Exa native base

Skill CodeAlive-AI/exa-skills/exa-native-base

Exa web search as standalone Agent Skills — no MCP server, just an EXA_API_KEY. Company, people, papers, code, finance, personal-site & X search + lead generation for Claude Code, Codex, Cursor, OpenCode, Gemini CLI, and any SKILL.md agent.

Install

npx -y skills add CodeAlive-AI/exa-skills --skill exa-native-base

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What its author says it does

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Foundation for the Exa skill collection — how to call Exa (search, contents, answer, similar) from a local script with just an EXA_API_KEY, how to write good semantic queries, and how to orchestrate subagents for deep research. Read this first; the use-case skills (company, people, papers, code, news, finance, personal sites, X) build on it.

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SKILL.md

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Exa Native Base

The shared foundation for every Exa skill in this collection. Exa is a semantic search engine (vector embeddings, not keywords) plus content-fetch and answer APIs. These skills call the Exa REST API directly through a local Python script — no MCP server, no SDK install, only an API key.

Setup (once)

export EXA_API_KEY=your-key            # or create a .env at the repo root: EXA_API_KEY=...

Get a key at https://dashboard.exa.ai/api-keys. The client (_shared/exa_client.py) auto-loads a .env found by walking up from the script, so any skill works from any directory once the key is set. Python 3.8+, standard library only.

The client

One zero-dependency CLI backs all skills: _shared/exa_client.py (also exposed here as scripts/exa.py).

python scripts/exa.py search "<query>" [-n N] [-c CATEGORY] [-t TYPE] [--text] [--json] \
                                       [--include-domains a.com,b.com] [--only-domains a.com] \
                                       [--start-published 2025-01-01] [--include-text "phrase"]
python scripts/exa.py contents <url> [<url> ...] [--text] [--summary]
python scripts/exa.py answer  "<question>" [--text]
python scripts/exa.py similar <url> [-n N]

Every search prints a Markdown list and a trailing sources_reviewed: N line so an orchestrator can tally coverage.

Writing good queries (read this)

Describe the page you want to find, not the fact you want to know. Exa returns nearest neighbours in embedding space; it does not do keyword matching, boolean logic, or quoted exact-match.

Looking for	Weak query	Strong query
Blog posts about X	`X`	`detailed blog post about X written by a practitioner`
A company doing Y	`Y company`	`category:company startup building Y for enterprise`
A person	`person at company`	`category:people senior infra engineer at Acme`

Categories focus the index. Pass with -c: company, research paper, news, pdf, github, personal site, linkedin profile, financial report. You can also write category:company … inline in the query. (The old tweet category was retired — exa-x-search handles X via domain filters.)
numResults sizing: named entity → ~5; precise filter → ~10; broad discovery → ~15. Never go above 25 — run more queries at different angles instead of one huge query.
Query diversity: vary the angle (skeptic vs builder vs practitioner), not just synonyms. Word order shifts embeddings, so 2-3 phrasings in parallel widen coverage.
Dates: compute exact dates from today's date first, then either encode them in the query ("published in March 2026") or use --start-published / --end-published. Never reuse dates from examples.
type: auto (default) is fine for almost everything; fast for latency; deep / deep-reasoning for hard multi-hop questions.

Token isolation & orchestration (for deep research)

Never dump bulk search output into your main context. When a task needs more than 1-2 searches:

Plan the sub-questions / workstreams (e.g. competitors, funding, hiring).
Dispatch one subagent per workstream (use the Agent tool; model: haiku is enough). Tell each: which skill script to run, which queries, what to return. Aim for 3-5 searches per subagent; launch them in parallel.
Compile: merge results, drop exact-URL duplicates, merge same-entity rows, note "deduplicated X → Y".
Validate coverage and fill gaps with targeted follow-ups.
Deliver a tight, well-formatted answer (prefer tables; hyperlink sources; lead with the answer).

Treat Exa results as similarity, not validation — always review and filter before reporting.

Reference files (read on demand)

File	When
`references/searching.md`	Full query-writing guide and category index
`references/filtering.md`	Apply hard + soft filters to results
`references/extraction.md`	Pull structured fields into a schema
`references/synthesis.md`	Write a narrative answer with citations
`references/source-quality.md`	Judge source credibility (best-of / expert queries)
`references/python-sdk-spec.md`	Full Exa API surface (search/contents/answer/research, all params)

The collection

Skill	Use for
`exa-web-search`	General web search — the `search_web` analog; start here for open-ended lookups
`exa-company-research`	Companies, competitors, funding, market maps
`exa-people-research`	People by role/company/location (LinkedIn-weighted)
`exa-lead-generation`	Prospect lists: companies → decision-makers
`exa-research-paper-search`	Academic papers, surveys, preprints
`exa-code-context`	Code, APIs, docs, GitHub repos, error fixes
`exa-financial-report-search`	SEC filings, earnings, annual reports
`exa-personal-site-search`	Personal blogs, portfolios, homepages
`exa-x-search`	X/Twitter posts (limited — see that skill's notes)

Query patterns and research guidance adapted from Exa Labs' open-source search skill (MIT). This collection reimplements them as standalone, script-based skills that need no MCP server.