News Collection
SkillAI & modelsCollect, filter, and freshness-qualify news items.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the News Collection skill
What this skill tells your AI
The instructions your AI receives, as published by notque/vexjoy-agent in skills/research/news-collection/SKILL.md and read by ahel’s review.
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 schema in
references/evidence-contract.md. 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 from
references/coarse-filter.md.
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. See the dispatch note in references/coarse-filter.md.
Gate: every item has exactly one verdict and one reason code. reject
verdicts on items that look high-magnitude get re-checked once before the
phase closes.
Phase 3: FRESHNESS CHECK
Goal: for each keep and monitor_only item, establish when the story
first became public and whether this page is the original coverage. Methods in
references/freshness-forensics.md:
- First-public time vs page date — a page can carry today's date on old news.
- Syndication and aggregator reposts detected; select the canonical coverage.
- Same-story vs new-development rubric — consolidate duplicates, keep genuine developments.
Rule (verbatim from the design): two independent sources or verdict "unclear". Conservative default: unclear over guessed. An "unclear" verdict is recoverable downstream; a confidently wrong "fresh" verdict ships stale news.
Gate: every surviving item carries freshness: fresh | stale | unclear, a
first_public_estimate (or null), and the count of sources backing the
verdict. Duplicate clusters are consolidated to one canonical item with
duplicates_of links.
Phase 4: DELIVER
Goal: emit qualified items as a structured JSON artifact (schema:
references/evidence-contract.md) plus a summary table. Every verdict state
appears in the artifact — monitor_only, unclear, and reject items ship
with their verdicts rather than vanishing, so consumers see the full triage.
Gate (deterministic phase checkpoint — emit this table before delivering the artifact; delivery without it is incomplete):
| Verdict | Count |
|---|---|
| keep | n |
| monitor_only | n |
| reject | n |
| unclear (freshness) | n |
| duplicates consolidated | n |
The counts make silent drops visible: collected total must equal keep + monitor_only + reject. If it does not, return to the phase that lost items.
Error Handling
No items collected
- Cause: sources unreachable, or feed genuinely empty.
- Solution: unreachable sources → report collection failure and stop; reachable but empty → deliver an empty artifact with zero counts. The two outcomes stay distinct so an outage is not read as a quiet news day.
No timestamp found anywhere for an item
- Cause: page has no metadata, byline date, or sitemap entry.
- Solution: set
published_at: null,confidence: low, disclose inevidence_notes; freshness verdict for that item isunclear.
Two sources disagree on first-public time
- Cause: syndication chain or republished update.
- Solution: apply canonical-coverage selection
(
references/freshness-forensics.md); if still split, verdictunclear.
Item count mismatch at DELIVER
- Cause: an item was dropped without a verdict.
- Solution: diff item ids against the COLLECT record; assign the missing item a verdict with reason code.
Reference Loading Table
| Signal | Load These Files | Why |
|---|---|---|
| Recording items, JSON artifact schema, confidence fields | evidence-contract.md | Five-fact contract and artifact schema |
| Assigning verdicts, reason codes, cheap-model dispatch | coarse-filter.md | Verdict definitions and dispatch note |
| Dating a story, syndication, duplicates, canonical pick | freshness-forensics.md | Forensic methods and rubrics |
Signals
- GitHub stars
- 419
- Forks
- 44
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
news-collection- Source
- github.com/notque/vexjoy-agent