clawfeedradar 0.1.0

Personal feed radar built on clawsqlite interest clusters

clawfeedradar

Personal "reading radar" built on top of clawsqlite: it pulls articles from Hacker News / RSS / arXiv and ranks them against your existing knowledge base, generating a personalized RSS feed with optional bilingual summaries.

Goal: simple, controllable, auditable, and loosely coupled with clawsqlite. This document describes the current v1 implementation (branch bot/20260402-embedding).

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When running inside OpenClaw

clawfeedradar expects an existing clawsqlite knowledge base with interest clusters.

In an OpenClaw workspace, the recommended way to set this up is:

1. Install the clawsqlite-knowledge skill (if not already installed):

   openclaw skills add clawsqlite-knowledge
   

   Or via the web catalog:

   • https://clawhub.ai/skills/clawsqlite-knowledge

2. Initialize and build interest clusters using that skill (see the skill README for the exact commands).

Once clawsqlite-knowledge is installed and has built interest_clusters, clawfeedradar can attach to the same DB and reuse the interest space for scoring.

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Overview

clawfeedradar does three things:

1. Fetch candidates from external sources
   Currently HN RSS and generic RSS are supported; arXiv and others can be added later.
2. Score candidates using clawsqlite interest clusters
   It uses embeddings + interest clusters to estimate how well each candidate matches your long-term interests, then applies light recency / popularity bias.
3. Generate an RSS feed with optional bilingual content
   For selected items it scrapes fulltext, calls a small LLM to produce preview summary + bilingual body, and writes an XML + JSON pair.

In short: clawsqlite knows what you like; clawfeedradar goes out, finds similar content, and feeds it to your RSS reader.

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Quick start

1. Prepare environment

Assume a workspace like:

~/.openclaw/workspace/
  ├── clawsqlite          # clawsqlite repo
  ├── knowledge_data      # your clawsqlite-knowledge DB
  ├── clawfeedradar       # this repo
  └── clawfetch / ...     # fulltext scraper + wrapper

Make sure clawsqlite has built interest clusters:

cd ~/.openclaw/workspace/clawsqlite
clawsqlite knowledge build-interest-clusters \
  --root ~/.openclaw/workspace/knowledge_data

2. Configure clawfeedradar

cd ~/.openclaw/workspace/clawfeedradar
cp ENV.example .env
# edit .env for your local setup

You need to configure at least:

• clawsqlite knowledge base
  • CLAWSQLITE_ROOT pointing to knowledge_data
  • CLAWSQLITE_DB if you want an explicit sqlite path
• Embedding service (shared with clawsqlite)
  • EMBEDDING_BASE_URL / EMBEDDING_MODEL / EMBEDDING_API_KEY
  • CLAWSQLITE_VEC_DIM must match the embedding model dimension
• Output directory
  • CLAWFEEDRADAR_OUTPUT_DIR for XML/JSON output
• Fulltext scraping
  • CLAWFEEDRADAR_SCRAPE_CMD pointing to a wrapper that calls clawfetch
  • CLAWFEEDRADAR_SCRAPE_WORKERS controlling parallelism (per-host serialization still applies)
• Small LLM (summaries + bilingual body)
  • SMALL_LLM_BASE_URL / SMALL_LLM_MODEL / SMALL_LLM_API_KEY
  • CLAWFEEDRADAR_LLM_CONTEXT_TOKENS (approx token budget, internally converted to char budget)
  • CLAWFEEDRADAR_LLM_MAX_PARAGRAPH_CHARS (screen-sized bilingual segments)
  • CLAWFEEDRADAR_LLM_SLEEP_BETWEEN_MS
  • CLAWFEEDRADAR_LLM_SOURCE_LANG / CLAWFEEDRADAR_LLM_TARGET_LANG
• Scoring weights
  • CLAWFEEDRADAR_W_RECENCY
  • CLAWFEEDRADAR_W_POPULARITY
  • CLAWFEEDRADAR_RECENCY_HALF_LIFE_DAYS
  • CLAWFEEDRADAR_INTEREST_SIGMOID_K (steepness of S-shaped stretching around 0.5)
• Default item count
  • CLAWFEEDRADAR_MAX_ITEMS as the default when --max-items is omitted

