Freesearch

/freesearch - TRULY FREE Research

Uses 0 Claude Code tokens. Calls Exa API directly via curl.

When To Use

User says:

• /freesearch [topic] - Slash command
• "Free research on [topic]"
• "Research without burning tokens"

How It Works

Direct API calls via curl:

1. Ask 2-3 clarifying questions (goal, depth, audience)
2. Create docs/research/{date}_{topic}_in_progress.md
3. Search Exa API:
   • web search for overview
   • code search for technical details
   • company/person search if relevant
4. Update in-progress file with raw results
5. Create docs/research/{date}_{topic}_final.md with:
   • Executive summary
   • Key findings
   • Sources with links
   • Related topics
6. Return file path to Claude for reading

Exa API Configuration

The EXA_API_KEY is loaded from encrypted secrets:

# Decrypt and load key (done via skill wrapper)
EXA_KEY=$(sops --decrypt --output-type json ~/github/oneshot/secrets/research_keys.json.encrypted | grep -o '"EXA_API_KEY": "[^"]*"' | cut -d'"' -f4)

API Endpoint: https://api.exa.ai/search

Research Prompt Template

curl -s -X POST 'https://api.exa.ai/search' \
  -H "x-api-key: $EXA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "[TOPIC]",
    "type": "auto",
    "numResults": 10,
    "contents": {
      "text": {
        "maxCharacters": 20000
      }
    }
  }'

Output Locations

Project research files:

docs/research/{YYYY-MM-DD}_{topic_slug}_in_progress.md
docs/research/{YYYY-MM-DD}_{topic_slug}_final.md

Historical research:

research/{topic_slug}/research.md

Quick Wins to Implement

Based on competitor research, these features should be added to ONE_SHOT:

1. /browse command - Visual skill discovery with fuzzy search
2. bd test framework - Skill testing framework
3. Diff preview - Show changes before applying
4. Skill analytics - Track most-used skills

Example Usage

User says: /freesearch AI coding tools with persistent memory

You do:

1. Ask clarifying questions:

   • "What's your goal?"
   • "How deep should I go?"

2. Create in-progress file with initial query

3. Search Exa API:

   • web_search_exa: "AI coding tools persistent memory cross-session"
   • get_code_context_exa: "AI coding task orchestration memory"

4. Update in-progress file with findings

5. Create final report with executive summary

6. Return:

   Key findings:
   - MCP Task Orchestrator, Cipher, Pieces AI Memory, Cursor Rules
   - All use RAG + vector stores for persistent context
   
   📄 Full research: docs/research/2025-01-31_ai_persistent_memory_final.md
   

File Format Template

In-Progress Template

# Research: {Topic}

**Started:** {timestamp}
**Status:** In Progress

## Search Queries Used
- {query1}
- {query2}

## Raw Results

### Source 1
- **URL:** {url}
- **Title:** {title}
- **Snippet:** {content}

### Source 2
- **URL:** {url}
- **Title:** {title}
- **Snippet:** {content}

## Initial Notes
{ongoing analysis}

Final Template

# Research: {Topic}

**Completed:** {timestamp}
**Duration:** {duration}

## Executive Summary
{2-3 sentence overview}

## Key Findings
1. {finding with citation}
2. {finding with citation}

## Sources
1. [{Title}]({url}) - {description}
2. [{Title}]({url}) - {description}

## Related Topics
- {topic for further research}

## Full Details
{detailed analysis}

---

📄 **In-progress research:** docs/research/{date}_{topic}_in_progress.md

Why This Exists

The deep-research skill wraps Gemini CLI in a Claude sub-agent, which still burns tokens. This skill calls Exa API directly via curl:

• ✅ 0 Claude Code tokens for research
• ✅ Only main conversation tokens (clarifying questions, summary)
• ✅ Same quality research (Exa's neural search)
• ✅ Saves to project docs/research/ (tracked in git)

Tips

• Research takes 10-30 seconds (Exa is fast)
• Always save to docs/research/ NOT ~/github/oneshot/research/
• Include user's goal in the prompt for better results
• Link in-progress file in final report for drill-down

Keywords

free research, exa api, zero tokens, web search, research save