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Upgrade

Find concrete LifeOS improvements in Anthropic updates, practitioner examples, and internal reflections. Check each candidate against the current setup, previous decisions, and compatibility, then present actionable recommendations tied to specific files or components.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Improve LifeOS from what the best practitioners are shipping around AI harnesses — Anthropic first (changelogs, docs, releases), then trusted creators, trending repos, and the system's own reflections — extracting concrete techniques and filtering them against verified current state so nothing already-done or rejected is re-recommended. USE WHEN upgrade, system upgrade, check Anthropic, new Claude features, algorithm upgrade, LifeOS upgrade, mine reflections.

연동 도구YouTubeX (Twitter)
설치 방법 보기

이럴 때 유용합니다

  • Checking Anthropic updates for LifeOS improvements
  • Finding improvements in internal reflections
  • Researching a candidate feature in more detail
  • Discovering new sources for improvement ideas

포함된 파일(10)

  • SKILL.md9.1 KB
  • References/ExampleReport.md4.8 KB
  • References/OutputFormat.md5.8 KB
  • sources.json6.5 KB
  • Tools/Anthropic.ts37.5 KB
  • Workflows/AlgorithmUpgrade.md13.0 KB
  • Workflows/FindSources.md5.2 KB
  • Workflows/MineReflections.md5.7 KB
  • Workflows/ResearchUpgrade.md5.5 KB
  • Workflows/Upgrade.md6.1 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Customization

Before executing, check for user customizations at: ~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/Upgrade/

If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.

🚨 MANDATORY: Voice Notification (REQUIRED BEFORE ANY ACTION)

You MUST send this notification BEFORE doing anything else when this skill is invoked.

  1. Send voice notification:
    curl -s -X POST http://localhost:31337/notify \
      -H "Content-Type: application/json" \
      -d '{"message": "Running the WORKFLOWNAME workflow in the Upgrade skill to ACTION"}' \
      > /dev/null 2>&1 &
    
  2. Output text notification:
    Running the **WorkflowName** workflow in the **Upgrade** skill to ACTION...
    

Upgrade

The ideal state: the system knows what the best people on the internet are saying, doing, and implementing around AI harnesses — Anthropic most importantly — and its configuration improves from that input. A run is done when every worthwhile new technique from the monitored sources has been extracted (quoted, mapped to a specific LifeOS file or component), every candidate has been checked against what the system already has or already decided, and the result reaches the user as tiered, evidence-backed recommendations they can act on immediately.

Signal comes from two directions, and a good run uses both: external (what Anthropic and the best practitioners are shipping) and internal (what the system's own reflections and failure history say is weak). The most valuable recommendations are usually where the two agree.

Workflow Routing

WorkflowTriggerFile
Upgrade"check for upgrades", "check sources", "any updates", "check Anthropic", "check YouTube", "upgrade"Workflows/Upgrade.md
MineReflections"mine reflections", "check reflections", "what have we learned", "internal improvements", "reflection insights"Workflows/MineReflections.md
AlgorithmUpgrade"algorithm upgrade", "upgrade algorithm", "improve the algorithm", "algorithm improvements", "fix the algorithm"Workflows/AlgorithmUpgrade.md
ResearchUpgrade"research this upgrade", "deep dive on [feature]", "further research"Workflows/ResearchUpgrade.md
FindSources"find upgrade sources", "find new sources", "discover channels"Workflows/FindSources.md
<!-- public issue #1750, @xmasyx — the TwitterBookmarks routing row was removed here. Workflows/TwitterBookmarks.md is release-excluded (it depends on the private _X skill), so on a public install the row advertised a route to a file that does not ship. The workflow file itself is untouched and can still be run directly; bookmark sweeps route through _X PullBookmarks (`/tb`) and the BookmarkSweep launchd service. -->

Default workflow: a bare "upgrade" or "check for upgrades" runs Upgrade (which includes reflection mining).

The Contract (what every recommendation must satisfy)

  1. Grounded in current state. No recommendation without a Prior Status tag (🆕/🔶/💬/🚫) backed by file:line evidence gathered this run. Already-implemented items go to Skipped Content with evidence — that's the proof the prior-state check ran. Rejected ideas (MEMORY/KNOWLEDGE/REJECTED/) only resurface with a named reason the context changed. This standard binds internal synthesis inference exactly as it binds external findings: before any absence-claim earns a 🆕/CRITICAL tag, the thing claimed missing must be positively probed this run (grep/read for it), never inferred. A reported absence you did not check is a fabricated finding — the 2026-08-06 scan shipped a false CRITICAL across seven skills by grepping context: fork while never grepping background:. Internal inference is exempt from nothing.
  2. A technique, not a pointer. Quote or code-block the actual content; name the exact LifeOS file or component it improves; include What It Is and How It Helps LifeOS (≤2 concrete sentences each). The test: if "show me the technique" has no answer, it doesn't ship. Content with nothing extractable goes to Skipped with a reason — skip boldly rather than dilute.
  3. Won't break what exists. Check backward compatibility against current skills, hooks, and workflows before recommending adoption.
  4. Formatted per the contract. References/OutputFormat.md is the single source of truth for section order, Prior Status legend, table columns, and hard rules.