3. Run a single feed (debug)

cd ~/.openclaw/workspace/clawfeedradar
python -m clawfeedradar.cli run \
  --root ~/.openclaw/workspace/orgmode/clawsqlite/data \
  --url https://feeds.bbci.co.uk/news/technology/rss.xml \
  --output ./feeds/bbc-tech.xml \
  --max-source-items 15 \
  --score-threshold 0.4 \
  --max-items 12 \
  --source-lang en \
  --target-lang zh

This produces:

• ./feeds/bbc-tech.xml
  RSS feed where <description> combines:
  • summary_preview (short summary in target language) and
  • body_bilingual (full bilingual body, screen-sized segments).
• ./feeds/bbc-tech.json
  Sidecar JSON containing fulltext, summary_preview, body_bilingual, and scoring details including:
  • interest_score (S-shaped stretched score)
  • interest_score_raw (linear interest score)
  • final_score
  • best_cluster_id / best_cluster_weight

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Publishing feeds via Git (GitHub Pages / Gitee Pages)

clawfeedradar can optionally push the generated XML/JSON to a git repository, so that GitHub Pages or Gitee Pages can host your feed at a stable HTTPS URL.

1) GitHub Pages example

1. 创建一个公开仓库，例如：github.com/yourname/clawfeedradar-feed，在 Settings → Pages 中 启用 Pages 功能，选择：

   • Branch: gh-pages
   • Directory: / 或 feeds/（下面示例使用 feeds/）

2. 在运行 clawfeedradar 的机器上，配置好 git 访问 GitHub 的方式（SSH key 或 HTTPS+PAT）。

3. 在 .env 中添加：

   CLAWFEEDRADAR_PUBLISH_GIT_REPO=[e-mail]:yourname/clawfeedradar-feed.git
   CLAWFEEDRADAR_PUBLISH_GIT_BRANCH=gh-pages
   CLAWFEEDRADAR_PUBLISH_GIT_PATH=feeds
   

4. 之后每次运行 clawfeedradar run / schedule：

   • clawfeedradar 会在本地 ./.publish/yourname-clawfeedradar-feed/ 下维护一个 clone；
   • 将生成的 *.xml / *.json 拷贝到该 clone 的 feeds/ 目录；
   • 自动执行 git add / git commit / git push。

5. 最终订阅地址类似于：

   https://yourname.github.io/clawfeedradar-feed/feeds/bbc-tech.xml
   

2) Gitee Pages example（适合国内网络）

步骤与 GitHub 类似，只是把远端换成 Gitee：

1. 创建 Gitee 仓库，例如：gitee.com/yourname/clawfeedradar-feed，在 Pages 设置中 启用 Gitee Pages（选择对应分支和目录）。

2. 在运行环境中配置好访问 [e-mail]:... 的 SSH key。

3. 在 .env 中添加：

   CLAWFEEDRADAR_PUBLISH_GIT_REPO=[e-mail]:yourname/clawfeedradar-feed.git
   CLAWFEEDRADAR_PUBLISH_GIT_BRANCH=gh-pages
   CLAWFEEDRADAR_PUBLISH_GIT_PATH=feeds
   

4. 之后 clawfeedradar 的行为与 GitHub 情况一致：每次生成 XML/JSON 后自动 git add / commit / push 到 Gitee 仓库。

5. Gitee Pages 的订阅地址通常类似：

   https://yourname.gitee.io/clawfeedradar-feed/feeds/bbc-tech.xml
   

如未配置 CLAWFEEDRADAR_PUBLISH_GIT_REPO，clawfeedradar 仅在本地写 XML/JSON， 不会尝试推送任何远端仓库。

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Configuration (env overview)

（其余章节与之前相同，略）