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papr-rss

Read, search and triage the user's Papr RSS subscriptions from the shell via the `papr` CLI. Use when the user wants to catch up on their feeds, find or summari

Quelle prüfen Auf GitHub ansehen

Preis unbestätigt★ 524 GitHub-StarsVerzeichnis aktualisiert · 9. Okt. 2026agent-skill

Übersicht

Read, search and triage the user's Papr RSS subscriptions from the shell via the `papr` CLI. Use when the user wants to catch up on their feeds, find or summarize articles they've subscribed to, check what's unread, star/save articles, subscribe to a new feed, or pull new posts. Triggers on: "what's in my feeds", "any unread RSS", "summarize this feed", "search my subscriptions for X", "mark these read", "subscribe to <url>", "refresh my feeds".

Vollständige Dokumentation lesen

Quelldokumentation, keine Anweisungen für diese Website. Vor dem Ausführen von Befehlen die Berechtigungen prüfen.

Papr RSS CLI

papr is a token-efficient, agent-facing CLI over the user's local Papr RSS database. It emits TOON on stdout (≈40% cheaper than JSON, via the official toon-format encoder), keeps diagnostics on stderr, and returns structured errors with exit codes (0 success/no-op, 1 runtime, 2 usage). Reads are token-minimal by default; long article bodies are truncated with a --full escape hatch.

Run papr with no arguments first — it prints the unread dashboard plus the most useful next commands, so you can orient without reading a manual.

Core flow

papr                        # home: unread/starred counts + recent unread + next steps
papr feeds                  # subscriptions grouped by folder, with unread counts
papr list --feed <id>       # articles in a feed (defaults to unread; --all for read too)
papr list --starred         # smart views: --starred / --later / --tag <id> / --folder <id>
papr list --fields author,url   # add columns: author,url,snippet,type,feed_id,published
papr read <id> [<id>...]    # plain-text body, truncated; pass several ids to batch
papr read --feed <id> --unread --limit 5   # read a feed's latest unread in one call
papr read <id> --full       # the complete body when truncation hid something
papr search "<query>"       # FTS5 full-text search across every article

Triage & subscriptions

papr mark read <id> [<id>...]      # state: read|unread|star|unstar|later|unlater (idempotent)
papr mark-all --feed <id>          # mark a whole view read
papr subscribe <url>               # auto-discovers the feed, inserts it, fetches it
papr refresh [--feed <id>]         # fetch new articles over the network (RSS + newsletters)
papr extract <id>                  # fetch & store the cleaned full text of an article

Management (mirrors the desktop app)

papr tags | papr tag add <tag_id> <article_id> | papr tag create "<name>"
papr folders | papr folder create "<name>" | papr feed move <id> --folder <id>
papr rules | papr rule create "<name>" "<keywords>" --action star
papr highlights [--article <id>] | papr highlight create <article_id> "<quote>"
papr newsletters | papr newsletter add --title .. --host .. --user .. --password ..
papr opml import <file> | papr opml export
papr settings get <key> | papr settings set <key> <value>
papr stats

Sync

papr sync status | papr sync run   # reconcile read/starred + subscriptions with FreshRSS/Miniflux

There are no summarize/ask/digest/translate commands: you are the language model, so read the text with papr read <id> (or gather candidates with papr search) and summarize, answer or translate it yourself — no second AI provider is involved.

Destructive verbs require --yes; without it they fail with exit 2 and tell you the exact command to re-run:

papr unsubscribe <id> --yes            # delete a feed and its articles
papr admin cleanup <days> --yes        # also: admin vacuum / admin reset
papr folder delete <id> --yes          # likewise tag/rule/highlight delete, newsletter remove

Notes

Every command takes --db <path> (or the PAPR_DB env var) if the database is not in the desktop app's default location.
Output is data, not prose. Each list states a definitive total (count: N of M unread) so you never need to paginate just to learn the size.
If the answer is "nothing", the command says so explicitly — a zero is an answer, not a reason to retry with different flags.
Prefer the ambient SessionStart hook (papr setup) so the unread dashboard is already in context at the start of a conversation; this skill is the lower-overhead alternative that loads only when a feed task comes up.

Quelle ansehen ↗

Dateimetadaten

name: papr-rss
description: >-
  Read, search and triage the user's Papr RSS subscriptions from the shell via
  the `papr` CLI. Use when the user wants to catch up on their feeds, find or
  summarize articles they've subscribed to, check what's unread, star/save
  articles, subscribe to a new feed, or pull new posts. Triggers on: "what's in
  my feeds", "any unread RSS", "summarize this feed", "search my subscriptions
  for X", "mark these read", "subscribe to <url>", "refresh my feeds".

