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Oma Search

OrganizationPopular
first-fluke
oma-search

Find external documentation, web sources, or remote code with citations. Local code navigation uses the configured code tools.

Overview

Publisherfirst-fluke
Repositoryoh-my-agent
Skill nameoma-search
Stars
1.3K
Forks
149
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by first-fluke on GitHub. Read the source before you install it.

Installation

Install the Oma Search AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/first-fluke/oh-my-agent.git /tmp/oh-my-agent
mkdir -p .claude/skills
cp -r /tmp/oh-my-agent/skills/oma-search .claude/skills/oma-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oma Search in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Oma Search on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Oma Search is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Search Router

Scheduling

Goal

Route an information request to the appropriate channel and return relevant sources with trust labels.

Intent signature

  • Search, find, look up, reference docs, inspect official APIs, or search remote code.
  • Another skill requests docs, web, code, or local search with a query and constraints.

When to use

  • Library documentation, web research, GitHub/GitLab implementation patterns, or unclear search channels.

When NOT to use

  • Pure local code exploration -> use configured code intelligence under the shared contract.
  • Git history/blame -> oma-scm; architecture analysis -> oma-architecture.

Expected inputs

Query, optional route hint (docs, web, code, local), source/recency constraints, and flags. Explicit --docs, --code, or --web selects the route; --strict filters trust, --wide retains labeled results, and --gitlab selects the remote code host.

Expected outputs

Ranked sources with URL or file reference, route, relevance, trust level/score, and material fallback limits. Do not invent sources when no reliable result is found.

Dependencies

Context7 for docs, runtime web search, oma search/gh/glab for remote code, and configured code intelligence or native local search.

Structural Flow

Routes

RoutePrimaryFallbackUse for
docsContext7 resolve-library-idquery-docsWeb searchOfficial API/framework documentation
webRuntime web searchoma search fetch <url> for known result URLsWeb sources
codeoma search codeReport unavailable host/authRemote repository patterns
localConfigured code-intelligence providerNative search and scoped readsCurrent project files and symbols

Failure and recovery

  • Missing/empty documentation lookup: use web search and state the fallback.
  • A known URL cannot be fetched: use the fetch strategies in the route reference; retain authentication/blocking errors if all strategies fail.
  • Configured local tools unavailable or timed out: follow ../_shared/core/code-intelligence.md.
  • Unknown trust: retain the result as unknown with score ; do not imply verification.
  • No result satisfies --strict: report that outcome and offer a wider or narrower query.

Exit

Return evidence-backed results and disclose failed routes or source limitations. A domain score alone does not verify an individual claim.

Logical Operations

Canonical workflow path

  1. Parse the query and explicit flags. Use resources/intent-rules.md only when classification is needed; select one route unless ambiguity requires more.
  2. Dispatch using Routes. Load resources/execution-protocol.md for the selected channel's command flags, fetch strategies, or normalization details.
  3. Collect source references and deduplicate by URL. For non-local results, resolve domain trust using resources/trust-registry.md; reuse the current session cache. Apply --strict after scoring.
  4. Rank by relevance, using trust as a tiebreaker. Return the requested answer or source list with supporting references and fallback limitations.

CLI entry points

bash
oma search code "<query>" [--host gitlab] [--language <lang>] [--repo <owner/repo>]
oma search trust <domain>
oma search fetch <url>

The CLI also exposes api, api:search, meta, rss, rss:google, media, archive, and doctor; use a primitive only when the selected route needs it.

Guardrails

  • Explicit route/source constraints take precedence over automatic classification.
  • Score at domain level, not URL-path or page level. Use the CLI registry and the documented Context7/official-site exceptions; do not invent scores.
  • Do not duplicate successful routes or search locally via the web.
  • Use available runtime web tools and the configured local provider; do not install or track a repository automatically.

Resource scope and effects

Search may contact external services or inspect local code and spawn gh/glab processes. Query constraints, selected sources, and trust metadata are session context; persistent caches belong in generated state, not skill definitions.

References

  • Intent classifier: resources/intent-rules.md (no explicit route or ambiguous query)
  • Route execution detail: resources/execution-protocol.md (selected channel only)
  • Trust resolution and cache rules: resources/trust-registry.md (non-local sources)
  • Recovery detail: resources/error-playbook.md (route failures)
  • Result checklist: resources/checklist.md (applicable route checks)
  • Examples: resources/examples.md (unfamiliar input/output contracts)
  • Local code-intelligence contract: ../_shared/core/code-intelligence.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Oma Search AI skill do?

Find external documentation, web sources, or remote code with citations. Local code navigation uses the configured code tools.

Why use Oma Search on TypingMind?

Because you install it once and use it with any model. Oma Search is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Oma Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/first-fluke/oh-my-agent/tree/main/skills/oma-search. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Oma Search?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Oma Search?

As many as you like. As long as a model supports skills, you can use Oma Search with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Oma Search AI skill free?

Yes. It is published on GitHub by first-fluke under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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