Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add x15907982411/exa-mcp-server-for-operit --skill searchgit clone --depth 1 https://github.com/x15907982411/exa-mcp-server-for-operitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/x15907982411/exa-mcp-server-for-operit/search)<a href="https://agentmods.dev/skills/x15907982411/exa-mcp-server-for-operit/search"><img src="https://agentmods.dev/badge/skills/x15907982411/exa-mcp-server-for-operit/search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/x15907982411/exa-mcp-server-for-operit/search"><img src="https://agentmods.dev/badge/skills/x15907982411/exa-mcp-server-for-operit/search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.03170 |
| Opus 5 | $0.00028 | $0.01585 |
| Sonnet 5 | $0.00011 | $0.00634 |
| Haiku 4.5 | $0.00006 | $0.00317 |
Grade A, and why
search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to search — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exa Research Orchestrator
You are the orchestrator. Your job: understand the query, plan the work, dispatch subagents with the right context, then compile and deliver the final result.
Prerequisites: Auth
Server: https://mcp.exa.ai/mcp.
- OAuth (recommended) — client opens
auth.exa.ai, user signs in with Google / SSO / email, JWT is attached automatically. No key to copy. - API key — if OAuth isn't available, get one at https://dashboard.exa.ai/api-keys and pass it via
Authorization: Bearer …,?exaApiKey=…, orEXA_API_KEY(local npm). - Anonymous — works without setup but rate-limited.
On auth / rate-limit errors, surface the fix (prefer OAuth) — don't fall back to generic web search.
Date Calculation (Do This First)
If the query involves time ("last week", "recent", "past 6 months"), calculate exact dates from today's date in your environment context. Write out the calculation explicitly before doing anything else. Never eyeball dates or reuse dates from examples.
Step 1: Assess the Query
Read the user's query and determine two things:
How complex is this?
- Extremely Simple (e.g. reading the contents of 1-2 pages): Handle it yourself. Read
references/searching.mdfor query-writing guidance, run the searches, review and filter results, then respond directly. No subagents needed. - Moderate (when a fast or low-effort search is requested): Delegate to 1 subagent to keep your context window clean.
- Advanced (clear topic, clear filters, a few parallel searches): Light subagent use. One round of parallel subagents, then compile.
- Complex (cross-referencing across entity types, multi-hop chains, exhaustive coverage, semantic filtering): Full multi-pass with parallel subagents.
Confirm when ambiguous: If the query could reasonably be handled as Extremely Simple/Moderate OR as Advanced/Complex, pause and ask the user before proceeding. Present:
- Your interpretation of the query
- The two (or more) plausible complexity levels
- What each level would look like in practice (e.g., "I can do a quick 1-2 search lookup, or I can fan out across 3-4 subagents to get deeper coverage")
- Let the user choose
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/extraction.md 3.0 KB
- references/filtering.md 2.2 KB
- references/patterns-code.md 557 B
- references/patterns-companies.md 1004 B
- references/patterns-news.md 481 B
- references/patterns-papers.md 746 B
- references/patterns-people.md 1.4 KB
- references/patterns-relationships.md 1.4 KB
- references/searching.md 4.9 KB
- references/source-quality.md 2.2 KB
- references/synthesis.md 2.2 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 208 lines · 55 tokens per session scan A 0aacd119c70b
search is a skill published in the GitHub repository x15907982411/exa-mcp-server-for-operit (0 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 3,170 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to search, differing in 0 lines, and is treated as a copy.
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