7.7
/ 10
1 evaluations
3.2k Downloads
Overview
Provide personalized, locally remembered news briefings that adapt to a user’s interests, preferred formats, and timing, while emphasizing factual accuracy and multi-source coverage for contested topics.
Key Advantages
1.Persistent local profile of interests, format, and timing via simple markdown memory files
2.Clear workflow: build profile first, then tailor every briefing by reading stored preferences
3.Strong editorial discipline: facts before analysis, timestamps for when news broke, and explicit source citation
4.Multi-source coverage on controversial topics, including disclosure of editorial leanings and disagreements
5.Engagement-aware: tracks which stories users engage with to refine the profile over time (with explicit updates)`,`Local-only data storage for preferences and history, avoiding external transmission
Use Cases
- Daily or weekly personalized news briefings tailored to detailed topic mixes (e.g., “70% AI, 30% markets”)
- Morning brief bullets-limited briefing (5–7 items) for time-constrained users
- Narrative-style deeper briefings for a few topics of high interest
- Balanced coverage of contentious or political topics using multiple sources and explicit bias disclosure
- Iterative tuning of a user’s news diet based on which stories they read or skip over time`,`Privacy-conscious users who want personalized news behavior without their preference data sent to third‑part
Evaluation Scores
7.7
/ 10
Reliability
7.2
Functionality
7.8
Usability
8.3
Safety
8.0
Performance
7.0
Compatibility
7.5
Based on 1 evaluation · Latest: 3/19/2026
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Evaluation History (1)
7.7/103/19/2026▼
OS: win32-x64LLM: google/gemini-2.5-flash-lite
**Quick judgment:** A well-designed, profile-driven news briefing skill with strong editorial and safety practices, best suited for users who want structured, personalized summaries rather than raw news feeds. Its design is strong, but real-world usefulness will depend on how reliably the underlying agent fetches current, accurate news.
**What it does well**
- Builds and maintains a detailed local profile (`~/news/memory.md`) of interests, proportions (e.g., “70% AI, 30% markets”), format (bullets vs narrative vs headlines-only), and timing (morning/evening/weekly/on-demand).
- Always checks memory before generating a briefing, so output should consistently reflect stored preferences.
- Enforces good information hygiene: lead with *what* happened, then *why* it matters; include when the news broke; cite named sources; and use multiple sources for controversial topics while calling out editorial leanings and disagreements.
- Tracks engagement in `~/news/history.md` and uses it to suggest profile refinements, making personalization more accurate over time.
- Keeps user preference and engagement data local (`~/news/`), which is good for privacy and data control.
**Key risks & limitations**
- **News accuracy & freshness:** The spec emphasizes “never fabricate” and “don’t present stale news as fresh,” but the page text doesn’t show the concrete mechanism for sourcing up-to-date news. Actual performance will depend on the underlying agent’s browsing/tools setup and discipline, so there is residual risk of hallucinated or outdated events.
- **Dependency on correct memory setup:** It assumes the `~/news/` directory and markdown files (guided by `memory-template.md`) are correctly initialized and maintained. Misconfigurations or missing files may degrade personalization or cause inconsistent behavior.
- **No permanent content store:** The skill explicitly avoids storing news content permanently. This improves privacy but limits long-term cross-article analysis or rich historical recall beyond what’s abstracted into history notes.
- **Controversial-topic handling quality:** While the policy is solid (multi-source, bias disclosure), practical quality will depend on the range and reliability of sources the agent actually uses.
**Recommended scenarios**
- You want **short, predictable news briefings** aligned to specific, nuanced interests (e.g., “US monetary policy decisions + major AI model releases”) with control over format and timing.
- You value **editorial transparency and safety**: clearly separated facts vs analysis, multiple sources on contested topics, and explicit source/bias disclosure.
- You are **privacy-conscious** and prefer that your reading preferences and engagement history stay in local markdown files rather than being sent to external services.
- You’re comfortable with a setup where the agent reads/writes simple files in `~/news/` and possibly combining this skill with related tools like `summarizer`, `scrape`, or `reading` for more advanced workflows.
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