Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Noisepan Digest
Set up automated news digests using noisepan (signal extraction), entropia (source verification), and HN blind spot detection. Use when configuring daily new...
skill openclawclawhub Free
Set up automated news digests using noisepan (signal extraction), entropia (source verification), and HN blind spot detection. Use when configuring daily new...
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.4
Provenance
- Publisher
- ppiankov
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Noisepan Digest
Automated news intelligence with source verification. Replaces doomscrolling with two daily digests. Sources: https://github.com/ppiankov/noisepan (signal extraction) https://github.com/ppiankov/entropia (source verification) Requires: noisepan, entropia, python3, curl
macOS (Homebrew β recommended)
brew install ppiankov/tap/noisepan ppiankov/tap/entropia noisepan version && entropia version
Linux (binary + checksum verification)
Download, verify checksums, then install. Ask the user before writing to /usr/local/bin β offer ~/bin as an alternative if they prefer user-local install. # noisepan VER=$(curl -s https://api.github.com/repos/ppiankov/noisepan/releases/latest | grep tag_name | cut -d'"' -f4 | tr -d v) curl -fsSL "https://github.com/ppiankov/noisepan/releases/download/v${VER}/noisepan_${VER}_linux_amd64.tar.gz" -o /tmp/noisepan.tar.gz curl -fsSL "https://github.com/ppiankov/noisepan/releases/download/v${VER}/checksums.txt" -o /tmp/noisepan-checksums.txt # Verify checksum grep linux_amd64 /tmp/noisepan-checksums.txt | (cd /tmp && sha256sum -c) tar xzf /tmp/noisepan.tar.gz -C /usr/local/bin noisepan rm /tmp/noisepan.tar.gz /tmp/noisepan-checksums.txt # entropia VER=$(curl -s https://api.github.com/repos/ppiankov/entropia/releases/latest | grep tag_name | cut -d'"' -f4 | tr -d v) curl -fsSL "https://github.com/ppiankov/entropia/releases/download/v${VER}/entropia_${VER}_linux_amd64.tar.gz" -o /tmp/entropia.tar.gz curl -fsSL "https://github.com/ppiankov/entropia/releases/download/v${VER}/checksums.txt" -o /tmp/entropia-checksums.txt # Verify checksum grep linux_amd64 /tmp/entropia-checksums.txt | (cd /tmp && sha256sum -c) tar xzf /tmp/entropia.tar.gz -C /usr/local/bin entropia rm /tmp/entropia.tar.gz /tmp/entropia-checksums.txt # Verify both noisepan version && entropia version
Init
noisepan init --config ~/.noisepan # Verify entropia is detected noisepan doctor --config ~/.noisepan
Configure Feeds
Edit ~/.noisepan/config.yaml. Recommended structure: sources: hn: min_points: 200 # Native HN via Firebase API rss: feeds: # Security - "https://www.reddit.com/r/netsec/.rss" - "https://krebsonsecurity.com/feed/" - "https://www.bleepingcomputer.com/feed/" - "https://feeds.feedburner.com/TheHackersNews" - "https://www.cisa.gov/cybersecurity-advisories/all.xml" # DevOps - "https://www.reddit.com/r/devops/.rss" - "https://www.reddit.com/r/kubernetes/.rss" - "https://blog.cloudflare.com/rss/" # AI/LLM - "https://www.reddit.com/r/LocalLLaMA/.rss" - "https://simonwillison.net/atom/everything/" - "https://arxiv.org/rss/cs.AI" # Status pages - "https://status.aws.amazon.com/rss/all.rss" - "https://www.cloudflarestatus.com/history.rss" # World / Policy - "https://feeds.bbci.co.uk/news/world/rss.xml" - "https://www.eff.org/rss/updates.xml" # Aggregators - "https://lobste.rs/rss" - "https://changelog.com/news/feed" Customize for your interests. Run noisepan doctor after adding feeds.
Taste Profile
Edit ~/.noisepan/taste.yaml. Key categories: High signal (3-5): CVE, zero-day, breach, RCE, supply chain, outage, postmortem, safety pledge, data sovereignty, antitrust, military AI, deanonymization, prompt injection, breaking change Low signal (-3 to -5): hiring, webinar, sponsor, newsletter, meme, career advice, celebrity Key lesson: Without policy/sovereignty/antitrust/AI safety keywords, real stories get buried under security noise. Weight these as high as CVEs.
