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 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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<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="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 23 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Data Exfiltration · line 186 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 270 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 5.5 | $0.00039 | $0.02472 |
| Sonnet 5.5 | $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 1mo 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
88% identical to data-scraper-agent — 29 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 — 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.
- 1mo 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 (1,080 stars, last pushed 2mo ago), licensed MIT. It adds 97 tokens to every session and 6,179 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 88% identical to data-scraper-agent, differing in 29 lines, and is treated as a copy.
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