data-scraper-agent

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• SKILL.md

SKILL.md (first lines)

# Data Scraper Agent

Build a production-ready, AI-powered data collection agent for any public data source.
Runs on a schedule, enriches results with a free LLM, stores to a database, and improves over time.

**Stack: Python · Gemini Flash (free) · GitHub Actions (free) · Notion / Sheets / Supabase**

## When to Activate

- User wants to scrape or monitor any public website or API
- User says "build a bot that checks...", "monitor X for me", "collect data from..."
- User wants to track jobs, prices, news, repos, sports scores, events, listings
- User asks how to automate data collection without paying for hosting
- User wants an agent that gets smarter over time based on their decisions

## Core Concepts

### The Three Layers

Every data scraper agent has three layers:

```
COLLECT → ENRICH → STORE
  │           │        │
Scraper    AI (LLM)  Database
runs on    scores/   Notion /
schedule   summarises Sheets /
           & classifies Supabase
```

### Free Stack

| Layer | Tool | Why |
|---|---|---|
| **Scraping** | `requests` + `BeautifulSoup` | No cost, covers 80% of public sites |
| **JS-rendered sites** | `playwright` (free) | When HTML scraping fails |
| **AI enrichment** | Gemini Flash via REST API | 500 req/day, 1M tokens/day — free |
| **Storage** | Notion API | Free tier, great UI for review |
| **Schedule** | GitHub Actions cron | Free for public repos |
| **Learning** | JSON feedback file in repo | Zero infra, persists in git |

### AI Model Fallback Chain

Build agents to auto-fallback across Gemini models on quota exhaustion:

```
gemini-2.0-flash-lite (30 RPM) →
gemini-2.0-flash (15 RPM) →
gemini-2.5-flash (10 RPM) →
gemini-flash-lite-latest (fallback)
```

### Batch API Calls for Efficiency

Never call the LLM once per item. Always batch:

```python
# BAD: 33 API calls for 33 items
for item in items:
    result = call_ai(item)  # 33 calls → hits rate limit

# GOOD: 7 API calls for 33 items (batch size 5)
for batch in chunks(items, size=5):
    results = call_ai(batch)  # 7 calls → stays within free tier
```

---

## Workflow

### Step 1: Understand the Goal

Ask the user:

1. **What to collect:** "What data source? URL / API / RSS / public endpoint?"
2. **What to extract:** "What fields matter? Title, price, URL, date, score?"
3. **How to store:** "Where should results go? Notion, Google Sheets, Supabase, or local file?"
4. **How to enrich:** "Do you want AI to score, summarise, classify, or match each item?"
5. **Frequency:** "How often should it run? Every hour, daily, weekly?"

Common examples to prompt:
- Job boards → score relevance to resume
- Product prices → alert on drops
- GitHub repos → summarise new releases
- News feeds → classify by topic + sentiment
- Sports results → extract stats to tracker
- Events calendar → filter by interest

---

### Step 2: Design the Agent Architecture

Generate this directory structure for the user:

```
my-agent/
├── config.yaml              # User customises this (keywords, filters, preferences)
├── profile/
│   └── context.md           # User context the AI uses (resume, interests, criteria)
├── scraper/
│   ├── __init__.py
│   ├── main.py              # Orchestrator: scrape → enrich → store
│   ├── filters.py           # Rule-based pre-filter (fast, before AI)
│   └── sources/
│       ├── __init__.py
│       └── source_name.py   # One file per data source
├── ai/
│   ├── __init__.py
│   ├── client.py            # Gemini REST client with model fallback
│   ├── pipeline.py          # Batch AI analysis
│   ├── jd_fetcher.py        # Fetch full content from URLs (optional)
│   └── memory.py            # Learn from user feedback
├── storage/
│   ├── __init__.py
│   └── notion_sync.py       # Or sheets_sync.py / supabase_sync.py
├── data/
│   └── feedback.json        # User decision history (auto-updated)
├── .env.example
├── setup.py                 # One-time DB/schema creation

From ViewWay/hermes-by-everythings, MIT.

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