everscout

imageA scout with binoculars on a hilltop at sunrise overlooking a valley of many small glowing village squares, a field notebook in hand

Name a subject and get a scout for it. Everscout keeps up with any subject by reading the communities where people make and discuss it, and asks the makers themselves when they're happy to talk.

Say "set up a scout for vintage synth restoration" (or container gardening, or Formula 1 aerodynamics). The everscout-beat skill asks you up to three questions: what's in scope, what you want to learn, and whether you want to talk to makers. Then it researches where that subject lives online, fetches every candidate community and feed to check it's real and active, and writes the scout, called a beat. You can have as many beats as you like.

A beat covers one subject: AI video, global economics, down-tempo music, whatever you follow. Everscout reads that beat's subreddits, forums, Hacker News, Bluesky, Mastodon, Lemmy, newsletters, YouTube channels, GitHub releases, arXiv and Hugging Face at each platform's polite pace. It writes one short paraphrased note per thread worth keeping and counts the tools, models, artists or indicators those notes name. From that it writes a monthly radar of what is new, rising and fading, and a sourced history-and-guide for the subject. When a maker posts their work, it drafts a question in your voice. You approve it and paste it yourself. And you can interrogate it like a reporter on the beat: ask it anything about the subject, and it answers from what it has already read, fills the gaps from the beat's communities and the web, and tells you what it saw before, what it just found, where sources disagree and how sure it is.

It is a Claude Code plugin: five skills, a standard-library Python CLI and a markdown knowledge base. The skills are evergreen units, so their research refreshes on a schedule instead of going stale.

The five skills

Starter beats

The starter beats are examples of what a generated beat looks like, and a quick start if one happens to be your subject. beats/ai-video (a maker community, open about AI), beats/global-economics (analysts and data, strict forums), beats/downtempo (musicians, hostile to AI music) beats/tech-hiring (candidates, hiring managers and labour data; built from scratch by everscout-beat on 2026-09-26 as a test) and beats/personal-sites (how tech and design people build their own websites and blogs; added 2026-09-27) were picked because they differ, and their sources were checked on the day they were added. beats/_template is what beat-new copies. Copy a starter beat into your private beats folder to make it yours; a private beat shadows a built-in one with the same name.

Where your data lives

Nothing personal goes in this repository. python scripts/everscout.py where prints the paths.

• ~/.everscout/config.json: your contact for the User-Agent, your Reddit name, paths, pace overrides. Start from everscout.config.example.json.
• ~/.everscout/voice.md: how you write and what is true about you, so drafts sound like you and never invent a persona. Start from kb/voice-template.md.
• ~/.everscout/beats/: your private beats.
• Data folder per beat (data_root/<slug> or a path per beat): notes, reports, study, signals, your journal and feedback. It works fine inside an Obsidian vault or a private git repo.
• One engagement ledger for all beats, because pacing belongs to your account and not to a subject.
• A local cache, purged after 48 hours.

How it reads

Feeds and documented public endpoints only, no keys needed:

Every request names everscout and your contact. Threads (post plus comments) can be read from Reddit, HN, Bluesky, Mastodon, Lemmy and Discourse. X, Threads, Instagram, TikTok, Discord and Telegram aren't read at all, and neither is anything a platform forbids scraping (Bandcamp, for one). kb/platforms.md has what each platform allows as of 2026-09-26.

How it engages

Everscout never posts. It drafts, you edit and approve the exact text, and you paste it. The ledger records every draft so the pace holds: an hour between comments, four a day per platform, one per community a day, never the same person twice. The full conduct code is kb/conduct.md. Its main lines:

• You're the author. No personas, no claims about you that your voice file doesn't support.
• Ask how something was made. Never ask "is this AI?"
• Where a community bans AI-written comments (Hacker News does), you get ideas, not a draft.
• Read the work, not the person. No profiling, no DMs, no links, no self-promotion.
• Thank people who answer.

The rules come from research into platform policies, the 2025 r/changemyview AI experiment, internet research ethics and studies of what gets answers. The research notes are in ai-docs/research/.

The CLI

python scripts/everscout.py <command>. Python 3.9 or later, standard library only.

Statistics

Each beat can keep a few numbers over time, so a growing data pool can be charted. The catalog is metrics.md in the beat folder: one row per metric with the research question it answers, a definition, unit, source, method, cadence, an Admiralty source grade (B2), a status and a version. Values go to DATA/stats/series.csv, one long-form CSV per beat (date,metric,version,value,unit,source,note_ref) that is only ever appended to.