Sources & Tools

SurfaceContract
Anthropic (30+ sources: blog, changelogs, GitHub repos, docs)bun Tools/Anthropic.ts — diffs against State/last-check.json, updates it itself
YouTube channelsConfig: youtube-channels.json (base) + user copy in CUSTOMIZATIONS. List: yt-dlp --flat-playlist --dump-json 'https://www.youtube.com/@HANDLE/videos'. Transcript: bun ~/.claude/LIFEOS/TOOLS/GetTranscript.ts '<url>'. Seen-state: State/youtube-videos.json — update after processing
GitHub trendingConfig: github_trending block in user user-sources.json. gh api 'search/repositories?q=QUERY+created:>DATE+stars:>N&sort=...&per_page=3'. Seen-state: State/github-trending.json — merge, never drop entries
Custom sourcesuser-sources.json in CUSTOMIZATIONS — fetch each; skip dead/redirected pages with a note
Claude Code internalsWhen discoveries touch hooks, settings, slash commands, MCP, agent types, or the SDK/API, spawn Agent(subagent_type="claude-code-guide") to verify against the live surface — never answer from memory
Internal reflectionsMEMORY/LEARNING/REFLECTIONS/algorithm-reflections.jsonl — method in Workflows/MineReflections.md

Source labels in output: GitHub: claude-code vX.Y.Z · YouTube: Creator @ MM:SS · Docs: Section · Blog: Title.

Gotchas

  • Hard deadline, fail-open — never block on a straggler. Set a synthesis deadline (~4 min) at dispatch; report with whatever is back when it hits. A missing source is listed as ⏳ timed out in Sources Processed; it degrades coverage, never delays the report. (2026-07-18: one hung GitHub-trending agent stalled a run ~1 hour.)
  • Right-size the fan-out (~8 agents). Small-file reads collapse into one agent; network sources get short budgets and are first to drop. Over-fan-out is the failure the reflection corpus flags most.
  • Budget the claude-code-guide freshness spawn per-spawn — it dies on a broad ask. Asking one spawn to cover the whole Claude Code surface (hook events, settings.json, slash commands, SKILL/subagent frontmatter, MCP, SDK, API) returns nothing: it fans out a batch of doc lookups on its first turn and the combined results blow its context window, surfacing as Prompt is too long ~10s after spawn. The prompt length is not the cause — a trivial prompt to the same agent type in a larger parent conversation completes fine; how much the ask makes it FETCH is the variable. Cap at ~3 areas per spawn, tell it to look things up rather than pull whole pages, and split the surface across parallel spawns so losing one costs a slice instead of the whole freshness check. Diagnostic tell: sibling agents in the same batch all succeed while this one dies — that points at the spawn's context budget, not the batch. Fallback that needs no agent at all: read Claude Code CHANGELOG from sources.json directly and diff the version range. (public PR #1659, @elhoim.)
  • Idle teammate ≠ delivered result. Spawned agents sometimes go idle without sending output — a one-line SendMessage nudge recovers them. Nudge once; don't re-spawn.
  • GitHub search 422s on bare OR between qualifiers with no free-text term. Give every query a free-text term; don't retry a 422 inside the budget.
  • Absence from settings.json ≠ a dead hook. It's GENERATED by MergeSettings; hooks also fire via dispatchers (PreToolGuard) and Pulse HTTP. Flag "verify before concluding dark," never assert dead from absence.
  • Check sources in parallel, not sequentially — the run is network-bound.

Execution Log

After completing any workflow, append a single JSONL entry:

echo '{"ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","skill":"Upgrade","workflow":"WORKFLOW_USED","input":"8_WORD_SUMMARY","status":"ok|error","duration_s":SECONDS}' >> ~/.claude/LIFEOS/MEMORY/SKILLS/execution.jsonl

Replace WORKFLOW_USED with the workflow executed, 8_WORD_SUMMARY with a brief input description, and SECONDS with approximate wall-clock time. Log status: "error" if the workflow failed.

리뷰

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같은 저장소의 스킬

Compare a component's own design needs with the goals of the larger system through three structured passes. The result identifies conflicts, recommends an appropriate scope, and assesses whether the proposed design fits the whole.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Choosing between a service, a library, and inline code
  • Comparing feature requests with product goals
  • Checking how new components fit an existing system
danielmiessler/LifeOS2만2026년 9월 4일 업데이트

Aphorisms

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Find quotes that fit a text’s themes, with attribution and a short reason for each suggestion. Add sourced quotes, research thinkers, and organize a quote collection by theme and usage history to avoid repetition.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Finding a fitting quote for a newsletter
  • Saving sourced quotes without duplicates
  • Researching a thinker’s quotes by theme
danielmiessler/LifeOS2만2026년 9월 4일 업데이트

Apify

무료

Collect social posts, business listings, and product reviews through Apify actors, ready-made data collection tools. Filter results by date, rating, or engagement in code before sending relevant records to the AI for research or lead generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Find competitors' most popular social posts
  • Build a contact list of local businesses
  • Extract recent negative Amazon reviews
danielmiessler/LifeOS2만2026년 9월 4일 업데이트

Art

무료

Create blog illustrations, technical diagrams, comparisons, comics, and icons through workflows tailored to each format. The skill guides composition, color, and model selection, then stages generated images for review before they are used in a project.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Creating blog header illustrations
  • Explaining a system with a technical diagram
  • Visualizing comparisons and timelines
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ArXiv

무료

Find arXiv papers by topic, research category, or paper ID. Retrieve titles, authors, abstracts, and links, check new submissions, and add AlphaXiv's AI-generated overviews when available to help decide which papers to read.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Checking new AI and machine learning papers
  • Finding papers on a specific research topic
  • Looking up a paper by its ID or arXiv URL
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Clean spoken recordings by transcribing words, identifying filler and false starts, and joining cuts with short audio fades. Preview proposed edits, reduce breaths, repair noise-gate clicks, and optionally use Cleanvoice for additional cloud processing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Removing filler words and long silences from a podcast
  • Reviewing proposed edits before applying them
  • Repairing clicks caused by a noise gate
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