACILITATORS> - Approval authority:
<APPROVER> - Tool track(s):
<TOOL_TRACKS_OR_NONE> - Source and research owner:
<RESEARCH_OWNER> - Learning evidence:
<REVIEWABLE_ARTIFACT_OR_MEASURE> - Post-event review owner:
<REVIEW_OWNER> - Community/follow-up link:
<COMMUNITY_LINK>
Tool and framework choices belong to the event's learning context. They do not change Commons, another ecosystem product, or AI Dev Days as a whole.
- Attendee links
- Requirements
- Facilitator runbook
- Fallback plan
- Day-before checklist
- Post-event review
- Root start page
- Root facilitator runbook
- AI Dev Days Charter and Commons adoption
- AI-Native Operating Framework teaching alignment, when selected by the event
- Research and education method
- Research source note template
- First success lab
- Markdown thinking-layer lab
- Helper install triage
- Publication safety
- Event refresh checklist
Before approval, confirm the packet makes the following business meaning clear without forcing a particular document layout:
- intent: purpose, audience, scope, outcome, and requirements;
- responsibility: owner, participants, authority, approval, and escalation;
- work: prerequisites, agenda, activities, decisions, outputs, and handoffs;
- control: safety, permissions, exceptions, stop conditions, and recovery;
- assurance: completion, checks, evidence, reviewers, and authoritative result;
- learning: maintenance owner, feedback, lessons, and review triggers.
- Material claims link to primary or authoritative sources.
- Facts, interpretation, event assumptions, and decisions are distinguishable.
- The intended learner, prerequisite, outcome, exercise, and reviewable evidence are explicit.
- Learners can distinguish source material, AI output, inference, and the authoritative result.
- Event feedback has a post-event disposition path and does not become reusable curriculum automatically.