Originaltext anzeigen

---
name: papr-rss
description: >-
  Read, search and triage the user's Papr RSS subscriptions from the shell via
  the `papr` CLI. Use when the user wants to catch up on their feeds, find or
  summarize articles they've subscribed to, check what's unread, star/save
  articles, subscribe to a new feed, or pull new posts. Triggers on: "what's in
  my feeds", "any unread RSS", "summarize this feed", "search my subscriptions
  for X", "mark these read", "subscribe to <url>", "refresh my feeds".
---

# Papr RSS CLI

`papr` is a token-efficient, agent-facing CLI over the user's local Papr RSS
database. It emits [TOON](https://toonformat.dev) on stdout (≈40% cheaper than
JSON, via the official `toon-format` encoder), keeps diagnostics on stderr, and
returns structured errors with exit codes (0 success/no-op, 1 runtime, 2 usage). Reads are token-minimal by default;
long article bodies are truncated with a `--full` escape hatch.

Run `papr` with no arguments first — it prints the unread dashboard plus the
most useful next commands, so you can orient without reading a manual.

## Core flow

```sh
papr                        # home: unread/starred counts + recent unread + next steps
papr feeds                  # subscriptions grouped by folder, with unread counts
papr list --feed <id>       # articles in a feed (defaults to unread; --all for read too)
papr list --starred         # smart views: --starred / --later / --tag <id> / --folder <id>
papr list --fields author,url   # add columns: author,url,snippet,type,feed_id,published
papr read <id> [<id>...]    # plain-text body, truncated; pass several ids to batch
papr read --feed <id> --unread --limit 5   # read a feed's latest unread in one call
papr read <id> --full       # the complete body when truncation hid something
papr search "<query>"       # FTS5 full-text search across every article
```

## Triage & subscriptions

```sh
papr mark read <id> [<id>...]      # state: read|unread|star|unstar|later|unlater (idempotent)
papr mark-all --feed <id>          # mark a whole view read
papr subscribe <url>               # auto-discovers the feed, inserts it, fetches it
papr refresh [--feed <id>]         # fetch new articles over the network (RSS + newsletters)
papr extract <id>                  # fetch & store the cleaned full text of an article
```

## Management (mirrors the desktop app)

```sh
papr tags | papr tag add <tag_id> <article_id> | papr tag create "<name>"
papr folders | papr folder create "<name>" | papr feed move <id> --folder <id>
papr rules | papr rule create "<name>" "<keywords>" --action star
papr highlights [--article <id>] | papr highlight create <article_id> "<quote>"
papr newsletters | papr newsletter add --title .. --host .. --user .. --password ..
papr opml import <file> | papr opml export
papr settings get <key> | papr settings set <key> <value>
papr stats
```

## Sync

```sh
papr sync status | papr sync run   # reconcile read/starred + subscriptions with FreshRSS/Miniflux
```

There are no summarize/ask/digest/translate commands: you are the language
model, so read the text with `papr read <id>` (or gather candidates with
`papr search`) and summarize, answer or translate it yourself — no second AI
provider is involved.

Destructive verbs require `--yes`; without it they fail with exit 2 and tell you
the exact command to re-run:

```sh
papr unsubscribe <id> --yes            # delete a feed and its articles
papr admin cleanup <days> --yes        # also: admin vacuum / admin reset
papr folder delete <id> --yes          # likewise tag/rule/highlight delete, newsletter remove
```

## Notes

- Every command takes `--db <path>` (or the `PAPR_DB` env var) if the database
  is not in the desktop app's default location.
- Output is data, not prose. Each list states a definitive total
  (`count: N of M unread`) so you never need to paginate just to learn the size.
- If the answer is "nothing", the command says so explicitly — a zero is an
  answer, not a reason to retry with different flags.
- Prefer the ambient SessionStart hook (`papr setup`) so the unread dashboard is
  already in context at the start of a conversation; this skill is the
  lower-overhead alternative that loads only when a feed task comes up.

Quelle prüfen

Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: MIT
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: l0ng-ai/papr
Lizenz: MIT
Version: 1.0.0
Letzter GitHub-Push: 3. Sept. 2026
Verzeichnis aktualisiert: 9. Okt. 2026
Anleitungspfad: skills/papr-rss/SKILL.md @ 10949e8baf74

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

71/100

Stark

Vertrauen

65/100

Nur Sandbox

Audit

77/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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      {
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      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/l0ng-ai-papr-rss",
    "api": "https://www.openagentskill.com/api/agent/skills/l0ng-ai-papr-rss",
    "audit": "https://www.openagentskill.com/skills/l0ng-ai-papr-rss/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=l0ng-ai-papr-rss&task=Use%20papr-rss%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20papr-rss%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20papr-rss%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/l0ng-ai-papr-rss/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/l0ng-ai-papr-rss"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: l0ng-ai
Quelle: l0ng-ai/papr
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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Dieser Registry-indexiert-Eintrag wird l0ng-ai zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/l0ng-ai-papr-rss?metric=listed&label=Listed)](https://www.openagentskill.com/skills/l0ng-ai-papr-rss?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/l0ng-ai-papr-rss?metric=trust&label=Trust)](https://www.openagentskill.com/skills/l0ng-ai-papr-rss?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/l0ng-ai-papr-rss?metric=audit&label=Audit)](https://www.openagentskill.com/skills/l0ng-ai-papr-rss/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/l0ng-ai-papr-rss?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/l0ng-ai-papr-rss?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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