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AIsa Multi-Source Search

Parallel multi-source search combining Web, Scholar, Smart, and Tavily results with confidence scoring and AI synthesis. Best for comprehensive research requiring cross-source validation. Use when: the user needs web search, research, source discovery, or content extraction.

Installable skillMITBy baofeng-techUpdated Oct 30, 2026
INTENDED FORClaude CodeInstall in a listed agent.

instructions.md

---
name: multi-search-d0efd3e1654e
description: 'Parallel multi-source search combining Web, Scholar, Smart, and Tavily results with confidence scoring and AI synthesis. Best for comprehensive research requiring cross-source validation. Use when: the user needs web search, research, source discovery, or content extraction.'
license: MIT
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:
  author: AIsa
  version: 1.0.0
  homepage: https://aisa.one
  repository: https://github.com/baofeng-tech/agent-skills
  tags: x,search,research
  platforms: agentskills.io,agentskill.sh,github
  primary_env: AISA_API_KEY
allowed-tools: Read Bash Grep
---

# AIsa Multi-Source Search

The most comprehensive search tool in this plugin. Queries Web, Scholar, Smart, and Tavily sources in parallel, then computes a confidence score based on source availability, result quality, and diversity. Optionally generates an AI synthesis of all results.

## Setup

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

## Usage

Run the search client with the `verity` subcommand:

```bash
python3 scripts/search_client.py verity --query "<search query>" --count <results_per_source>
```

### Arguments

| Argument | Required | Default | Description |
|----------|----------|---------|-------------|
| `--query` / `-q` | Yes | — | Search query |
| `--count` / `-c` | No | 5 | Maximum results per source (1–20) |

### Example

```bash
python3 scripts/search_client.py verity --query "impact of AI on healthcare diagnostics" --count 5
```

## Output

The script prints:

1. **Individual results** from each source (Web, Smart, Scholar, Tavily)
2. **Confidence Assessment** with:
   - **Score** (0–100) — Overall confidence in the search results
   - **Level** — Very High / High / Medium / Low / Very Low
   - **Sources queried** and **Sources OK** — How many sources responded
   - **Total results** — Combined result count across all sources
3. **AI Synthesis** — A coherent summary combining insights from all sources, with citations

### Confidence Scoring Breakdown

| Factor | Weight | Description |
|--------|--------|-------------|
| Source availability | 40% | How many of the 4 sources returned results |
| Result quality | 35% | Ratio of actual results to expected results |
| Source diversity | 15% | Whether both academic and web sources are present |
| Recency bonus | 10% | Bonus for having at least one successful source |

## When to Use

Use this skill when the user needs the most thorough and reliable search results possible. Best for fact-checking, comprehensive research, verifying claims across multiple sources, or any query where cross-source validation adds significant value. This tool is slower but more reliable than individual search tools.

Platform guidance

Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Includes source scripts; review them before use. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.