Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nklofy/code-agent-skills/data-scraper-agentnpx skills add nklofy/code-agent-skills --skill data-scraper-agentgit clone --depth 1 https://github.com/nklofy/code-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nklofy/code-agent-skills/data-scraper-agent)<a href="https://agentmods.dev/skills/nklofy/code-agent-skills/data-scraper-agent"><img src="https://agentmods.dev/badge/skills/nklofy/code-agent-skills/data-scraper-agent.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00097 | $0.06083 |
| Opus 5 | $0.00048 | $0.03041 |
| Sonnet 5 | $0.00019 | $0.01217 |
| Haiku 4.5 | $0.00010 | $0.00608 |
Grade A, and why
data-scraper-agent scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.get("https://api.example.com/items", headers=HEADERS, timeout=15) This is a copy
91% identical to data-scraper-agent — 30 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 766 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
# 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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 766 lines · 97 tokens per session scan A f5efab367741
data-scraper-agent is a skill published in the GitHub repository nklofy/code-agent-skills (18 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 97 tokens to every session and 6,083 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 91% identical to data-scraper-agent, differing in 30 lines, and is treated as a copy.
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