• Sources: the beat's own tallies (tally:notes, tally:<facet>/<entity>, optionally as a share), GDELT news volume (gdelt: share, gdelt-raw: counts), Wikimedia pageviews (wikipedia:en.wikipedia/<Article>), a metric derived from two others, or a number entered by hand from a release. All keyless; Google Trends stays out because the only routes are scrapers.
• Lifecycle: new metrics are candidates. stats review proposes promotion after three values from a source graded C3 or better (you say yes, now or in advance with stats approve, after which it is promoted automatically once ready), and archiving when a metric is stale (collection failed for three periods, or no value for three periods) or flat (under 5% variation over eight values). You can archive one as irrelevant, gamed or superseded. Nothing is deleted. A definition change bumps the version, and charts split the series there. The thresholds can be changed under stats in config.json.
• Charts: stats export writes DATA/stats/export.csv. When chartwright is installed (found next to this repository, in the Claude Code plugin cache, or at $EVERSCOUT_CHARTWRIGHT), it prints the commands for a line chart, terminal sparklines and small multiples, and --charts builds them into DATA/reports/charts/. Without chartwright the CSV opens in any spreadsheet.

The starter beats ship two or three candidate metrics each. kb/stats.md is what the skills follow; the research is ai-docs/research/2026-09-27-beat-statistics.md.

Method

Everscout follows netnography (Kozinets 2020) for the stages and horizon scanning for the trend layer. A signal is one dated observation, and a trend needs three independent signals across two reports. Signals are scored on novelty, velocity, breadth, evidence and relevance. Reports use the ThoughtWorks radar rings: Adopt, Trial, Assess, Hold. Counting follows the LLM-wiki pattern: one note per thread, entities resolved through aliases in code, weight by comment count with time decay. URLs in reports have to come from notes or from pages logged as read; lint catches the rest. kb/method.md explains each step.

Install

claude plugin marketplace add https://github.com/m4bwav/everscout
claude plugin install everscout@everscout --scope user

Then run python <plugin dir>/scripts/everscout.py where, write ~/.everscout/config.json with at least contact, and say "set up a scout for " or "scan the ai-video beat". Other agents (Copilot, Codex, Cursor) can use the five skill folders directly; AGENTS.md has the rules for working in the repository.

Tests

python -m unittest discover -s tests runs offline against synthetic fixtures and also checks every built-in beat. Skill evals live in each skill's evals/evals.json and run through the evergreen plugin's evergreen-test.

Privacy

The CLI, the skills and the knowledge base run on your own machine. Your config, voice file and private beats stay in ~/.everscout/, and notes, reports, statistics and the engagement ledger stay in the data folders your config names. Fetched pages are cached locally and the cache is purged after 48 hours. Nothing is uploaded to the author or to any everscout service, because there is none. The plugin has no hooks, no MCP server and no telemetry.

Scanning a beat is network traffic by design. The CLI sends plain HTTP GET requests, with no login, cookies or API keys, to the hosts on that beat's watch list. Those can be Reddit (www.reddit.com Atom feeds and search), Hacker News through its Algolia API (hn.algolia.com), Bluesky (bsky.app and public.api.bsky.app), any Mastodon, Lemmy or Discourse server a beat names, YouTube channel feeds (www.youtube.com), GitHub release feeds (github.com), arXiv (export.arxiv.org and rss.arxiv.org), Hugging Face (huggingface.co), Google News search feeds (news.google.com), and any RSS or Atom feed URL a beat lists, such as a newsletter, blog or podcast. Statistics collection also queries GDELT (api.gdeltproject.org) and Wikimedia pageviews (wikimedia.org). Each request carries its URL, which holds the beat's search terms. It also carries a User-Agent naming everscout, its version and the contact you set in config.json; Reddit requests also carry the Reddit user name you set there. Like any web visit, each host sees your IP address. Each beat's sources.md is its watch list, and python scripts/everscout.py sources --beat <slug> prints every feed URL the beat reads without fetching anything.

The skills also ask your agent to use its own web search and web fetch tools for beat research, the beat's web searches and pages the CLI has no reader for. Those requests go through your agent and fall under its privacy terms. Everscout never posts or sends messages: comment drafts are shown to you, and you paste them yourself.

Everscout reads no credentials, tokens or API keys. The only environment variables it reads are EVERSCOUT_HOME, EVERSCOUT_DATA, EVERSCOUT_LOCAL and EVERSCOUT_CHARTWRIGHT, all of them local paths. When chartwright is installed, stats export --charts runs it on your machine to draw charts. Questions or problems go to the issue tracker.

Prior art

last30days-skill (engagement-ranked briefs across many sources) is the closest cousin; everscout differs in keeping memory across runs, tallying a fixed vocabulary, and engaging under a conduct code. F5Bot and Syften showed that boolean keyword filters are enough for alerts. GummySearch showed what happens to a tool built on Reddit's paid API. ai-docs/research/2026-09-26-prior-art.md has the full survey.

License

MIT.