Viking Search

When to Use

Use this skill for:

• search query verification
• search scene inspection and updates
• online result checks
• recall or ranking issue diagnosis
• separating persistent scene config from request-only runtime parameters
• fine-grained operations / serving-controls updates, including query-specific search rules

If the user wants automated batch evaluation or similarity tuning across many queries and strategies, use vs-search-tuning instead.

Preconditions

• an application-id is available
• if you will edit a scene, you should preferably know the scene-id
• for fresh apps, be prepared for the runtime not to be ready yet
• the agent should treat the installed CLI behavior as authoritative when help text, skill text, and runtime behavior disagree

Scope

Before changing anything, decide whether the user wants:

• a persistent search strategy change
• a temporary runtime verification
• a readiness diagnosis for a failing app

This skill stays at the search workflow level. Do not embed low-level API field mappings, payload design, or enum interpretation here. When a concrete command needs exact parameters, first consult vs-product-qa.

Commands

• search run: send a production-style search request
• search scene create / search scene list / search scene get: manage search scenes
• search scene update: update scene configuration
• app status / app diagnose: inspect readiness before blaming the query

Workflow

1. Start by determining whether the user wants:

- a persistent search strategy change - a temporary runtime verification - a readiness diagnosis for a failing app

1. If the request is a persistent search strategy change, use search scene list/get first and inspect the current scene before mutating it.
2. Before running any concrete command, consult vs-product-qa to confirm the current command behavior and the exact parameter requirements.
3. Use search run for verification requests and search scene update for persistent scene changes.
4. After every scene mutation, immediately read the scene back with search scene get and verify that the intended change is visible online.
5. If a fresh app fails, check app status and then app diagnose.
6. Only after readiness is clear should you focus on recall quality or scene configuration.
7. If the command behavior conflicts with the skill text or --help, trust the installed CLI behavior first, and only then inspect repository code when needed to explain or fix the gap.

References

• references/search-scene-natural-language-routing.md: workflow-oriented mapping from natural-language search-scene requests to the first config area or workflow you should inspect

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

• Before executing any concrete vs ... command in this search 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.
• Field name case sensitivity: All dataset field names (used in ShuffleConfig.Rules[].FieldName, ShuffleExpr.field, BoostBuryCondConfig.Rules[].Config.field, FilterConfig.Config.field, AuxiliaryPools[].Filter.field, etc.) are case-sensitive. Never infer or normalize field name casing from the user's natural-language description. Before writing any field name into a config, first look up the exact field name from the dataset schema or data-config via dataset get --id <dataset-id> --full or app dataset-config get --application-id <id> --dataset-id <id> --full, and copy the field name exactly as it appears there (case-for-case). If the field name you have doesn't match any field in the schema, stop and ask the user to confirm which field they mean instead of guessing.
• When an app is bound to exactly one dataset, the CLI can infer dataset-id
• For fresh apps, treat readiness as the first hypothesis before blaming the query
• Prefer public vs search ... commands over bypassing the CLI and calling lower-level APIs directly
• Use search scene update for persistent search behavior and do not invent low-level API mappings inside this skill
• For natural-language scene-change requests, use references/search-scene-natural-language-routing.md as the routing layer; if the target is a rule-resource workflow such as filter-item-scope, do not reduce it to a single inline scene field edit
• Do not assume --help, skill text, and the installed command implementation are perfectly aligned; verify the actual command behavior before making high-risk scene changes
• For scene updates, prefer a readback check after mutation instead of assuming the write succeeded
• If a scene update unexpectedly fails or appears to no-op, verify the accepted command behavior and parameter requirements before retrying
• If a command failure or user follow-up turns into a product concept, capability, API field, purchase, billing, or general troubleshooting question outside this search workflow, temporarily hand off to vs-product-qa; return to this workflow only after the grounded product answer is complete.