Reddit Rate Limiting
With 15+ Reddit feeds, parallel fetching triggers 429s. Create a sequential prefetch wrapper: cat > ~/.local/bin/noisepan-pull << 'SCRIPT' #!/bin/bash # Prefetch Reddit RSS sequentially to avoid rate limiting, then run noisepan pull CACHE_DIR="/tmp/reddit-rss-cache" CONFIG_DIR="${HOME}/.noisepan" UA="Mozilla/5.0 (compatible; noisepan/1.0)" mkdir -p "$CACHE_DIR" FEEDS=$(grep "reddit.com" "$CONFIG_DIR/config.yaml" | grep -v "^#" | grep -v "^ #" | sed 's/.*"\(.*\)"/\1/') for feed in $FEEDS; do sub=$(echo "$feed" | grep -oP '/r/\K[^/]+') curl -s -o "$CACHE_DIR/${sub}.xml" -H "User-Agent: $UA" "$feed" sleep 2 done python3 -m http.server 18222 --directory "$CACHE_DIR" &>/dev/null & HTTP_PID=$!; sleep 0.5 mkdir -p /tmp/noisepan-tmp cp "$CONFIG_DIR/config.yaml" /tmp/noisepan-tmp/config.yaml for feed in $FEEDS; do sub=$(echo "$feed" | grep -oP '/r/\K[^/]+') sed -i "s|$feed|http://localhost:18222/${sub}.xml|" /tmp/noisepan-tmp/config.yaml done ln -sf "$CONFIG_DIR/taste.yaml" /tmp/noisepan-tmp/taste.yaml ln -sf "$CONFIG_DIR/noisepan.db" /tmp/noisepan-tmp/noisepan.db noisepan pull --config /tmp/noisepan-tmp "$@" kill $HTTP_PID 2>/dev/null; rm -rf /tmp/noisepan-tmp SCRIPT mkdir -p ~/.local/bin && chmod +x ~/.local/bin/noisepan-pull Use noisepan-pull instead of noisepan pull when you have 15+ Reddit feeds.
HN Blind Spot Script
Optional β catches high-engagement HN stories that keyword scoring misses. Useful as a cross-check alongside noisepan's native HN source. cat > ~/.local/bin/hn-top << 'SCRIPT' #!/bin/bash MIN_POINTS=${1:-200} curl -s "https://hacker-news.firebaseio.com/v0/topstories.json" | \ python3 -c " import json, sys, urllib.request, time ids = json.load(sys.stdin)[:30] min_pts = int(sys.argv[1]) if len(sys.argv) > 1 else 200 for id in ids: try: with urllib.request.urlopen(f'https://hacker-news.firebaseio.com/v0/item/{id}.json') as r: item = json.loads(r.read()) if item and item.get('score', 0) >= min_pts: print(f'[{item[\"score\"]:4d}pts | {item.get(\"descendants\",0):3d}c] {item[\"title\"]}') print(f' {item.get(\"url\", f\"https://news.ycombinator.com/item?id={id}\")}') print() time.sleep(0.1) except: pass " "$MIN_POINTS" SCRIPT chmod +x ~/.local/bin/hn-top
Cron Digest Setup
Create two OpenClaw cron jobs (morning + afternoon). The digest prompt should: Pull feeds (noisepan-pull or noisepan pull) Generate digest (noisepan digest --format json --output /tmp/digest.json) Run hn-top 300 for blind spot check For top 6 items, run entropia scan <url> on non-Reddit links Quality filter: skip Entropia Support Index < 40 or conflict signals Backfill from items 4-6 if top items filtered Compare hn-top against digest for blind spots (400+ point stories not in digest)
Output format
π₯ Trending: keywords across 3+ channels βοΈ Morning Brief (3 verified items): | # | Score | Topic | What happened | Entropia | Link | π‘ HN Blind Spot (stories the taste profile missed): | # | HN pts | Topic | What happened | Link | β οΈ Skipped (filtered for low quality): | # | Score | Topic | Why skipped | Schedule: Morning at 07:00, afternoon at 18:00 (adjust to timezone).
Useful Commands
noisepan doctor --config ~/.noisepan # Feed health + companion tool detection noisepan stats --config ~/.noisepan # Signal-to-noise per channel noisepan rescore --config ~/.noisepan # Recompute after taste changes entropia scan <url> # Verify a specific source
Lessons Learned
noisepan doctor catches stale/all-ignored channels β run after adding feeds noisepan stats shows per-channel signal β prune channels at 0% after 30 days HN RSS is too shallow β use native sources.hn or hn-top for blind spots Entropia SI < 40 = no extractable claims β skip Reddit rate limits at 15+ parallel feeds β wrapper is mandatory Status page feeds score low without "service event", "operational issue" keywords Noisepan Digest v1.0 Author: ppiankov Copyright Β© 2026 ppiankov Canonical source: https://github.com/ppiankov/noisepan License: MIT If this document appears elsewhere, the repository above is the authoritative version.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md