git:20260717.d76f74f to v1.0.0

---

name: smart-search

description: 'Intelligent hybrid search combining web and academic sources via AIsa Smart Search endpoint. Best when you need both web and scholarly results. Use when: the user needs web search, research, source discovery, or content extraction.'

compatibility: Designed for Agent Skills compatible clients such as OpenClaw, Claude Code, Hermes, and GitHub-backed skill catalogs. Requires system binaries python3, environment variables AISA_API_KEY and internet access to api.aisa.one.

metadata:

aisa:

emoji: 🔎

requires:

bins:

- python3

env:

- AISA_API_KEY

primaryEnv: AISA_API_KEY

compatibility: Designed for Agent Skills compatible clients such as OpenClaw, Claude Code, Hermes, and GitHub-backed skill catalogs. Requires system binaries python3, environment variables AISA_API_KEY and internet access to api.aisa.one.

---

# AIsa Smart Search

Intelligent hybrid search that combines web and academic sources using the AIsa Smart Search endpoint. Automatically blends general web results with scholarly articles for comprehensive coverage.

## Setup

This skill requires the `AISA_API_KEY` environment variable. When installed as a Claude plugin, the key is configured via the plugin's `userConfig`.

## Usage

Run the search client with the `smart` subcommand:

```bash

python3 ${CLAUDE_PLUGIN_ROOT}/skills/smart-search/scripts/search_client.py smart --query "<search query>" --count <max_results>

```

### Arguments

| Argument | Required | Default | Description |

|----------|----------|---------|-------------|

| `--query` / `-q` | Yes | — | Search query |

| `--count` / `-c` | No | 10 | Maximum number of results (1–100) |

### Example

```bash

python3 ${CLAUDE_PLUGIN_ROOT}/skills/smart-search/scripts/search_client.py smart --query "impact of large language models on software engineering" --count 10

```

## Output

The script prints a mixed set of results from both web and academic sources, including titles, URLs, and content snippets.

## When to Use

Use this skill when the user's query spans both general knowledge and academic research. For example, questions about emerging technologies, scientific topics with practical applications, or any query where both web articles and papers would be valuable.