ソース情報
- リポジトリ
- notque/vexjoy-agent
- ソースの最終更新活動
- 2026年7月5日 20:41
- 検出された SKILL.md の言語
- 英語
- スター
- 415
- フォーク
- 44
インストール方法
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
ソースファイルを確認
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
メニュー
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/notque/vexjoy-agent --skill news-collectionコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか?SkillsMP が現在取得できるファイルをダウンロードできます。
Run the full evidence-to-live implementation workflow for large, multi-system, multi-wave, or CPU-delegated 5 Star Booker GM programs.
Classify user requests and route to the correct agent + skill. Primary entry point for all delegated work.
Structured multi-phase workflows: review, debug, refactor (tidy, clean up, untangle messy code without behaviour change), deploy, create, research.
SOC 職業分類に基づく
SKILL.md を表示中
| name | news-collection |
| promoted_to | content-calendar |
| description | Collect, filter, and freshness-qualify news items. |
| user-invocable | false |
| routing | {"triggers":["news collection","collect news","qualify news items","news triage","filter news feed","check news freshness"],"not_for":"general research reports — that is research-pipeline. Pick this when the input is a stream of news items to qualify for a content pipeline.","pairs_with":["fact-check"],"complexity":"Medium","category":"research"} |
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.
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.
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.
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:
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.
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.
No items collected
No timestamp found anywhere for an item
published_at: null, confidence: low, disclose in
evidence_notes; freshness verdict for that item is unclear.Two sources disagree on first-public time
references/freshness-forensics.md); if still split, verdict unclear.Item count mismatch at DELIVER
| 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 |