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 notque/vexjoy-agent --skill news-collectiongit clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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/notque/vexjoy-agent/news-collection)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/news-collection"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/news-collection/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/notque/vexjoy-agent/news-collection"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/news-collection.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00016 | $0.02298 |
| Opus 5.5 | $0.00006 | $0.00919 |
| Sonnet 5.5 | $0.00003 | $0.00460 |
| Haiku 4.5 | $0.00002 | $0.00230 |
Grade A, and why
news-collection 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 25d 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.
How it starts
The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
News Collection
Gather news items on a topic, filter junk cheaply, verify freshness, and emit qualified items under an evidence contract. Pipeline-shaped: four phases, a gate between each. Content pipelines consume the JSON artifact; pair with fact-check to verify what this skill qualifies.
Instructions
Phase 1: COLLECT
Goal: gather candidate items from available sources (feeds, search results, provided fixtures). Every item carries the five-fact evidence contract: title, url, outlet, author, published_at.
Rule (verbatim from the design): publish times are extracted (article
metadata), never guessed; missing timestamp → recorded as unknown, confidence
lowered and disclosed. A guessed timestamp poisons every downstream freshness
verdict; an honest "published_at": null keeps the item usable with known
uncertainty.
Record each item as one JSON object per the item schema below. Fill
evidence_notes with where each fact came from (meta tag, byline, JSON-LD,
sitemap).
Distinguish outcomes: zero items because sources were unreachable is a collection failure (report it, stop); zero items from reachable sources is a valid empty feed (deliver an empty artifact with counts of zero).
Gate: every collected item has all five fields present — value or explicit
null with a confidence downgrade and a disclosure note. Items with silent
gaps stay in COLLECT until the gap is recorded.
Phase 2: COARSE FILTER
Goal: cheap, high-recall pass over collected items. Three verdicts:
keep / monitor_only / reject, each with a reason code (see
Coarse Filter below).
Rule (verbatim from the design): high-magnitude stories are downgraded to monitor_only at most, never rejected — a false keep is cheap, a silent drop is expensive. A keep costs one extra freshness check; a wrongly dropped major story costs the whole pipeline its value.
This phase runs on a cheap model when dispatched -- it needs recall, not judgment depth. Give the dispatched model: topic, verdict/code tables, asymmetry rule, and items' five facts plus a text excerpt.
What ships with it
3 files 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.
- 26d ago Changed · -214 lines · +4 tokens per session f50f76f47df1
- 27d ago Changed · +99 lines a95dc6b2e479
- 1mo ago First seen · 144 lines · 16 tokens per session scan A 305c42b80636
news-collection is a skill published in the GitHub repository notque/vexjoy-agent (442 stars, last pushed today), licensed MIT. It adds 16 tokens to every session and 2,298 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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