yourrss

name

yourrss

description

AI-powered RSS digest from your FreshRSS instance with personalized recommendations via Cortex Memory API. Trigger with /digest, /recommend, or ask for news/headlines. Fetches articles via FreshRSS API, scores by relevance/quality using configurable AI provider (agent or external OpenAI-compatible API), generates summaries, learns preferences via Cortex REST API (agent: reader), and saves highlights to Blinko.

YourRSS — FreshRSS AI Digest

AI-powered daily digest from your self-hosted FreshRSS instance. Scores, summarizes, and recommends articles based on your reading preferences stored in Cortex Memory (agent: reader). Supports flexible AI provider selection — use the current Agent (free with Cursor) or an external cheap model (save tokens with Claude Code).

Setup

Required: FreshRSS

export FRESHRSS_URL="https://your-freshrss-instance.com"
export FRESHRSS_USER="your-username"
export FRESHRSS_API_PASSWORD="your-api-password"

API password: FreshRSS → Settings → Profile → API Management.

Required: Cortex Memory

export CORTEX_URL="http://localhost:21100"
export CORTEX_TOKEN="your-cortex-auth-token"    # optional if no auth
export CORTEX_AGENT="reader"                     # isolated agent for RSS preferences

Optional: AI Scoring Provider

Choose who does the scoring and summarization work:

# "agent" = you (the Agent) handle scoring directly — best when tokens are free (e.g. Cursor)
# "openai" = call an external OpenAI-compatible API — best to save tokens (e.g. Claude Code)
export AI_PROVIDER="agent"
export AI_BASE_URL="https://api.openai.com/v1"   # or your gateway URL
export AI_API_KEY="sk-..."
export AI_MODEL="gpt-4o-mini"                      # any OpenAI-compatible model

Compatible APIs: OpenAI, Gemini, DeepSeek, Qwen, Ollama, your own vercel-gateway-tools proxy, etc.

Optional: Blinko

export BLINKO_URL="https://your-blinko-instance.com"
export BLINKO_TOKEN="your-blinko-api-token"

Commands

Daily Digest: /digest

Parameters (ask user if not specified):

Param	Options	Default
Time range	4h / 12h / 24h / 48h / 72h	24h
Top N	5 / 10 / 15 / 20	10
Language	zh / en	zh
Category	any FreshRSS category	all

Recommendations: /recommend

Save: /save [number]

Preference: /like [number], /dislike [number], /prefer topic:X

Browse: /feeds, /categories

Manage: /unsubscribe [feed], /subscribe [url]

Workflow

Step 0: Initialize Cortex reader agent

Before first use, ensure the reader agent exists in Cortex:

node {baseDir}/scripts/cortex-api.mjs init

This checks if the reader agent exists and creates it if needed. Run this once or at the start of every digest session (idempotent).

Step 1: Fetch articles from FreshRSS

node {baseDir}/scripts/fetch-freshrss.mjs --hours <HOURS> --count 50 --unread

Category-specific:

node {baseDir}/scripts/fetch-freshrss.mjs --hours 24 --count 50 --unread --category "Technology"

List categories / feeds:

node {baseDir}/scripts/fetch-freshrss.mjs --categories
node {baseDir}/scripts/fetch-freshrss.mjs --feeds

Manage subscriptions:

# Unsubscribe by feed ID (get IDs from --feeds)
node {baseDir}/scripts/fetch-freshrss.mjs --unsubscribe "feed/123"

# Subscribe to new feed
node {baseDir}/scripts/fetch-freshrss.mjs --subscribe "https://example.com/feed.xml" --subscribe-category "Technology"

Capture the JSON output from stdout.

Step 2: Load user preferences from Cortex (REST API)

Fetch user reading preferences from the reader agent:

node {baseDir}/scripts/cortex-api.mjs preferences

This queries Cortex for all preference, fact, insight, and habit memories related to RSS reading. The output is a JSON array of memory objects.

Parse the returned memories to build a preference profile:

• Liked topics: extract topic keywords from "User liked article..." memories
• Disliked topics: extract from "User found uninteresting..." memories
• Preferred sources: extract source names from liked article memories
• Reading patterns: extract from digest log memories (time, frequency, categories)

If no preferences found (first run), proceed with neutral scoring.

Step 3: Score and classify (AI Provider Selection)

Check AI_PROVIDER setting to decide who handles scoring:

Option A: AI_PROVIDER=openai (external model)

First, export user preferences from Cortex for the scoring model:

node {baseDir}/scripts/cortex-api.mjs preferences > /tmp/rss-prefs.json

Then pipe articles + preferences into the scoring script:

node {baseDir}/scripts/fetch-freshrss.mjs --hours <HOURS> --count <COUNT> --unread \
  | node {baseDir}/scripts/score-articles.mjs --top <N> --language zh --preferences /tmp/rss-prefs.json

The script embeds user preferences (liked/disliked topics, preferred sources) into the scoring prompt, calls the configured OpenAI-compatible API, and returns a JSON with personalized scores, summaries, categories, keywords, and recommendation reasons. The Agent then just formats and presents the results — minimal token usage.

