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Agent skill

Oma Search

by first-fluke in first-fluke/oh-my-agent

Intent-based search router with trust scoring. An agent skill from first-fluke/oh-my-agent.

MITAuto-check passedProductivity & Automation

Install Oma Search

skills CLI
$ npx skills add first-fluke/oh-my-agent --skill oma-search -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install first-fluke/oh-my-agent oma-search --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/first-fluke/oh-my-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/benchmarks/runs/oma/.agents/skills/oma-search .claude/skills/oma-search && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
oma-search
GitHub stars
1.3k
Token cost
~2k tokens
SKILL.md length
862 words
Files
7
Skills in repo
57
Repo updated
First seen
Licence
MIT

At a glance

Intent-based search router with trust scoring. An agent skill from first-fluke/oh-my-agent.

  • Works in 3 steps: Parse the query and flags. → Classify the search intent. → Select one best route unless ambiguity…
  • Tasks that involve Codebase onboarding
  • SKILL.md covers Scheduling, Structural Flow, Logical Operations and References
  • Calls gh and glab

What it does

Oma Search is an agent skill from first-fluke/oh-my-agent. Intent-based search router with trust scoring. Routes queries to optimal channels (Context7 docs, native web search, gh/glab code search, Serena local) and attaches domain trust labels. Use for search, find, lookup, reference, docs, code search, and web research.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files (for example `resources/checklist.md`, `resources/error-playbook.md` and `resources/examples.md`).

It sits in Productivity & Automation, covering Codebase onboarding and Web search. It works with GitLab. The repository describes itself as: Mechanical verification for AI coding agents — skills pack or full harness (stop-hook gates, artifact checks, independent judges). The licence is MIT.

When your agent uses it

  • Tasks that involve Codebase onboarding
  • Tasks that involve Web search

Example prompts

  • “/oma-search”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

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

What it can do on your machine

Read from SKILL.md and the folder at commit 268bb4a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh
    • glab

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gh and glab, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Oma Search loads about 2k tokens when it runs. Until then it costs about 69 tokens; SKILL.md has 862 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from first-fluke/oh-my-agent at commit 268bb4a, republished under its MIT licence (© first-fluke). 862 words, ~1,951 tokens.

Download SKILL.mdSave it as .claude/skills/oma-search/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
oma-search
description
Intent-based search router with trust scoring. Routes queries to optimal channels (Context7 docs, native web search, gh/glab code search, Serena local) and attaches domain trust labels. Use for search, find, lookup, reference, docs, code search, and web research.

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
ActionSSL primitiveEvidence
Parse query and flagsREADUser request
Classify intentSELECTIntent rules
Dispatch search routeCALL_TOOLDocs, web, code, local tools
Collect resultsREADSearch outputs
Score trustVALIDATETrust registry
Rank and formatINFERRelevance and trust
Report resultsNOTIFYFinal answer
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
bash
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
ScopeResource target
NETWORKWeb/docs/source-code search targets
CODEBASELocal files when local search is selected
PROCESSgh, glab, and CLI search commands
MEMORYQuery classification, trust labels, selected results
Show full SKILL.md (344 more words)Show less
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
RoutePrimary ToolFallbackTrigger
docsContext7 MCP (resolve-library-id → query-docs)web routeOfficial docs, API reference
webRuntime native searchoma search fetch (api/probe/impersonate/browser)Tutorials, examples, solutions
codegh search code / glab api—Implementation patterns, repos
localSerena MCP (delegate)—Current project files, symbols
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

© first-fluke, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files in benchmarks/runs/oma/.agents/skills/oma-search of first-fluke/oh-my-agent.

  • SKILL.md
  • resources/checklist.md
  • resources/error-playbook.md
  • resources/examples.md
  • resources/execution-protocol.md
  • resources/intent-rules.md
  • resources/trust-registry.md

Open the folder on GitHubat commit 268bb4a

Compare with similar skills

Oma Search next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Oma Search compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Oma Search this skillfirst-fluke/oh-my-agent1.3k—~2kAutomated safety check: PassMIT
Drupalorg Issue Searchmglaman/drupalorg-cli169—~956Automated safety check: PassNone
Exa Researchaiskillstore/marketplace433—~1.8kAutomated safety check: PassNone
Mysearchskernelx/MySearch-Proxy159—~1.4kAutomated safety check: NotesMIT
Z.AI CLInumman-ali/zai-cli110—~528Automated safety check: PassMIT
A Stock Predictiondigoal/blog8.6k—~2.2kAutomated safety check: PassGPL-2.0

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Works with

Categories

Questions about Oma Search

What does Oma Search do?

Intent-based search router with trust scoring. An agent skill from first-fluke/oh-my-agent. Oma Search is an agent skill from first-fluke/oh-my-agent. Intent-based search router with trust scoring.

When should I use Oma Search?

Oma Search fits situations like: tasks that involve Codebase onboarding; tasks that involve Web search.

How do I install Oma Search in Claude Code?

Run `npx skills add first-fluke/oh-my-agent --skill oma-search -a claude-code`. Or copy the skill folder (benchmarks/runs/oma/.agents/skills/oma-search in first-fluke/oh-my-agent) into .claude/skills/oma-search in your project. Claude Code loads it when a task matches its description.

How do I install Oma Search in Codex?

Run `npx skills add first-fluke/oh-my-agent --skill oma-search -a codex`. Or copy the skill folder (benchmarks/runs/oma/.agents/skills/oma-search in first-fluke/oh-my-agent) into .agents/skills/oma-search in your project. Codex loads it when a task matches its description.

Can I use Oma Search in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add first-fluke/oh-my-agent --skill oma-search -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/oma-search, .gemini/skills/oma-search, .github/skills/oma-search and .opencode/skills/oma-search in your project.

What does Oma Search need to run?

Going by SKILL.md and its folder, Oma Search needs the command-line tools its instructions call (gh and glab).

Does Oma Search access the network?

SKILL.md contains no URLs. Its commands use gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Oma Search safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Oma Search use?

Oma Search is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Oma Search use?

About 2k tokens (SKILL.md is roughly 7.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Oma Search?

Skills that share tags, products or a category with Oma Search: Drupalorg Issue Search (mglaman/drupalorg-cli, 169 stars), Exa Research (aiskillstore/marketplace, 433 stars), Mysearch (skernelx/MySearch-Proxy, 159 stars) and Z.AI CLI (numman-ali/zai-cli, 110 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Oma Search?

first-fluke (a GitHub organization) maintains it in first-fluke/oh-my-agent, which has 1,336 GitHub stars. The repository holds 57 skills in this directory. The repository was last updated on October 10, 2026.

Source: first-fluke/oh-my-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.