Oma Search

Search Agent - Intent-Based Search Router

Scheduling

Goal

Classify information-seeking requests, route them to the best search channel, attach trust labels, and return source-grounded results.

Intent signature

• User asks to search, find, look up, reference docs, inspect official documentation, search GitHub/GitLab code, or gather web research.
• Another skill needs reusable search infrastructure with trust scoring.

When to use

• Finding official library/framework documentation
• Web research for tutorials, examples, comparisons, and solutions
• Searching GitHub/GitLab code for implementation patterns
• Any query where the search channel is unclear (auto-routing)
• Other skills needing search infrastructure (shared invocation)

When NOT to use

• Local codebase exploration only -> use Serena MCP directly
• Git history or blame analysis -> use SCM Agent
• Full architecture research -> use Architecture Agent (may invoke this skill internally)

Expected inputs

• Query string, intent hint, or explicit flags such as --docs, --code, --web, --strict, --wide, --gitlab
• Optional required source type, recency, domain, or trust constraints

Expected outputs

• Ranked search results with route, source, trust label, and concise relevance summary
• Fallback explanation when primary route fails
• Source links or references suitable for the calling skill

Dependencies

• Context7 MCP for docs, runtime-native web search, gh/glab for code, Serena for local search
• resources/intent-rules.md, resources/trust-registry.md, execution protocol, examples, and checklist

Control-flow features

• Branches by classified intent, user flags, route success/failure, and trust constraints
• May call web/docs/code/local tools
• Scores domains at domain level only

Structural Flow

Entry

1. Parse the query and flags.
2. Classify the search intent.
3. Select one best route unless ambiguity or flags justify more.

Scenes

1. PREPARE: Parse query and classify route.
2. ACT: Dispatch to docs, web, code, or local search.
3. ACQUIRE: Collect search results and source metadata.
4. VERIFY: Apply trust scoring and route-specific quality checks.
5. FINALIZE: Present ranked results or fallback status.

Transitions

• If --docs, --code, --web, --strict, --wide, or --gitlab is provided, flags override classifier.
• If docs route fails, fall back to web.
• If web search needs fetch escalation, use oma search fetch strategies.
• If query is purely local, use Serena MCP instead of web.

Failure and recovery

• If primary route fails, fall forward to the next appropriate route.
• If trust score is weak, label it instead of hiding uncertainty.
• If no reliable results exist, report that and suggest a narrower query.

Exit

• Success: results are routed, trust-scored, and source-grounded.
• Partial success: route failures or trust limitations are explicit.

Logical Operations

Actions

Tools and instruments

• Context7 docs tools
• Runtime-native web search
• gh search code or glab api
• Serena MCP for local project search

Canonical command path

gh search code "<query>"
glab api "/search?scope=blobs&search=<query>"

For docs and web routes, use the runtime's available official-docs or web-search tools after classifying intent; do not duplicate routes unless the intent is ambiguous.

Resource scope

Preconditions

• Query and route constraints are clear enough to classify.
• Required search tools are available or fallback is possible.

Effects and side effects

• Performs external searches or local code searches.
• Produces ranked references that may influence downstream implementation or research.

Guardrails

1. Classify intent before searching: every query goes through IntentClassifier first
2. One query, one best route: avoid redundant multi-route unless intent is ambiguous
3. Trust score every result: all non-local results get domain trust labels from the registry
4. Flags override classifier: user-provided flags (--docs, --code, --web, --strict, --wide, --gitlab) always take precedence
5. Fail forward: if primary route fails, fall back gracefully (docs->web, web->oma search fetch strategies)
6. No additional MCP required: Context7 for docs, runtime native for web, CLI for code, Serena for local
7. Vendor-agnostic web search: use whatever the current runtime provides (WebSearch, Google, Bing)
8. Domain-level trust only: do not attempt sub-path or page-level scoring

Routes

Default Workflow

1. Parse: Extract query, detect flags, classify intent
2. Route: Dispatch to the appropriate search channel(s)
3. Collect: Gather results from dispatched routes
4. Score: Attach trust labels to each result domain
5. Present: Format and rank results for the user

Invocation

Standalone

/oma-search "React Server Components streaming"
/oma-search --docs "Next.js middleware"
/oma-search --code "PKCE implementation"
/oma-search --strict "JWT refresh token rotation"

Shared Infrastructure (from other skills)

Other skills reference oma-search by specifying intent and query:

1. State intent: docs | web | code | local
2. Pass query string
3. Use Trust Score in results to weigh source reliability

References

Follow resources/execution-protocol.md step by step. See resources/examples.md for input/output examples. Use resources/intent-rules.md for intent classification reference. Use resources/trust-registry.md for domain trust scoring reference. Before submitting, run resources/checklist.md. Vendor-specific execution protocols are injected automatically by oma agent:spawn. Source files live under ../_shared/runtime/execution-protocols/{vendor}.md.

• Execution steps: resources/execution-protocol.md
• Intent classification: resources/intent-rules.md
• Trust registry: resources/trust-registry.md
• Examples: resources/examples.md
• Checklist: resources/checklist.md
• Error recovery: resources/error-playbook.md
• Context loading: ../_shared/core/context-loading.md
• Context budget: ../_shared/core/context-budget.md
• Lessons learned: ../_shared/core/lessons-learned.md