Agent skill

Blogwatcher

by AlexAI-MCP in AlexAI-MCP/hermes-CCC

Monitor and summarize blog posts, RSS feeds, and web content for research and staying current with topics.

MITAuto-check passedWriting & Content

Install Blogwatcher

skills CLI
$ npx skills add AlexAI-MCP/hermes-CCC --skill blogwatcher -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install AlexAI-MCP/hermes-CCC blogwatcher --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/AlexAI-MCP/hermes-CCC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/blogwatcher .claude/skills/blogwatcher && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
blogwatcher
GitHub stars
135
Token cost
~1.2k tokens
SKILL.md length
458 words
Files
1
Skills in repo
44
Repo updated
First seen
Licence
MIT

At a glance

Monitor and summarize blog posts, RSS feeds, and web content for research and staying current with topics.

  • Works in 6 steps: Define a small set of feeds by topic. → Fetch them on a schedule. → Normalize fields into one list. → …
  • Tasks that involve Blog and article writing
  • SKILL.md covers Purpose, Install, Parse a Feed and Access Entry Fields, plus 9 more sections
  • Calls pip

What it does

Blogwatcher is an agent skill from AlexAI-MCP/hermes-CCC. Monitor and summarize blog posts, RSS feeds, and web content for research and staying current with topics.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Writing & Content, covering Blog and article writing. It works with arXiv. The repository describes itself as: Hermes Agent ported to Claude Code Channel — 46 native skills, no OAuth, no external process. The licence is MIT.

When your agent uses it

  • Tasks that involve Blog and article writing

Example prompts

  • “/blogwatcher”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Define a small set of feeds by topic.
  2. Fetch them on a schedule.
  3. Normalize fields into one list.
  4. Filter by keyword and freshness.
  5. Ask Claude to synthesize the daily or weekly signal.
  6. Save the resulting digest for later reference.

What it can do on your machine

Read from SKILL.md and the folder at commit 8107e89. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Blogwatcher loads about 1.2k tokens when it runs. Until then it costs about 30 tokens; SKILL.md has 458 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~30
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from AlexAI-MCP/hermes-CCC at commit 8107e89, republished under its MIT licence (© AlexAI-MCP). 458 words, ~1,205 tokens.

Download SKILL.mdSave it as .claude/skills/blogwatcher/SKILL.md (or your agent's skills folder).
name
blogwatcher
description
Monitor and summarize blog posts, RSS feeds, and web content for research and staying current with topics.
version
1.0.0
author
hermes-CCC (ported from Hermes Agent by NousResearch)
license
MIT

Blogwatcher

Purpose

  • Use this skill to monitor RSS feeds and blog-style content for ongoing research.
  • It is useful for staying current on AI, ML, engineering, and product ecosystems.
  • Prefer RSS when you want structured updates without scraping full websites every time.

Install

bash
pip install feedparser

Parse a Feed

python
import feedparser

feed = feedparser.parse("https://example.com/rss")
print(feed.feed.title)
print(feed.entries[0].title)
  • feedparser handles RSS and Atom feeds.
  • The parsed object exposes feed metadata and a list of entries.

Access Entry Fields

python
entry = feed.entries[0]
print(entry.title)
print(entry.summary)
print(entry.link)
print(entry.published)
  • The most commonly useful fields are .title, .summary, .link, and .published.
  • Not every feed includes every field, so code defensively.

Batch Fetch Multiple Feeds

python
import feedparser

feeds = [
    "https://example.com/rss",
    "https://another.example/feed.xml",
]

all_entries = []
for url in feeds:
    parsed = feedparser.parse(url)
    for entry in parsed.entries:
        all_entries.append(
            {
                "source": parsed.feed.get("title", url),
                "title": entry.get("title", ""),
                "summary": entry.get("summary", ""),
                "link": entry.get("link", ""),
                "published": entry.get("published", ""),
            }
        )
  • Batch fetches are the standard pattern for topic monitoring.
  • Normalize fields early so downstream summarization stays simple.

Filter by Date or Keyword

  • Filter by date when you only care about the most recent week or month.
  • Filter by keyword when watching narrow topics like agents, evals, or multimodal.
  • Keep the filter stage simple and deterministic.

Example:

python
keywords = ["llm", "agents", "retrieval"]
filtered = [
    e for e in all_entries
    if any(k.lower() in (e["title"] + " " + e["summary"]).lower() for k in keywords)
]

Summarize With Claude

  • Extract the title and summary from each entry.
  • Feed the collected set into Claude and ask for synthesis by theme, signal, and novelty.
  • This works better than summarizing one feed item at a time when you are tracking a field.

Example prompt shape:

text
Summarize these AI research updates. Group them into model releases, tooling, benchmarks, and policy. Highlight what appears genuinely new.
  • Keep the raw title, summary, link, and publication date in the source material.
  • Ask for a synthesis, not a rewrite.

