Notre avis
Cette compétence effectue des recherches web via l'API Exa directement avec curl, sans utiliser de tokens Claude.
Points forts
- Économise les tokens Claude en utilisant une API externe.
- Produit des fichiers de recherche structurés (brouillon et final).
- Pose des questions de clarification pour affiner la recherche.
- Inclut les sources et les liens dans le rapport final.
Limites
- Nécessite une clé API Exa et curl installé.
- La qualité des résultats dépend d'Exa.
- Pas de mises à jour en temps réel après la création du fichier.
Utilisez cette compétence lorsque vous avez besoin d'une recherche approfondie sans consommer de tokens Claude Code.
Évitez-la si l'API Exa est indisponible ou si le sujet nécessite des interactions en temps réel.
Analyse de sécurité
PrudenceThe skill instructs running curl with an API key loaded from an encrypted file using sops, which is a legitimate network call for research purposes. There is no destructive action or obfuscation, but the use of Bash and network access to an external service introduces moderate risk if the AI's behavior is altered by malicious input.
- •Uses Bash and curl to call external API (exa.ai) with a secret API key decrypted from an encrypted file, which could be misused for data exfiltration if prompt injection overwrites the query.
Exemples
/freesearch AI coding tools with persistent memoryFree research on quantum computing breakthroughs 2025Research climate change impacts on agriculture without burning tokensname: freesearch description: TRULY FREE research via Exa API - zero Claude tokens. Uses Exa directly, no Gemini CLI wrapper. homepage: https://github.com/Khamel83/oneshot allowed-tools: Read, Write, Edit, Bash metadata: {"oneshot":{"emoji":"🆓","requires":{"bins":["curl"]}}}
/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:
- Ask 2-3 clarifying questions (goal, depth, audience)
- Create
docs/research/{date}_{topic}_in_progress.md - Search Exa API:
- web search for overview
- code search for technical details
- company/person search if relevant
- Update in-progress file with raw results
- Create
docs/research/{date}_{topic}_final.mdwith:- Executive summary
- Key findings
- Sources with links
- Related topics
- 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:
/browsecommand - Visual skill discovery with fuzzy searchbd testframework - Skill testing framework- Diff preview - Show changes before applying
- Skill analytics - Track most-used skills
Example Usage
User says: /freesearch AI coding tools with persistent memory
You do:
-
Ask clarifying questions:
- "What's your goal?"
- "How deep should I go?"
-
Create in-progress file with initial query
-
Search Exa API:
web_search_exa: "AI coding tools persistent memory cross-session"get_code_context_exa: "AI coding task orchestration memory"
-
Update in-progress file with findings
-
Create final report with executive summary
-
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
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