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/monkilabs/opencastle/data-engineeringnpx skills add monkilabs/opencastle --skill data-engineeringgit clone --depth 1 https://github.com/monkilabs/opencastleWhat 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 | $0.00035 | $0.00418 |
| Opus 5 | $0.00017 | $0.00209 |
| Sonnet 5 | $0.00007 | $0.00084 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
data-engineering scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Data Engineering
Project-specific sources, full schema, full scraper and extended validator: REFERENCE.md.
Scraper
Headless browser cluster (Puppeteer Cluster / Playwright) with retryLimit: 3, retryDelay: 5000, timeout: 30000, args: ['--no-sandbox', '--disable-setuid-sandbox'].
NDJSON Output
One record per line. Required: name (preserve original encoding), lat/lng, address (full text), source (e.g. google-maps), sourceId (source-unique), category. Optional: rating, reviewCount, phone, website, openingHours, photos, priceLevel.
Pipeline
node ./scripts/scrape-to-ndjson.js --out=data.ndjson --pages=100
node ./scripts/validate-ndjson.js data.ndjson
node ./scripts/dry-import.js data.ndjson --target=staging
node ./scripts/import.js data.ndjson --target=production
- Scrape a
--dry-runsample of 50–200 records; require expected fields and geo data. Otherwise fix extractor selectors and re-run the sample. - Validate NDJSON line-by-line (JSON parse + schema): require 0 parse errors, all required fields. Isolate failures with
ndjson-filter, inspect source HTML. - Dry-run import to staging with
createOrReplacedisabled: counts within ±5% of expectation, no duplicates. Otherwise revert staging and adjust the dedupe key. - Snapshot the target (timestamped export) before writing.
- Import with idempotent keys; revert to the snapshot on failure.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 32 lines · 35 tokens per session scan A b7ac20ac2f4a
data-engineering is a skill published in the GitHub repository monkilabs/opencastle (61 stars, last pushed 4d ago), licensed MIT. It adds 35 tokens to every session and 418 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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