Override model on-the-fly:

... | node {baseDir}/scripts/score-articles.mjs --top 10 --provider openai --model gemini-2.5-flash-lite --preferences /tmp/rss-prefs.json

Option B: AI_PROVIDER=agent (default — Agent does scoring)

The Agent processes scoring directly. Save the fetched articles from Step 1, then score each on three dimensions (1-10):

1. Relevance — Match against user preferences from Cortex. If no prefs, score on general tech/AI relevance.
2. Quality — Depth, originality, substance (from title + summary).
3. Timeliness — Breaking vs. evergreen.

Apply preference multipliers from Cortex data:

• Topics matching liked topics: score × 1.3
• Topics matching disliked topics: score × 0.5
• Sources from liked articles: score × 1.2
• Topics explicitly preferred (/prefer): score × 1.5

Classify:

• 🤖 AI / ML
• 🔒 Security
• ⚙️ Engineering
• 🛠 Tools / Open Source
• 💡 Opinion / Essay
• 🌐 Web / Frontend
• 📊 Data / Infrastructure
• 📝 Other

Select top N by weighted score.

In both cases, the output should be a ranked list of articles with scores, categories, summaries, and keywords.

Step 4: Generate summaries (if not already done by score-articles.mjs)

If using openai provider, summaries are already included in the scored output — skip to Step 5.

If using agent provider, for each selected article:

1. If summary > 100 chars, use it
2. If not, use web_fetch to read full article
3. Generate:
   • Chinese title translation (original as link)
   • 2-3 sentence summary: problem → insight → conclusion
   • Personalized recommendation reason (using Cortex preferences if available)
   • Keywords (2-3 tags)

Step 5: Trend highlights

Analyze selected articles and identify 2-3 macro trends.

Step 6: Format output

📰 FreshRSS AI Digest — {date}
来自你的 FreshRSS 订阅 · {feed_count} 源 · {hours}h 窗口

📝 今日看点
{2-3 sentence trend summary}

🏆 今日必读 (Top 3)
1. [{Chinese title}]({url})
   {source} · {relative time} · [{weighted_score}分]
   {summary}
   🏷️ {keywords}
   💡 推荐理由：{personalized reason from Cortex prefs}
   🔗 {url}

2. ...
3. ...

📋 更多精选
4. [{Chinese title}]({url}) — {source} · [{score}分] {one-line summary}
5. ...

📊 统计：{N} 源 → {M} 篇未读 → {K} 篇精选
🧠 个性化：基于 Cortex reader agent ({pref_count} 条偏好记忆)

IMPORTANT: Every article MUST include its original URL. The URL is available in the scored article data (url field). Never omit the link — it is the most important part for the user to access the original content.

Step 7: Interactive Dialogue — Wait for User Feedback

CRITICAL: Do NOT end the conversation after showing the digest. This is a multi-turn interactive session.

After presenting the digest, ask the user:

你对今天的精选有什么想法？
- 回复数字（如 "1"、"3"）查看文章详情
- 说 "喜欢 1" 或 "不感兴趣 4" 来训练推荐
- 说 "多看看 AI 安全" 来调整偏好
- 说 "保存 2" 保存到 Blinko
- 说 "下一页" 查看更多文章
- 说 "结束" 结束本次阅读

Step 8: Process User Feedback (Multi-turn Loop)

This step repeats until the user says "结束" or leaves:

If user replies with a number (e.g., "1", "看看第3篇"):

• Fetch the full article content via web_fetch
• Present a detailed summary with key takeaways
• Ask: "这篇文章怎么样？觉得有用吗？"

If user says "喜欢 X" or positive feedback (e.g., "不错", "这个好"):

• Identify the article and its topics/source
• Record to Cortex:

  node {baseDir}/scripts/cortex-api.mjs like "{title}" --source "{source}" --topics "{topics}" --url "{url}"
  

• Respond: "已记住！以后会多推荐 {topics} 相关的内容。"

If user says "不感兴趣 X" or negative feedback (e.g., "无聊", "不想看这类"):

• Record to Cortex:

  node {baseDir}/scripts/cortex-api.mjs dislike "{title}" --source "{source}" --topics "{topics}"
  

• Respond: "收到，以后会减少推荐 {topics} 类内容。"

If user adjusts preferences (e.g., "多看看AI安全", "少推荐区块链"):

