vs-item-onboarding

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General item-level onboarding: understand structured item data, generate schema and config plans, create datasets, and create or bind apps only when the user explicitly asks for app-level setup.

volcengine By volcengine schedule Updated 6/8/2026
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name: vs-item-onboarding description: "General item-level onboarding: understand structured item data, generate schema and config plans, create datasets, and create or bind apps only when the user explicitly asks for app-level setup." category: workflow applies_to: codex, agents, external-agent requires_cli: ">=0.1.0" keywords: item search onboarding, structured data onboarding, dataset-only onboarding, schema design, field config, online config, validation, recommend bootstrap, item profile, item plan, item apply commands: item profile, item plan, item apply, dataset create, dataset ingest, dataset schema check, app diagnose, search run, chat run, recommend scene create

Viking Item Onboarding

When to Use

Use this skill when a user provides structured item data and expects the agent to understand the data, design the schema, and either provision a dataset only or continue to app-level search onboarding. Understand the business goal and the requested delivery boundary first, then use item profile / plan / apply plus lower-level dataset commands to standardize the high-risk execution steps.

Preconditions

  • vs CLI and Viking skills are installed
  • authentication is complete; at least vs auth status and vs doctor succeed
  • input file is preferably JSON array, JSONL, or CSV (convert binary spreadsheets first)
  • the user has stated a business goal such as "Build catalog search" or "Build content search"

High-level Flow

For dataset-only, there is exactly one valid schema-level user confirmation for the current draft: Stage A. That dialog must happen only after the full schema context has been rendered.

  1. item profile --file <data> --type <item|video> — first-pass profiling and validation risk scan
  2. Confirm the requested provisioning mode: dataset-only or dataset+app; if the user did not ask for app creation, default to dataset-only
  3. item plan --file <data> --type <item|video> --goal "<goal>" --schema-source console — generate plan directory through the stable schema workflow: the CLI handles the internal upload/signature and schema-inference steps, then continues plan generation. Add --skip-app when the requested mode is dataset-only. If execution later goes through item provision or item apply, those commands also accept --skip-app as an execution-time guard rail.
  4. Stage A — render the schema header and full schema table, verify row count, then ask exactly one dialog question (see agent-confirmation-ux.md §A)
  5. If the requested mode is dataset-only, run dataset create + dataset ingest immediately after a valid Stage A answer and stop after dataset provisioning succeeds; do not issue another schema-level confirmation. Prefer creating the dataset from the full dataset-create.json payload so Schema and DataFieldConfig.FieldDescMap are submitted together. For --type video, this full-payload path is mandatory; do not use schema.json alone
  6. If the requested mode is dataset+app, run Stage B — bind-time field-config review (table per group + dialog, see agent-confirmation-ux.md §B); for --type video apply video-field-constraints.md first
  7. item apply --plan-dir <dir> --confirm-review — stage-one execution for the dataset+app branch
  8. Optional: --run-trials, recommend bootstrap, app diagnose for failures. For any failure, see references/recovery.md — do NOT blindly retry.

Full step-by-step workflow, dataset-type selection rules, examples, and extended guidance live in workflow.md.

Commands

See the commands: frontmatter above for the exhaustive list; the primary entry points are item profile, item plan, dataset create, dataset ingest, and item apply --plan-dir <dir> --confirm-review. Detailed command forms, flags, dataset-only branching, and dataset-type selection rules live in workflow.md.

Workflow

The high-level 8-step flow is listed above under High-level Flow. The full branch-aware workflow — including dataset-only vs. dataset+app routing, profiling, plan review, Stage A, conditional Stage B, dataset provisioning, optional app provisioning, optional --run-trials, and recommend bootstrap — with per-step cross-references to the Hard Rules below lives in workflow.md.

Customer Environment Principle

  • In customer environments, assume repository source code is unavailable.
  • Execute tasks using only the installed skills, the packaged vs CLI surface (--help, command output, and observed runtime behavior), and explicit user-provided information.
  • Do not rely on reading local repository source files, generated repo snapshots, or implementation details to decide runtime actions.
  • If the installed CLI behavior conflicts with a skill, trust the installed CLI behavior first.
  • If the skills and the packaged CLI still do not provide enough information to proceed safely, stop and ask the user instead of searching source code.

Constraints

The enforceable rules are organized in two layers:

  • Before executing any concrete vs ... command in this onboarding workflow, first consult vs-product-qa to verify the current command surface, required flags, payload fields, input format, and allowed values. Only after that check may you finalize parameters and run the command.
  • Hard Rules (MUST / MUST NOT) — listed below; violating any one stops the workflow.
  • Pre-apply Checklist — listed below; a mechanical gate that the agent MUST verify before running vs item apply --plan-dir <dir> --confirm-review.
  • Additional MUST / Guidance — lower-priority MUST rules and quality guidance live in workflow.md under Extended Constraints.

Hard Rules (MUST / MUST NOT)

These rules prevent server-side failures and silent data loss. Violating any rule stops the workflow.

