llm-language:update — Self-Evolution Research Agent (v3.2)

v3.2 era note (2026-06-11)

The frontier moved: Claude Fable 5 (claude-fable-5, Mythos-class tier ABOVE Opus, GA 2026-06-09) and Opus 4.8 (claude-opus-4-8, new workhorse default since 2026-05-28). Terminology: "Mythos-class", NEVER "Claude 5 family" (docs never use it). Authoritative era reference: skills/llm-language/references/fable-5-era.md. New monitored surfaces: refusal API (stop_reason: "refusal" + stop_details.category), fallbacks beta param, classifier rerouting to Opus 4.8 (<5% sessions) + silent degradation controversy, dynamic workflows / ultracode, nested sub-agents (≤5 levels), advisor tool (beta), AutoDream (unofficial idle memory consolidation), MessageDisplay + post-session hooks.

P0 deprecation table (refresh each run): Sonnet 4 + Opus 4 retire 2026-06-15 (→ claude-sonnet-4-6 / → claude-opus-4-8); Agent SDK billing split 2026-06-15 (separate monthly credit pool, one-time manual claim required); Fable 5 leaves plan limits 2026-06-22→23 (prepaid usage credits); claude-mythos-preview retires 2026-06-30 (→ claude-mythos-5); fast mode on Opus 4.6 removed ~late June; Opus 4.1 retires 2026-08-05 (→ claude-opus-4-8).

Overview

Autonomous research agent that keeps llm-language current by searching for:

• Claude model updates (Fable 5 / Mythos-class, Opus 4.8+, Sonnet 4.6+, Haiku 4.5+, new effort tiers, adaptive thinking, task budgets, tokenizer changes, refusal/fallback API surface)
• Claude Code features (hooks, skills, plugins, memory, output styles, agent view, background sessions, /goal + Outcomes pattern)
• Long-horizon autonomy primitives (/goal, Outcomes, Dreaming, separate-evaluator patterns)
• New prompting research (arXiv, NeurIPS, ICLR, ACL, EMNLP — 2025-2026; MAD, MASPO, Tournament-of-Prompts, OPTAGENT, SDRL, Agentic Memory)
• Memory systems (auto-memory MEMORY.md, Memory Tool API, Memory Import, claude-mem, mem0/letta/zep/cognee, Dreaming consolidation)
• Agent framework ecosystem (OpenAI Agents SDK, Google ADK + A2A 1.2, Microsoft Agent Framework, CrewAI, LangGraph)
• Community innovations (plugins, skills, hooks, marketplace, awesome-claude-code-toolkit, claude-mem)
• YouTube content (demos, reviews, walkthroughs — via yt-learner skill)
• Deprecation & billing watch (P0: Sonnet 4 / Opus 4 retire 2026-06-15; Agent SDK Credit pool split)

v3.0 changes vs v2.0:

• /goal command as a first-class monitored feature AND as an internal mechanism to run the update loop itself
• Separate-evaluator pattern (/goal ⇄ Outcomes ⇄ LangGraph HITL ⇄ verification-before-completion) flagged as cross-vendor convergence — recommended integration
• P0 deprecation tracking (model retirement dates surfaced explicitly with migration mappings)
• Agent SDK billing-split warning (separate credit pool from 2026-06-15)
• A2A protocol 1.2 monitoring (signed agent cards, multi-vendor interop)
• Token-compaction tracking (ADK Event Compaction, Anthropic server-side compaction beta, Opus 4.7 tokenizer +0–35%)
• Outcomes + Dreaming as new monitored Anthropic primitives
• Negative-result reporting required (e.g., "no Sonnet 4.7 exists — Anthropic skipped that number")
• Reordered priority tiers to surface P0 deprecations before feature additions

When to Use

Explicitly invoked via /llm-language:update or when user asks about updates, new memory features, Opus 4.7, /goal, Outcomes, Dreaming, Agent View, deprecations, or agent frameworks.

Pipeline (v3.0)

Step 1: Parallel Research Dispatch

Launch 3–4 background agents in parallel (≤ 4 to avoid rate-limit). Brief each as a self-contained research agent with explicit "do not fabricate, cite URLs, flag low-confidence" rules.