• Record explicit preference:

  node {baseDir}/scripts/cortex-api.mjs remember "User wants more articles about: {topic}" --category preference --importance 0.9
  

  or for negative:

  node {baseDir}/scripts/cortex-api.mjs remember "User wants fewer articles about: {topic}" --category preference --importance 0.8
  

• Respond with confirmation and immediately re-score remaining articles with updated preferences

If user says "下一页" or "更多":

• Show the next batch of articles (items N+1 to N+5 from the scored list)
• Each article MUST include its original URL
• Continue the feedback loop

If user says "保存 X":

• Execute the Blinko save flow (see Save to Blinko section)
• Record the save action to Cortex

If user says "结束" / "够了" / "谢谢":

• Proceed to Step 9

Step 9: Session Summary & Cortex Log

At the end of the conversation, summarize what was learned and log to Cortex:

node {baseDir}/scripts/cortex-api.mjs digest-log "{date}" \
  --topics "AI,Security,Go" \
  --categories "AI/ML,Engineering" \
  --articles 10 \
  --feeds 45

Also store a session-level observation:

node {baseDir}/scripts/cortex-api.mjs remember \
  "Digest session {date}: User liked {liked_topics}, disliked {disliked_topics}. Saved {N} articles. Spent most time on {main_topic}. Preference trend: {observation}" \
  --category agent_user_habit --importance 0.7

This builds the long-term preference model.

Personalized Recommendations (/recommend)

1. Load full preference profile:

node {baseDir}/scripts/cortex-api.mjs preferences

2. Fetch recent unread articles (48h):

node {baseDir}/scripts/fetch-freshrss.mjs --hours 48 --count 100 --unread

3. Score heavily weighted by Cortex preferences:

   • Match favorite topics (×1.5)
   • Match preferred sources (×1.3)
   • Penalize disliked topics (×0.3)

4. Present top 5 with personalized explanation (always include URL):

🎯 为你推荐 — 基于 Cortex reader 的 {N} 条阅读偏好

1. [{title}]({url})
   📍 {source} · {time}
   🧠 推荐原因：你经常关注 {topic}，这篇深入讨论了 {angle}
   🔗 {url}

2. ...

Preference Learning

/like [number]

Record a positive preference to Cortex:

node {baseDir}/scripts/cortex-api.mjs like "{article_title}" \
  --source "{source_name}" \
  --topics "AI,Security" \
  --url "https://..."

/dislike [number]

Record a negative signal:

node {baseDir}/scripts/cortex-api.mjs dislike "{article_title}" \
  --source "{source_name}" \
  --topics "Crypto,NFT"

/prefer topic:X

Explicit preference:

node {baseDir}/scripts/cortex-api.mjs remember "User explicitly prefers reading about: {topic}" --category preference --importance 0.9

/forget topic:X

Remove preference:

# First search for the memory
node {baseDir}/scripts/cortex-api.mjs search "{topic} preference"
# Then delete by ID
node {baseDir}/scripts/cortex-api.mjs forget "{memory_id}"

Save to Blinko (/save)

When user says /save 3:

1. Get article details (title, URL, summary, tags)
2. If BLINKO_URL and BLINKO_TOKEN are set:

   curl -X POST "${BLINKO_URL}/api/v1/note/upsert" \
     -H "Authorization: Bearer ${BLINKO_TOKEN}" \
     -H "Content-Type: application/json" \
     -d '{"content": "# {title}\n\n{summary}\n\n[Original]({url})\n\n#rss-digest #{category}", "type": 0}'
   

3. After saving, record to Cortex:

   node {baseDir}/scripts/cortex-api.mjs remember "User saved article '{title}' from {source} about {topics} to Blinko" --category preference --importance 0.8
   

Scripts Reference

Script	Purpose
fetch-freshrss.mjs	Fetch articles, categories, feeds; subscribe/unsubscribe
cortex-api.mjs	Cortex Memory CRUD: recall, remember, like, dislike, preferences
score-articles.mjs	AI scoring: agent passthrough or external OpenAI API
load-env.mjs	Load .env config (shared by all scripts)

Notes

• Requires Node.js 18+ for the scripts
• FreshRSS API password ≠ login password (Settings → Profile → API Management)
• Cortex agent reader is auto-created via cortex-api.mjs init
• Works with OpenClaw, Cursor, Claude Code, OpenCode — any client that can run shell scripts
• All data self-hosted: FreshRSS + Cortex + Blinko = your infrastructure
• Default fetch: 50 articles; adjust --count for busier feeds
• AI_PROVIDER=agent uses zero extra cost when your Agent has unlimited tokens
• AI_PROVIDER=openai supports any OpenAI-compatible API (Gemini, DeepSeek, Qwen, Ollama, etc.)
• All parameters are configurable via CLI args — nothing is hardcoded