Multisource Intel Radar

Build and run a high-signal information radar for C-end founders and operators across YouTube, X/Twitter, Reddit, WeChat Official Accounts, and Xiaohongshu....

Security Scan

The skill mostly matches a feed-ingest + keyword-digest tool, but the README promises non-RSS scraping (WeChat / Xiaohongshu browser search) and other behaviors that are not implemented in the included scripts, and it uses a hard-coded example local OPML path which could cause accidental reading of private files — review before running.

Purpose & Capability

The name/description (multi-source radar across RSS/OPML + non-RSS sources) largely matches the included parsing and digest scripts which handle OPML -> feeds and RSS/Atom scoring. However the skill claims support for non-RSS sources (WeChat OA, Xiaohongshu) and browser search for XHS which are not implemented in the code; that capability mismatch is unexplained but could be intended as a manual step.

Instruction Scope

SKILL.md instructs parsing a user-specific OPML default (/Users/rogeryang/Downloads/follow.opml), running browser searches for Xiaohongshu, and using watchlist files for WeChat/XHS. The provided scripts only handle OPML parsing and RSS/Atom fetching/scoring — there is no code to perform browser searches, scrape WeChat/XHS, or integrate those watchlists. The default path may cause accidental exposure of local private feeds; the instructions also leave scraping/legal/credential details unspecified.

Install Mechanism

No install spec or remote downloads; included code files are plain Python scripts with standard library usage (urllib, xml.etree). No third-party packages or external installers are pulled in.

Credentials

The skill declares no required environment variables, credentials, or config paths. The scripts make outbound HTTP(S) requests to feed URLs but do not request secrets or access other system credentials.

Persistence & Privilege

The skill does not request always:true and is user-invocable only. It does not modify other skills or claim persistent system-wide privileges.

What to consider before installing

Before installing or running: (1) Note that the code only implements OPML->feeds parsing and RSS/Atom fetching + scoring — WeChat/Xiaohongshu scraping and 'browser search' are described but not implemented; expect manual steps or additional tooling if you need those sources. (2) The SKILL.md default OPML path points to a personal home directory; check and sanitize your OPML (assets/feeds.txt) so you don't leak private feed URLs. (3) The scripts make outbound HTTP requests to up to 20 feeds (default) — review the feed list for unwanted domains and be mindful of rate limits and scraping legality for non-RSS platforms. (4) If you need automated XHS/WeChat ingestion, ask the author for details or a concrete, auditable implementation (including how credentials/session cookies are handled); avoid running opaque browser automation. (5) Run the scripts in a sandboxed environment first and inspect outputs (and feed URLs) before integrating into daily automation.

Create a founder-grade signal system: less junk, more decisions.

Inputs

• OPML file (default: /Users/rogeryang/Downloads/follow.opml)
• Keyword whitelist (default: 创业, AI, 增长, 金融)
• Optional source lists for non-RSS channels (X list links, subreddit list, WeChat/XHS accounts)

Output Contract

Always output:

1. Top 3 must-read signals (one-line why + one action + clickable source link)
2. Top 5 watchlist items (with source link)
3. Dropped noise summary (what got filtered and why)
4. Filter transparency (counts + rates: scanned -> matched -> shortlisted -> top3)
5. Next experiment (one concrete growth/ops move)

Workflow

Step 1) Ingest feed sources

• Parse OPML with scripts/parse_opml.py
• Generate normalized feed list: assets/feeds.txt

Step 2) Fetch + filter

• Run scripts/build_digest.py with keyword whitelist
• Time window default: last 48h
• Keep only items that match whitelist in title/summary
• For Xiaohongshu: do browser search (not watchlist-dependent), using keyword combos like:
  • 创业 AI 增长
  • AI 产品 复盘
  • 增长运营 案例 Then append top findings with profile/note links.

Step 3) Score items

Use this weighted scoring:

• Relevance to whitelist (40%)
• Actionability in 7 days (30%)
• Novelty / non-obviousness (20%)
• Evidence density (10%)

Step 4) Summarize for execution

For each selected item, provide:

• Core insight (1 sentence)
• Why it matters for current product
• Suggested action today (1 step)

Source Coverage Notes

• YouTube/X/Reddit often available via RSSHub or platform feeds in OPML
• WeChat OA and Xiaohongshu are often not natively RSS; add via:
  • RSS bridge links (if available)
  • Manual watchlist files (assets/wechat_watchlist.txt, assets/xhs_watchlist.txt)
• If a source has no feed, include it in watchlist and mark as manual scan required

Anti-Noise Rules

• Do not output generic motivational posts
• Drop repeated观点 without new evidence
• Prefer first-hand data / concrete case over opinion
• Keep digest under 10 items total

Daily Cadence (recommended)

• 09:30: morning digest (strategic)
• 18:30: evening digest (tactical)

Keyword Defaults

创业, AI, 增长, 金融

If user provides new keywords, merge and deduplicate.

Files

• Parser: scripts/parse_opml.py
• Digest builder: scripts/build_digest.py
• Notes: references/scoring-and-ops.md