Agent A — Anthropic + Academic Deep Research (deep-research or general-purpose)

• Task: official docs, changelog, anthropic.com/news, arXiv 2025–2026
• Focus: /goal, Outcomes, Dreaming, Agent View, model deprecations, tokenizer changes, new effort tiers
• Must cross-verify every Anthropic-feature claim across ≥ 2 sources (official docs + one independent)

Agent B — YouTube Research (yt-learner or general-purpose)

• Task: find YouTube videos on recent updates
• Channels: AI Explained, Matthew Berman, Bycloud, David Shapiro, 1littlecoder, Prompt Engineering, Fireship, Two Minute Papers, Indy Dev Dan, Cole Medin, All About AI, Sam Witteveen, World of AI
• Note: YouTube anti-scraping often blocks channel/date extraction — accept partial attribution; cross-reference via aggregator articles

Agent C — Memory + Framework + Community (general-purpose)

• Task: memory modalities, agent frameworks, community ecosystem
• Focus: claude-mem, mem0/letta/zep, OpenAI Agents SDK, Google ADK, A2A protocol, Microsoft Agent Framework, LangGraph, CrewAI; awesome-claude-code-* repos; Simon Willison posts; Reddit r/ClaudeAI

Agent D (optional, only when scope demands) — Targeted Feature Deep-Dive

• Use when a specific high-impact feature (e.g., /goal) needs verbatim docs extraction + code-pattern study
• Do not launch unless A/B/C cannot satisfy

Step 2: Direct WebSearch + WebFetch (in parallel with agents)

Run 4–6 targeted queries for immediate signal while agents work in background:

1. Claude {NextOpus} OR {NextSonnet} release features {current_year}
2. Claude Code changelog {current_year} {current_month} new features
3. Claude Code "/goal" OR Outcomes OR Dreaming OR "Agent View" {current_year}
4. site:arxiv.org prompt optimization OR multi-agent debate OR agentic memory {current_year}
5. Claude Code memory MEMORY.md auto-memory {current_year}
6. Anthropic model deprecation OR retirement {current_year}

Direct WebFetch authoritative pages (always preferred over secondary blogs):

• https://code.claude.com/docs/en/changelog
• https://code.claude.com/docs/en/whats-new
• https://code.claude.com/docs/en/goal
• https://code.claude.com/docs/en/agent-view
• https://platform.claude.com/docs/en/about-claude/models/overview
• https://platform.claude.com/docs/en/about-claude/model-deprecations
• https://www.anthropic.com/news
• https://github.com/anthropics/claude-code/blob/main/CHANGELOG.md (use raw URL — https://github.com/anthropics/claude-code/raw/refs/heads/main/CHANGELOG.md — when WebFetch returns nav-only)

Step 3: Diff Analysis

Compare findings against current llm-language capabilities:

• Read skills/llm-language/SKILL.md — what's implemented?
• Read references/scoring-rubric.md — what dimensions exist?
• Read references/scientific-principles.md + docs/BIBLIOGRAPHY.md — what papers are referenced?
• Read references/xml-prompt-template.md — current schema version?
• Read .claude-plugin/plugin.json + marketplace.json — installed version?
• Read hooks/ directory — shipped hooks?
• Read skills/jarvis/SKILL.md + skills/refine-skills/SKILL.md — sub-skills state?

Identify GAPS:

• New model features not leveraged (effort tiers, tokenizer changes, thinking budgets)
• New hooks not used (PreCompact, TaskCreated, PermissionDenied, SubagentStart, effort.level/$CLAUDE_EFFORT exposure)
• New autonomy primitives not integrated (/goal, Outcomes, separate-evaluator)
• New memory modes not integrated (Dreaming, Memory Import, Memory for Managed Agents)
• New papers not referenced
• Community patterns not adopted
• Agent framework patterns worth borrowing (ADK Event Compaction, OpenAI harness+sandbox, LangGraph HITL checkpoints)
• Deprecation exposure (any pinned model IDs in llm-language scoring/templates that retire soon?)

