exa
High-precision semantic search and content retrieval via Exa API. Use when: (1) Deep research requiring semantic understanding, (2) Code documentation and examples lookup, (3) Company/professional research, (4) AI-powered comprehensive research tasks, (5) URL content extraction with structured output. Triggers: "research", "find papers", "code examples", "company info", "LinkedIn profiles", "deep analysis". Differentiator: Exa excels at semantic/neural search while grok-search is better for real-time news and general web content.
What this skill does
# Exa Search
High-precision semantic search via Exa API. Standalone CLI only (no MCP dependency).
## Execution Method
```bash
# Prerequisites: pip install httpx tenacity
# Environment: EXA_API_KEY (required), EXA_API_URL (optional, default: https://api.exa.ai)
# All examples assume cwd == skills/exa/. The shim auto-chdirs if you launch
# it from elsewhere (e.g., the repo root).
cd skills/exa
python scripts/exa_cli.py --help
```
## Available Tools
```bash
# Basic semantic search (highlights always on; supports inline category:<type>)
python scripts/exa_cli.py web_search_exa --query "TypeScript design patterns" [--num-results 10]
python scripts/exa_cli.py web_search_exa --query "category:company Anthropic AI safety"
# Batch URL fetch (urls is a repeatable flag; payload field is upstream `ids`)
python scripts/exa_cli.py web_fetch_exa \
--urls "https://a.com" --urls "https://b.com" \
[--max-chars 3000] [--out content.json]
# Advanced filtered search (list params are repeatable flags, no comma syntax)
python scripts/exa_cli.py web_search_advanced_exa --query "transformer" \
[--type auto|fast|instant] [--category research\ paper] \
[--include-domains arxiv.org --include-domains papers.nips.cc] \
[--exclude-domains medium.com] \
[--include-text "attention"] [--exclude-text "tutorial"] \
[--start-date 2024-01-01] [--end-date 2024-12-31] \
[--num-results 10] [--max-age-hours 168] \
[--text] [--highlights] [--summary] \
[--max-chars 5000] # only effective when --text is set; emits stderr warning otherwise
[--out results.json]
# Configuration / connectivity probe (omit --no-test to run a numResults=1 ping)
python scripts/exa_cli.py get_config_info [--no-test]
```
## Tool Capability Matrix
| Tool | Required | Optional | Output |
|------|----------|----------|--------|
| `web_search_exa` | `--query` | `--num-results` (1-100) | Search results JSON (highlights always present) |
| `web_fetch_exa` | `--urls` (repeatable, ≥1) | `--max-chars` (default 3000), `--out` | `/contents` response JSON |
| `web_search_advanced_exa` | `--query` | `--type`, `--category`, repeatable `--include-domains`/`--exclude-domains`/`--include-text`/`--exclude-text`, `--start-date`, `--end-date`, `--num-results`, `--max-age-hours`, `--text`, `--highlights`, `--summary`, `--max-chars`, `--out` | Filtered search results JSON |
| `get_config_info` | – | `--no-test` | Config + (default) `connection_test` |
## Global Options
Place before the subcommand:
| Option | Purpose |
|--------|---------|
| `--api-url` | Override `EXA_API_URL` (does not write to `os.environ`) |
| `--api-key` | Override `EXA_API_KEY` |
| `--debug` | Enable JSON debug events on stderr (`EXA_DEBUG=true`) — never logs auth values |
| `--max-retry-wait <s>` | Cap (seconds) for single retry wait + exponential backoff (default 60, env: `EXA_MAX_RETRY_WAIT`) |
| `--auth-scheme <scheme>` | Authentication scheme: `x-api-key` (default) or `bearer` for third-party endpoints (env: `EXA_AUTH_SCHEME`) |
## Tool Routing Guide
| Use Case | Recommended Tool |
|----------|------------------|
| Real-time news, current events | grok-search |
| Semantic/conceptual research | **exa** (`web_search_exa`) |
| Domain or date-bounded research | **exa** (`web_search_advanced_exa`) |
| Read full content of one or more URLs | **exa** (`web_fetch_exa`) |
| Academic papers, technical docs | **exa** (`web_search_advanced_exa --include-domains arxiv.org ...`) |
## Workflow Patterns
### Pattern 1: Quick Semantic Search
```bash
python scripts/exa_cli.py web_search_exa --query "best practices for React hooks" --num-results 5
```
### Pattern 2: Filtered Research (repeatable flags)
```bash
python scripts/exa_cli.py web_search_advanced_exa --query "transformer architecture" \
--include-domains arxiv.org --include-domains papers.nips.cc \
--start-date 2023-01-01 --text --summary
```
### Pattern 3: Batch URL Read
```bash
python scripts/exa_cli.py web_fetch_exa \
--urls "https://example.com/a" --urls "https://example.com/b" \
--max-chars 4000 --out batch.json
```
### Pattern 4: Third-Party Endpoint (Bearer Auth)
```bash
# Connect to exa-pool or other Exa-compatible proxy
export EXA_API_URL=https://pool.example.com
export EXA_AUTH_SCHEME=bearer
export EXA_API_KEY=your-bearer-token
python scripts/exa_cli.py web_search_exa --query "AI agents" --num-results 5
# Or use CLI flags for one-off requests
python scripts/exa_cli.py --auth-scheme bearer --api-url https://pool.example.com \
web_search_exa --query "AI agents"
```
## References
The `references/` directory carries 11 prompt-engineering guides. Open them on demand when crafting queries:
| File | When to read |
|------|--------------|
| `searching.md` | Crafting `web_search_exa` queries (semantic phrasing, category usage) |
| `extraction.md` | Choosing between highlights / text / summary on advanced search |
| `filtering.md` | Building include/exclude domain & date filters |
| `synthesis.md` | Aggregating multiple result sets into a coherent answer |
| `source-quality.md` | Vetting source credibility |
| `patterns-code.md` | Code/library research recipes |
| `patterns-companies.md` | Company research recipes |
| `patterns-news.md` | Current-events research recipes |
| `patterns-papers.md` | Academic paper recipes |
| `patterns-people.md` | People search recipes |
| `patterns-relationships.md` | Multi-entity relationship research |
## Error Handling
| Error | Recovery |
|-------|----------|
| `EXA_API_KEY not configured` | Set environment variable or pass `--api-key` |
| HTTP 408/429/5xx | Automatic retry with exponential backoff (max 4 attempts, capped by `--max-retry-wait`) |
| HTTP 401 | Verify API key |
| Timeout | Reduce `--num-results` or retry |
## Output Format
All commands print JSON (`ensure_ascii=False`, indent 2) to stdout. With `--out
<file>`, the response JSON is written to that path and stdout becomes
`{"status":"ok","file":"<file>"}`. Errors go to stderr as
`{"error":"<message>"}` with non-zero exit.
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.