AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
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/ufy2024/auc/data-scraper-agentnpx skills add ufy2024/AuC --skill data-scraper-agentgit clone --depth 1 https://github.com/ufy2024/AuCWrote 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/ufy2024/auc/data-scraper-agent)<a href="https://agentmods.dev/skills/ufy2024/auc/data-scraper-agent"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/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 | $0.06179 |
| Opus 5 | $0.00048 | $0.03089 |
| Sonnet 5 | $0.00019 | $0.01236 |
| Haiku 4.5 | $0.00010 | $0.00618 |
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) Copies of this mod
8 near-identical copies found in the catalogue:
- data-scraper-agent — 92% identical, 46 lines differ
- data-scraper-agent — 91% identical, 30 lines differ
- data-scraper-agent — 88% identical, 29 lines differ
- data-scraper-agent — 88% identical, 43 lines differ
- data-scraper-agent — 88% identical, 29 lines differ
- data-scraper-agent — 86% identical, 31 lines differ
- data-scraper-agent — 84% identical, 38 lines differ
- data-scraper-agent — 80% identical, 57 lines differ
How it starts
The opening of the file, as written. The whole thing — 788 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 · 788 lines · 97 tokens per session scan A 49a9764b2ed1
data-scraper-agent is a skill published in the GitHub repository ufy2024/AuC (1,091 stars, last pushed 1mo ago), licensed MIT. It adds 97 tokens to every session and 6,179 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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