Skip to content
cccskills
FreePublished on GitHub

news-collection

Collect, filter, and freshness-qualify news items.

The English summary is being prepared. Showing the original description.

See how to install

Files included (1)

  • SKILL.md9.4 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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.

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.

  • 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. See Freshness Forensics below.

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 (see Artifact Schema below) 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):

VerdictCount
keepn
monitor_onlyn
rejectn
unclear (freshness)n
duplicates consolidatedn

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 in evidence_notes; freshness verdict for that item is unclear.

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, verdict unclear.

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.

Evidence Contract

Every collected item carries five facts:

FactTypeMissing value handling
titlestringEmpty string only if page truly has none; note in evidence_notes
urlstringRequired -- no URL means unverifiable; exclude from artifact
outletstringDerive from domain when no masthead found; note derivation
authorstring or nullnull when no byline; note where you looked
published_atISO 8601 or nullExtracted from metadata, never guessed. null when absent; lower confidence, disclose

Confidence: high (all five extracted), medium (one derived or weak), low (published_at null or 2+ derived).

Item Schema

{
  "id": "string", "title": "string", "url": "string",
  "outlet": "string", "author": "string|null",
  "published_at": "ISO 8601|null", "confidence": "high|medium|low",
  "evidence_notes": "string", "verdict": "keep|monitor_only|reject",
  "reason_code": "string", "freshness": "fresh|stale|unclear|null",
  "first_public_estimate": "ISO 8601|null",
  "freshness_sources": "integer", "duplicates_of": "string id|null"
}

Artifact Schema

{
  "schema_version": "1.0", "topic": "string",
  "collected_at": "ISO 8601",
  "counts": {"collected":0,"keep":0,"monitor_only":0,"reject":0,"unclear_freshness":0,"duplicates_consolidated":0},
  "items": []
}

Conservation rule: collected == keep + monitor_only + reject.


Coarse Filter

VerdictMeaningGoes to freshness check
keepOn-topic, substantive, plausibly freshYes
monitor_onlyWorth watching: off-topic but high-magnitude, thin, or uncertainYes
rejectJunk: spam, ads, dead pages, clearly irrelevantNo

Asymmetry rule: high-magnitude stories downgrade to monitor_only at most, never reject. A false keep is cheap; a silent drop is expensive.

Reason Codes

CodePairs withMeaning
RC-ONTOPICkeepDirectly on topic
RC-DEVkeepNew development of known story
RC-OFFTOPIC-BIGmonitor_onlyOff-topic but high-magnitude
RC-THINmonitor_onlyOn-topic but little substance
RC-UNCERTAINmonitor_onlyRelevance unclear
RC-SPAMrejectPromotional, SEO bait
RC-IRRELEVANTrejectOutside topic, low magnitude
RC-NOCONTENTrejectDead link, paywall stub, empty page

Freshness Forensics

First-public time vs page date

A syndicated repost or refreshed CMS page can carry today's date on old news. Establish first-public time from (in trust order):

  1. Article metadata (article:published_time, JSON-LD datePublished) on the originating outlet's page.
  2. Earliest published_at across collected items covering the same story.
  3. In-text anchors ("announced Tuesday") tied to a known date.

Two independent sources or verdict unclear. Independent means separately reported -- two copies of the same wire story count as one.

VerdictCondition
freshTwo sources put first-public time inside the freshness window
staleTwo sources put it outside, or page repackages older coverage
unclear<2 sources, conflicting evidence, or no usable timestamp

Syndication detection

Signals of a repost: wire credit in byline, rel=canonical pointing elsewhere, body near-identical to an earlier item, aggregator framing ("read more at"), outlet with no original bylines across beats. A repost inherits the original's first-public time.

Canonical-coverage selection

When several items cover one story, deliver one canonical item, link the rest via duplicates_of. Pick canonical by: originating outlet > earliest published_at > most complete five-fact record.

Same-story vs new-development

QuestionSame story (consolidate)New development (keep separate)
New named facts?NoYes
New primary source?NoYes
Event advanced?NoYes
Only framing/outlet changed?Yes--

Two+ "new development" answers = separate item.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Manage multiple Claude Code accounts: add, list, check, launch, and install shell aliases for 10+ isolated CLAUDE_CONFIG_DIR profiles.

The English summary is being prepared. Showing the original description.

notque/vexjoy-agent442Updated Oct 20, 2026

Improve architecture across modules by deepening interfaces.

The English summary is being prepared. Showing the original description.

notque/vexjoy-agent442Updated Oct 20, 2026

Assessment: read-only inspection, codebase overview, value analysis, health checks, ADR consultation, decision analysis, multi-perspective critique.

The English summary is being prepared. Showing the original description.

notque/vexjoy-agent442Updated Oct 20, 2026

Background memory consolidation — overnight review, merge, and injection payload for memory files.

The English summary is being prepared. Showing the original description.

notque/vexjoy-agent442Updated Oct 20, 2026

Jev-driven browser automation: Jev picks operations, programs execute, a text model writes field values only when Jev cannot pick one from the goal.

The English summary is being prepared. Showing the original description.

notque/vexjoy-agent442Updated Oct 20, 2026

Write, compose, integrate, and improve programs that call Jev, TypeSafe's System One judgment model.

The English summary is being prepared. Showing the original description.

notque/vexjoy-agent442Updated Oct 20, 2026

See all skills by notque

Report a problem with this skill