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Pareto

Survey a topic across YouTube, GitHub, the web, and the Research skill, then rank findings by relevance, reach, recency, and distinctiveness. The report summarizes how thinking has changed, what is mainstream now, and which people or sources appear to be moving ahead, with links for frontier claims.

OriginalShow the original description

Maps any topic's zeitgeist and frontier — sweeps YouTube, GitHub, the web and the Research skill, ranks everything down to the vital few by relevance, reach, recency and outlier-ness, then reports the past meta, the current mainstream meta, and where the forward thinkers are heading, every frontier claim linked. USE WHEN pareto, pareto this, run pareto on, zeitgeist, what's the zeitgeist on, what's the meta on, what is the current meta, where is thinking on X heading, who are the forward thinkers on, who is ahead of the curve on, what are the big thinkers saying about, top content on a topic, vital few. NOT FOR answering a specific factual question (Research), analyzing one piece of content (ExtractWisdom), or AI industry news digests (Upgrade NewsDigest; security news SecurityUpdates).

danielmiessler/LifeOS19,636 stars(GitHub 19,636 + cccskills 0)
Works withYouTubeGitHub

Run score Not tested

See how to install

Good for

  • Mapping the current conversation around a topic
  • Finding influential sources and people to follow
  • Comparing past and current views on a field
  • Exploring where thinking on a topic may be heading

Overview

What it does

Pareto gathers information about a topic from YouTube, GitHub, the web, and the Research skill. It ranks relevant material by factors such as reach, recency, and distinctiveness, then narrows the results to the most useful findings.

The report describes earlier views, the current mainstream, people or sources that appear to be ahead, and possible directions for the field. Claims about emerging trends include links so readers can check the supporting sources. If one source is unavailable, the search continues with the others and identifies the source that failed.

When it helps

Use Pareto when you want to quickly understand which ideas are repeated across a field and which developments are new. It can help you find articles and videos worth reading, identify people to follow, or see how the dominant thinking has changed over time.

Example requests

  • “Summarize current trends in agentic coding.”
  • “Where are people ahead of the curve on personal AI infrastructure?”

Notes

YouTube search requires a YOUTUBE_API_KEY, and web search requires a PERPLEXITY_API_KEY. GitHub search uses an authenticated gh CLI. YouTube has an API quota, so results from that source may be missing when the limit is reached.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Document check

AI checked the SKILL.md for operations that need care and for setup requirements. The score comes only from actually running the skill (hands-on test).

SafetyAre sending data, deleting files, and running commands disclosed?
AGoodIt reads credentials from environment variables and performs searches.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingTwo API keys and authenticated gh CLI are required; setup is documented in Setup.md.

Not counted in the score. Rated by AI (OpenAI Codex) as of Oct 28, 2026. Treat it as a guide and read the original before installing.

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Files included (9)

  • SKILL.md5.5 KB
  • Intents/Run.md4.6 KB
  • Setup.md3.0 KB
  • Tools/Check.help.md629 B
  • Tools/Check.ts3.8 KB
  • Tools/Collect.help.md923 B
  • Tools/Collect.ts10.1 KB
  • Tools/Rank.help.md989 B
  • Tools/Rank.ts9.0 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Pareto

Most of what gets said about a topic is repetition. A few people and a few pieces carry the signal, and some of them are early. Pareto sweeps wide, ranks down to that vital few, and reports how thinking has moved and where it is going next.

Ideal State

  • The principal can act on a run without redoing the search: who to follow and what to read first are named, each with a link from the collected data.
  • A failed or quota-limited source narrows the sweep but never aborts the run, and the result names every source that failed.
  • Ranking keeps the vital few on topic: off-topic viral items and cracked-software lure repos never reach the ranked set.
  • Every era claim (past, current, frontier) carries a date taken from the source itself, never a crawl date.

Intent Routing

IntentTriggerFile
Runany topic: "pareto X", "what's the zeitgeist on X", "where are the forward thinkers going on X"Intents/Run.md
FileRead when
Tools/<Name>.help.mdcalling or changing Collect (the sweep), Rank (scoring to the vital few) or Check (the report gate): flags, env, output shape
Setup.mdfirst-time setup: the four sources, the keys and accounts each needs, and a read-only sweep to prove them

Data & Config

Credentials are env var names only, read from the LifeOS .env: YOUTUBE_API_KEY (YouTube Data API v3), PERPLEXITY_API_KEY (web search). GitHub uses the authenticated gh CLI.

First-time setup for these sources is in Setup.md.

User customizations live at <configRoot>/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/Pareto/ (configRoot is $CLAUDE_CONFIG_DIR when set, else ~/.claude). When that directory exists, its PREFERENCES.md (for example default sources, sweep size, or sources to skip) overrides the defaults in Intents/Run.md; when it is absent, the defaults stand.

Gotchas

  • YouTube search costs 100 quota units per call out of a 10,000 daily default. One run makes four searches plus cheap stats calls, so about 25 full runs a day before quota errors; a quota error shows as a failed youtube source, never a failed run.
  • A viral hit also beats its channel baseline (a 3M-view video on a 50k channel), so outlier alone flagged 73 of 100 items as frontier on the first real run. A frontier candidate needs both measured reach outside the top third of its source and outlier ≥ 0.6; sources with no reach signal never qualify except Research picks.
  • GitHub search for a software category returns cracked-software lure repos ("SketchUp-Pro-Download-2026", "ZBrush-Activator"; second real run, 3D modeling, 2026-09-25). These are malware bait. Rank drops repos whose names match the lure pattern or that have under 10 stars; never link one in a report.
  • One shared word let off-topic content in: "3D modeling software" admitted 3D-printing Shorts on "3d" alone. Items must contain 60% of the topic terms (both of two, two of three). Phrase topics as the category ("3D modeling software"), not as a judgment ("best 3D modeling system"), because "best" and "system" match everything.
  • Percentiles are computed over on-topic items only. A viral off-topic video once took the top outlier slot from the real one in testing; the relevance floor runs before any scoring.
  • The "top-reach" line in Check is the top third of reach within a source. A decile cut never fires on small sources.
  • Perplexity last_updated is the crawl date, not the publish date. Used as a fallback it dated a 2025-09 arXiv paper to 2026-09 (first real run, 2026-09-25). The collector now leaves such items undated; date claims in the report come from the page itself.
  • YouTube transcripts come from the Gemini watch pass on the public URL, never from scraped captions. A video the watch pass refuses is named in the report as unread; the run never retries it through another route.

Examples

Example 1: A topic sweep

User: "Pareto agentic coding"
→ Run intent
→ ~/Downloads/pareto-agentic-coding-<date>.md: past meta, current meta, frontier, who to follow

Example 2: Phrased as a question

User: "Where are the big thinkers going on personal AI infrastructure?"
→ Same intent; the console summary leads with the one-sentence shift and two names

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