Run a live QA pass over the app — first map all user-facing functionality, then boot the full stack and manually exercise flows with Playwright, recording pass/fail evidence and filing a QA report. Use for release QA, post-merge smoke passes, or "QA the app / this area" requests.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Two phases, always in this order. The map is what keeps the live pass honest: without it you test what's easy, not what exists.
Build (or refresh) docs/qa/functionality-map.md by enumerating from code, not
memory:
frontend/pages/** (file-based routing).backend/app/routes/*.py — group by domain.frontend/middleware/{auth,permissions,onboarding}.global.ts —
note which flows require auth, permissions, or onboarding state.frontend/tests/** and backend
e2e in backend/tests/e2e/** — mark what's already automated.Format — one row per user-facing flow:
| Area | Flow | Route(s) | API | Automated coverage | Last QA | Status |
|---|
Keep rows behavioral ("invite a member and they can sign in"), not structural ("members page renders"). If a functionality map already exists, diff it against the current routes/pages and update it rather than regenerating.
tools/agent/boot_stack.sh # backend :8000 + built frontend :3000
cd backend && uv run python ../tools/agent/seed_org.py --demo --invite member@example.com
http://localhost:3000, not by calling APIs directly. Use throwaway specs or
cd frontend && node ../tools/agent/capture.mjs <url> <shot.png> for
evidence. export PLAYWRIGHT_BROWSERS_PATH=/opt/pw-browsers in sandboxes./tmp/bow-agent/backend.log).docs/feedback-loops/qa-<date>-<scope>.md:
scope, environment, table of flows with verdicts + evidence, and a
findings section where each failure follows the sandbox-feedback-loop
format (symptom → repro → suspected root cause with file:line).Last QA / Status columns as you go — the
map is the durable artifact, the report is the snapshot.No reviews yet. Tell others what you thought.
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Add a new data source / connection type (e.g. a SQL Server-like database) end to end — client with get_schemas/execute_query, config + credentials schemas, registry entry, icon, tests — plus the verification steps. Also covers adding or updating an MCP connector (a preset, not a new type). Use when adding or significantly extending a connector.
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Add a new LLM model to the preset catalog, or a whole new LLM provider — with the mandatory pre-flight verification of model id, pricing, and context window against the provider's official docs. Use when asked to add/update LLM models, providers, or pricing.
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Query, visualize, and analyze data in a Bag of Words (BOW) workspace via the bagofwords MCP tools. Use for creating reports, running tracked queries, and building dashboards.
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Update the product docs at docs.bagofwords.com (Mintlify) with text and fresh screenshots after a user-facing change ships. Use when a merged change alters user-visible behavior, adds a feature, or when asked to update/refresh documentation.
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The locale/i18n architecture of bagofwords — catalogs, resolution order, RTL, backend contracts — and the procedures for adding strings, adding a locale, or translating UI. Use when adding/changing user-facing strings, working on RTL, emails, localized errors, or anything locale-related.
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Build or refresh a product README showcase using a seeded Bag of Words workspace, polished in-product screenshots, and repository-ready visual assets. Use when positioning has changed, README screenshots are stale, a launch/demo workspace is needed, or the user asks to populate agents, reports, dashboards, evals, automations, connectors, or monitoring for product storytelling.
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