Other

Upgrade

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

05
Workflows
01
Tool
00
References
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Triggers

The Problem

Knowing what to work on next is harder than doing the work. Generic AI can summarize what's new in an ecosystem, but it has no idea where you actually are, what you're trying to achieve, or whether a shiny new technique closes any real gap in your setup. The result is a list of interesting things with no signal about what matters — or worse, recommendations for things you already built. Most upgrade advice is source-driven: here's what came out this week. But what came out this week and what you should build next are almost never the same question.

How This Skill Approaches It

The skill runs four parallel threads that converge on ranked, personalized recommendations. Thread 0 audits prior work — Algorithm runs, ISAs, KNOWLEDGE, hooks, skills — so nothing already shipped gets re-recommended. Thread 1 reads your TELOS goals, IDEAL_STATE dimensions, and CURRENT_STATE/INFRASTRUCTURE.md to compute gap tuples (metric, current, target, direction) per goal — this is the load-bearing input. Thread 2 pulls from 30+ external sources: Anthropic blog, changelogs, GitHub trending, YouTube channels. Thread 3 mines algorithm-reflections.jsonl for recurring friction patterns. Recommendations are ranked by impact × ease × confidence, where impact comes from how many active TELOS goals a change touches and how far it moves their gap. Every recommendation includes a gap_dimension, gap_distance, telos_links, before/after implementation, and a Prior Status from Thread 0. The Recommend workflow leads with gaps; the Upgrade workflow leads with sources. TwitterBookmarks scans X bookmarks through the same four-thread analysis.

In Action

What you say to your DA, and what the Upgrade skill actually does.

  • You say "what should i work on next"
    Runs Recommend: reads TELOS goals and CURRENT_STATE/INFRASTRUCTURE.md in parallel, computes per-goal gap tuples, cross-references Thread 0 (prior work audit) to skip already-shipped items, and returns tiered recommendations (CRITICAL/HIGH/MEDIUM/LOW) each tagged with gap_dimension, gap_distance, telos_links, and concrete before/after implementation steps.
  • You say "check for upgrades, anything new from anthropic or in the ecosystem"
    Runs Upgrade: hits 30+ sources in parallel via Tools/Anthropic.ts (blogs, changelogs, GitHub releases, YouTube channels), extracts specific techniques with code examples and timestamps, maps each to a LifeOS component or INFRASTRUCTURE.md row, and filters through Thread 0 to skip already-implemented items before ranking.
  • You say "mine my reflections for recurring friction patterns"
    Runs MineReflections: reads algorithm-reflections.jsonl, clusters recurring friction by pattern type, surfaces the top candidates as Thread 3 Reflection-tagged recommendations with specific file paths and proposed fixes.

Inside the Skill

The thinking, frameworks, and architecture that distinguish this skill from a generic version of the same task.

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

Surface Contract
Anthropic (30+ sources: blog, changelogs, GitHub repos, docs) bun Tools/Anthropic.ts — diffs against State/last-check.json, updates it itself
YouTube channels Config: 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 trending Config: 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 sources user-sources.json in CUSTOMIZATIONS — fetch each; skip dead/redirected pages with a note
Claude Code internals When 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 reflections MEMORY/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.

Workflows & Routing · 5

Each workflow is one job the skill runs. The trigger phrases route your request to the right one — this is the skill's routing table.

  1. 01
    Upgrade Workflows/Upgrade.md

    check for upgrades, check sources, any updates, check Anthropic, check YouTube, upgrade

  2. 02
    MineReflections Workflows/MineReflections.md

    mine reflections, check reflections, what have we learned, internal improvements, reflection insights

  3. 03
    AlgorithmUpgrade Workflows/AlgorithmUpgrade.md

    algorithm upgrade, upgrade algorithm, improve the algorithm, algorithm improvements, fix the algorithm

  4. 04
    ResearchUpgrade Workflows/ResearchUpgrade.md

    research this upgrade, deep dive on [feature], further research

  5. 05
    FindSources Workflows/FindSources.md

    find upgrade sources, find new sources, discover channels

Tools · 1

Deterministic executables the workflows call — the code that does the real work, not prompt scaffolding.

  • Anthropic.ts

How to Invoke

Say any of these to your DA and LifeOS activates the Upgrade skill automatically:

  • "upgrade"
  • "system upgrade"
  • "check Anthropic"
  • "new Claude features"
  • "algorithm upgrade"
  • "LifeOS upgrade"
  • "mine reflections"

Or invoke explicitly:

Skill("Upgrade")

Want LifeOS to do this for you?

Install LifeOS on your machine — your DA gets the Upgrade skill plus 55 others, all hooked into one Life OS.