  1. Dataset type is explicit. MUST pass --type <item|video> on both profile and plan. MUST NOT infer dataset type from --goal. If data looks video-like but the user did not specify, MUST ask before continuing.
  2. App creation is explicit. MUST ask or infer from an explicit user request whether the target is dataset-only or dataset+app. If the user did not ask for app creation, bind-time config, search/chat verification, or app-level setup, MUST default to dataset-only. MUST NOT create or bind an app on the user's behalf.
  3. Stage A gates every provisioning path. MUST run Stage A (schema confirmation) before dataset create, dataset ingest, item apply, or any app-level bind. MUST NOT treat first-pass plan generation as schema approval.
  4. Stage A lists every field and every relevant schema attribute. MUST render a per-field table (name, type, attributes, meaning) covering every field in schema.json; MUST NOT summarize only key fields. For --type video, MUST also surface the status of the semantic slots content_id, content_type, video_url, parent_content_id, and sequence_index before approval.
  5. Stage A dialog comes last and only once per unchanged draft. MUST NOT issue the Stage A dialog before the header block, the full schema table, and the row-count self-check are complete. If a dialog is issued earlier, MUST discard it and restart Stage A instead of asking a second schema-level confirmation. For dataset-only, once a valid Stage A dialog answer is captured for the current schema draft, MUST NOT ask another schema-level confirmation unless the schema changes.
  6. Stage B is conditional and app-only. MUST run Stage B (bind-time field-config confirmation) only when the requested outcome includes application creation or dataset binding. MUST NOT run bind-time confirmation for a dataset-only request, and MUST NOT skip Stage B when app binding will happen.
  7. Plan artifacts are drafts, not confirmed bind config. item plan may emit draft dataset-side config artifacts such as field-config.json or dataset-create.json with DataFieldConfig. Treat them as execution inputs, not as user-confirmed bind config. For dataset-only, dataset creation SHOULD prefer the full dataset-create.json payload so Schema and DataFieldConfig.FieldDescMap are submitted together. For dataset-only + --type video, dataset creation MUST use a full payload that includes DataFieldConfig; using only --schema @schema.json can fail with MissingParameter.DefaultFieldStrategy. For dataset-only + --type item, fall back to --schema @schema.json only when dataset-create.json is missing or clearly unsuitable for the current plan.
  8. Video DefaultFieldStrategy is mandatory for app/bind flows. For --type video, any Stage B proposal MUST satisfy every row of video-field-constraints.md. Fix violations in memory before rendering Stage B; MUST NOT write a violating proposal to field-config.json or review-confirmation.json.
  9. Real bind is real. After Stage B confirmation, MUST run vs item apply --plan-dir <dir> --confirm-review without --dry-run and MUST NOT degrade into OnlySave=true semantics.
  10. Stop at the requested boundary. For dataset-only, stage one ends after dataset creation and ingest succeed. For dataset+app, stage one ends after dataset creation, ingest, app creation, and bind succeed. MUST NOT continue beyond the requested boundary unless the user explicitly asks.
  11. Recommend bootstrap needs explicit input. MUST NOT auto-create or update recommend scenes without both --recommend-bhv-scene-types and --confirm-recommend-entry-binding.

Pre-apply Checklist

Before provisioning anything, the agent MUST first decide whether the requested path is dataset-only or dataset+app. Use the checklist below only for the dataset+app path. If the request is dataset-only, stop after Stage A and dataset provisioning; do not run Stage B or item apply.

  1. Plan directory resolved — <plan-dir> points to the latest item plan output for the current dataset (not a stale one from earlier in the conversation).
  2. Dataset type matches intent — plan.json.defaults.datasetType and dataset-create.json.Type both equal the requested --type (item or video).
  3. Provisioning mode confirmed — the user explicitly asked for app-level setup, or explicitly agreed to continue from dataset-only into dataset+app. If not, do not run app-level provisioning.
  4. Stage A done — the header block was rendered, a per-field schema table was rendered (row count == len(schema.json.Fields)), and the user answered one valid interactive question dialog with a non-abort option. For --type video, the Stage A summary also surfaced the status of content_id, content_type, video_url, parent_content_id, and sequence_index.
  5. Stage B done — per-group tables for IndexFields / FilterFields / SuggestFields / ImageIndexFields / VideoIndexFields were rendered with Field / Type / Meaning / Reason to include / Risk or note, and the user answered an interactive dialog with a non-abort option.
  6. Video DefaultFieldStrategy satisfied — for --type video, the final proposal satisfies every row of video-field-constraints.md. MUST re-verify after any user adjustment in Stage B.
  7. Artifacts written — field-config.json reflects the final Stage B groups, and review-confirmation.json has status=confirmed, every requiredChecks.* true, and a fieldConfigReview block with the final groups.
  8. Validation not bypassed — validation.json has no unresolved blocking issues (or --force has been explicitly acknowledged by the user for a controlled test).
  9. Apply command is real — the command about to run is exactly vs item apply --plan-dir <dir> --confirm-review, with no --dry-run and without any flag that would degrade into OnlySave=true semantics.

If the user makes a follow-up edit (rename a field, drop a group member, switch --type, etc.) after this checklist passed, MUST re-run the affected checks before apply.

  • If provisioning failure analysis or user follow-up turns into a product concept, capability, API field, console UI path, purchase, billing, or general troubleshooting question outside this onboarding workflow, temporarily hand off to vs-product-qa; return to this workflow only after the grounded product answer is complete.

References

Install via CLI
npx skills add https://github.com/volcengine/SearchCLI --skill vs-item-onboarding
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