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veritas-kanban/docs/EXAMPLES-agent-workflows.md
Brad Groux eeb11ba219 feat(US-1600): Complete SOP Sprint + fix GH-86 & GH-87 
Documentation (8 new files in docs/):
- GETTING-STARTED.md: 5-min quickstart, BoardKit insights, sanity checks
- SOP-agent-task-workflow.md: Full lifecycle (claim → work → complete)
- SOP-sprint-planning.md: Epic → sprint → task hierarchy + estimation
- SOP-multi-agent-orchestration.md: PM + worker roles, handoff patterns
- SOP-cross-model-code-review.md: Claude ↔ GPT gate, checklist, RF-002 ref
- BEST-PRACTICES.md: 10 DOs + 10 DON'Ts based on real usage
- EXAMPLES-agent-workflows.md: 6 copy/pasteable recipes (feature, bug fix, docs, audit, content, research)
- TIPS-AND-TRICKS.md: CLI shortcuts, keyboard shortcuts, integrations (MCP, git worktrees, Obsidian)
- README.md: Added 'Documentation Map' linking all new docs

Bug Fixes:
- fix(GH-86): BulkActionsBar now handles archive errors gracefully
  * Per-task error tracking (replaces Promise.all)
  * Toast notifications on success/partial/failure
  * Logs individual failures to console

- fix(GH-87): Sidebar metrics now stay in sync with board state
  * Invalidate metrics cache when task status changes
  * Prevents up-to-30s lag in sidebar counts
  * Preserves timer state during mutations

Scripts:
- scripts/dev-clean.sh: Added explicit pnpm path resolution for launchd
- scripts/dev-watchdog.sh: Fixed restart storm prevention + pnpm path

BREAKING: None
TESTING:
- Manual: Bulk archive Done column tasks, verify toasts appear
- Manual: Move tasks between columns, verify sidebar counts update <2s
- Unit: Consider regression tests for metrics invalidation
2026-02-04 08:18:01 -06:00

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Real-World Agent Workflow Examples

Steal these end-to-end flows when building your own automations. Each example shows the goal, prompts, API/CLI calls, and outputs we expect.

1. Feature Development Sprint (BrainMeld PRD excerpt)

Goal: Build "Lessons Learned" field.

  1. Create task 
    vk create "Feature: Lessons Learned field" --project veritas-kanban --type feature --priority medium
  2. Prompt (worker) 
    Implement markdown lessonsLearned field on tasks (UI + API). Include migration + docs. Cross-model review required.
  3. Workflow
    • vk begin <id>
    • Implement server -> shared -> web changes
    • Update docs + tests
    • vk done <id> "Added lessons learned field"
  4. Outputs
    • Task summary with PR link
    • Lessons Learned comment describing future usage

2. Bug Fix (Archive bulk action)

Goal: Sprint archive button fails.

  1. Create bug task referencing GitHub Issue #86.
  2. Subtasks:
    • Reproduce in dev
    • Inspect network requests
    • Patch bulk archive handler
    • Add regression test (Playwright)
  3. CLI flow: vk begin, fix, vk done "Bulk archive now calls API"
  4. Cross-model review ensures UI + API parity.

3. Documentation Update

Goal: Add sanity checks to Getting Started.

  1. Task description includes sections to cover (API, UI, agent pickup).
  2. Agent edits docs/GETTING-STARTED.md + docs/TROUBLESHOOTING.md references.
  3. Completion summary links to diff + screenshot placeholders.

4. Security Audit (RF-002 style)

Goal: Run cross-model audit on repo.

  1. Task -> type=security, project=veritas-kanban.
  2. Subtasks: scope, run Codex audit, run Claude review, compile findings, create issues.
  3. Agents spawn using research prompt template, save results to refactoring/rf-002/*.
  4. Deliverables: Markdown report, HTML deck, GitHub issues.

5. Content Production (Podcast clip → LinkedIn post)

  1. Task type=content with acceptance criteria (summary, caption, schedule time).
  2. Agent fetches transcript, writes summary, drafts LinkedIn copy, saves assets to projects/start-small-think-big/....
  3. Completion summary includes copy + asset path; lessons learned capture platform insights.

6. Research & Report (Champions)

  1. Task type=research, project social, sprint CHAMP-02.
  2. Prompt includes dossier template, required sources, HTML deck requirement.
  3. Agent workflow: gather sources, write Markdown, generate HTML via script, brain-write.sh to mirror.
  4. Final comment: TL;DR + links to both artifacts.

Pattern to Copy

For any workflow:

  1. Task with crystal-clear done definition.
  2. Prompt stored in registry.
  3. API/CLI calls scripted (vk begin/done, time tracking, status updates).
  4. Artifacts saved to predictable paths and mirrored to Brain/engram if needed.
  5. Cross-model review if code/critical.
  6. Lessons learned field updated for systemic knowledge.

Use these recipes as seeds for your own automation playbooks.