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 n24q02m/wet-mcp --skill scrape-batchgit clone --depth 1 https://github.com/n24q02m/wet-mcpWrote 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/n24q02m/wet-mcp/scrape-batch)<a href="https://agentmods.dev/skills/n24q02m/wet-mcp/scrape-batch"><img src="https://agentmods.dev/badge/skills/n24q02m/wet-mcp/scrape-batch.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00084 | $0.01401 |
| Opus 5 | $0.00042 | $0.00700 |
| Sonnet 5 | $0.00017 | $0.00280 |
| Haiku 4.5 | $0.00008 | $0.00140 |
Grade A, and why
scrape-batch 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- scrape-batch — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
scrape-batch
Fan out extract(action="batch") over a URL list wet already knows, then
report per-URL outcomes honestly. The batch path applies per-domain
politeness (2 concurrent and 1 request/second per domain, 6 fetches in
flight overall) and returns whatever succeeded even when some URLs fail.
Use this skill when:
- The user supplies a list of URLs to read in full.
- A previous
searchreturned hits and the user wants the bodies, not the snippets. - A crawl or
extract(action="map")produced a URL set to pull down.
Do NOT use this skill when:
- There is one URL, or a handful from one domain -- call
extract(action="extract", urls=[...]), which is cached and cheaper. - The URLs are not known yet and the goal is an answer, not the pages --
use the
research-topicskill (extract(action="agent")). - The target is a whole site rather than a list -- use
extract(action="crawl")orextract(action="map"). - The user wants images or video from the pages -- use
media(action="list")thenmedia(action="download").
Steps
-
Collect and de-duplicate the URL list. Drop duplicates and fragment-only variants (
#section) -- each one costs a full fetch. Report the final count to the user before spending it. -
Split into chunks of at most 50. The cap is hard: 51 URLs returns
{"error": "Error: Maximum 50 URLs per batch (got 51)"}and nothing is fetched -- the call is refused, not truncated. -
Split further when one domain dominates. Politeness is per domain, so 40 URLs on a single host serialise to roughly one per second while 40 URLs across 20 hosts run near the global limit. A whole tool call is capped at 120 seconds (
TOOL_TIMEOUT), and hitting that ceiling returns only{"error": "... timed out after 120s ..."}-- the already-fetched pages are lost with it. Keep single-domain batches near 15-20 URLs. -
Call one chunk at a time, waiting for each to return:
extract(action="batch", urls=[...], format="markdown")formatacceptsmarkdown(default),textorhtml. Passstealth=trueonly after a normal attempt returns near-empty content for a protected site; it escalates to the heavier fetch strategies.
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.
- 8d ago First seen · 130 lines · 84 tokens per session scan A bc10527a42df
scrape-batch is a skill published in the GitHub repository n24q02m/wet-mcp (17 stars, last pushed today), licensed Apache-2.0. It adds 84 tokens to every session and 1,401 once invoked, about $0.0004 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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temporal-query
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memory-commit
Use when the user explicitly says "remember this", "save this", "ghi nho", "luu lai", "save for next time", or otherwise asks to persist the immediately preceding context. Captures with the appropriate contexttype (decision, preference, fact, skill, task, conversation) so future sessions can retrieve it accurately.
recall-context
Use at session start, before significant decisions, or when a new task references a known project to recall mnemo memories matching the current working directory, recently edited files, or topic keywords. Helps maintain continuity across sessions and avoid redoing past research.
session-handoff
End-of-session knowledge capture — decisions, preferences, corrections, conventions, open questions.
passport-bootstrap
Use when the user installs mnemo-mcp on a fresh machine and wants to restore prior memory state from S3 or Google Drive (Phase 2 passport sync). Triggers on phrases like "set up mnemo on this machine", "restore my memory passport", "import passport", "bootstrap mnemo", or when the user says they got a new laptop / VM…