Save Results to a File

python
import json

with open("feed_digest.json", "w", encoding="utf-8") as f:
    json.dump(filtered, f, ensure_ascii=False, indent=2)
  • Save structured digests for later comparison.
  • JSON is the easiest format for later reprocessing.
  • Markdown is convenient if the output is intended for direct reading.

Common AI and ML Blogs To Monitor

  • arXiv Sanity

  • Hugging Face Blog

  • OpenAI

  • Anthropic

  • engineering blogs from inference providers, vector DB vendors, and cloud platforms

  • Some sources are better via RSS.

  • Others may require periodic scraping or newsletter ingestion.

Show full SKILL.md (178 more words)Show less

Combine With /arxiv

  • Use this skill for blog and announcement monitoring.
  • Combine it with /arxiv for paper discovery and academic monitoring.
  • The combination gives better coverage across research papers, product launches, and engineering writeups.

Good Monitoring Workflow

  1. Define a small set of feeds by topic.
  2. Fetch them on a schedule.
  3. Normalize fields into one list.
  4. Filter by keyword and freshness.
  5. Ask Claude to synthesize the daily or weekly signal.
  6. Save the resulting digest for later reference.

Practical Notes

  • RSS coverage varies widely by site.
  • Feed summaries are often enough for triage but not enough for deep analysis.
  • Follow links for the few items that survive filtering.
  • Save source links alongside the synthesis so claims remain traceable.

Summary

  • Install RSS parsing support with pip install feedparser.
  • Parse feeds with feedparser.parse("https://example.com/rss").
  • Access fields like feed.entries[0].title, .summary, .link, and .published.
  • Batch fetch multiple feeds, then filter by date or keyword.
  • Summarize title and summary pairs with Claude for cross-source synthesis.
  • Save digests to a file and combine this workflow with /arxiv for broader research monitoring.

© AlexAI-MCP, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/blogwatcher of AlexAI-MCP/hermes-CCC.

Open the folder on GitHubat commit 8107e89

Compare with similar skills

Blogwatcher next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Blogwatcher compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Blogwatcher this skillAlexAI-MCP/hermes-CCC135—~1.2kAutomated safety check: PassMIT
SepiaNanako0129/sepia3.1k—~3.6kAutomated safety check: PassMIT
Sepiascott-fryxell/brayness125—~2.5kAutomated safety check: PassMIT
Paper Interpretationdigoal/blog8.6k—~1.5kAutomated safety check: PassGPL-2.0
News Aggregator Skillcclank/news-aggregator-skill1.3k—~2.1kAutomated safety check: PassNone
Blog FactcheckAgriciDaniel/claude-blog2.3k—~2.2kAutomated safety check: PassMIT

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Works with

Questions about Blogwatcher

What does Blogwatcher do?

Monitor and summarize blog posts, RSS feeds, and web content for research and staying current with topics. Blogwatcher is an agent skill from AlexAI-MCP/hermes-CCC. Monitor and summarize blog posts, RSS feeds, and web content for research and staying current with topics.

When should I use Blogwatcher?

Blogwatcher fits situations like: tasks that involve Blog and article writing.

How do I install Blogwatcher in Claude Code?

Run `npx skills add AlexAI-MCP/hermes-CCC --skill blogwatcher -a claude-code`. Or copy the skill folder (skills/blogwatcher in AlexAI-MCP/hermes-CCC) into .claude/skills/blogwatcher in your project. Claude Code loads it when a task matches its description.

How do I install Blogwatcher in Codex?

Run `npx skills add AlexAI-MCP/hermes-CCC --skill blogwatcher -a codex`. Or copy the skill folder (skills/blogwatcher in AlexAI-MCP/hermes-CCC) into .agents/skills/blogwatcher in your project. Codex loads it when a task matches its description.

Can I use Blogwatcher in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add AlexAI-MCP/hermes-CCC --skill blogwatcher -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/blogwatcher, .gemini/skills/blogwatcher, .github/skills/blogwatcher and .opencode/skills/blogwatcher in your project.

What does Blogwatcher need to run?

Going by SKILL.md and its folder, Blogwatcher needs the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Blogwatcher access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Blogwatcher safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Blogwatcher use?

Blogwatcher is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Blogwatcher use?

About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Blogwatcher?

Skills that share tags, products or a category with Blogwatcher: Sepia (Nanako0129/sepia, 3.1k stars), Sepia (scott-fryxell/brayness, 125 stars), Paper Interpretation (digoal/blog, 8.6k stars) and News Aggregator Skill (cclank/news-aggregator-skill, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Blogwatcher?

AlexAI-MCP (a GitHub user) maintains it in AlexAI-MCP/hermes-CCC, which has 135 GitHub stars. The repository holds 44 skills in this directory. The repository was last updated on April 8, 2026.

Source: AlexAI-MCP/hermes-CCC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.