Step 4: Impact Assessment

For each finding, assess:

Priority tiers (v3.0 reordered — P0 surfaces deprecations first):

• P0 (critical, time-bound) — model deprecations with dates, API removals, billing changes, security CVEs
• P1 (high) — significant quality gains (Impact ≥ 8), reasonable effort
• P2 (medium) — nice-to-have improvements
• P3 (low) — documentation, polish, future-proofing

Step 5: Report

Present findings as structured report:

★ llm-language:update v3.0 ──────────────────────
Research date: {date}
Sources scanned: {N} (official docs + papers + YouTube + changelog + community)
Agents dispatched: {N}
New findings: {N relevant}
Actionable updates: {N priority > 7}
──────────────────────────────────────────────────

## P0 — Time-Bound Breaking Changes
[Model retirements with dates, billing changes, must-have migrations]

## P0 — Negative Results
[What does NOT exist (e.g., "no Sonnet 4.7") — to prevent future hallucination]

## Claude Model Updates
[Versions, effort tiers, thinking modes, tokenizer notes]

## Claude Code Features
[/goal, Outcomes, Dreaming, Agent View, hooks, skills, plugins, memory, commands]

## Long-Horizon Autonomy Primitives  ← NEW v3.0 section
[Separate-evaluator pattern across vendors; integration recipes]

## New Prompting Research
[Papers with arXiv IDs + venue + one-line takeaway]

## Memory Modalities
[Auto-memory, Memory Tool API, Memory Import, Dreaming, third-party MCP memory servers]

## Agent Framework Insights
[Patterns from OpenAI/Google/Microsoft/CrewAI/LangGraph + A2A protocol status]

## YouTube Takeaways
[Top N videos with actionable insights + creator confidence notes]

## Community Innovations
[Notable plugins, skills, hooks, marketplaces]

## Recommended Actions (Priority-ordered)
- [P0] Critical migrations (with deadline)
- [P1] High-impact integrations
- [P2] Medium-impact improvements
- [P3] Polish

Step 6: Optional Auto-Apply

If the user approves (or in auto mode), apply the highest-priority updates:

• Update plugin.json version (semantic: major for breaking, minor for features, patch for fixes)
• Update marketplace.json to match
• Update SKILL.md files (llm-language, jarvis, refine-skills, update itself) with new features
• Update scoring-rubric.md if new dimensions needed
• Update scientific-principles.md + docs/BIBLIOGRAPHY.md with new papers
• Update rosetta-bootstrap.md if schema change
• Update xml-prompt-template.md if schema change
• Create new references/ files for major migration guides (e.g., goal-integration.md, deprecation-2026-06-15.md)
• Ship new hooks in hooks/ directory (e.g., a hook that warns when a deprecated model ID is detected in transcripts)
• Update README.md to reflect version change

Step 7: ROSETTA Update

Log the research session in ROSETTA.md Evolution Log:

• Date (ISO)
• Versions before → after
• Findings summary (one line per P0/P1)
• Changes applied (file-level)
• User feedback (if any)
• Confidence notes (which claims are well-sourced vs speculative)

Step 8: Git Commit (if applicable)

If running in a git repo (llm-language source):

• Stage changed files (NEVER git add . — list paths explicitly)
• Commit with conventional format: feat(vX.Y.Z): {summary} / fix(vX.Y.Z): {summary} / chore(vX.Y.Z): deprecation watch
• Push to origin/main only with user authorization
• For breaking changes (major bump), require explicit user approval before commit

Cross-Reference: llm-language v4.3 Native /goal Integration

As of llm-language v4.3.0 (2026-05-20), /goal is integrated as a first-class primitive at Phase 1.4b (Goal Gate) + Phase 5.0 (Execute Wrapper) of the main skill. The integration was decided via 3-agent debate (architecture / safety / cost — all three converged on the same hybrid placement) and follows the separate-evaluator pattern documented in scientific-principles.md § F8.

Key invariants enforced by the integration:

• 8-clause activation predicate (complex/critical + xhigh/max + task_budget≥200K + multi-step + hooks_enabled + no_loop + no_outer_goal + depth=0)
• Deterministic condition synthesis from <acceptance-criteria> + <sub-tasks> (Producer is NOT involved)
• Bound N = 15 (complex) / 30 (critical) / N_max = 40
• EVIDENCE block per turn mandatory
• Allowlist scope-creep prevention via PreToolUse hooks
• Three cost guards (per-run / per-day / per-month projection)
• Audit JSON in auto-memory/goal_invocations/<run_id>.json
• Critical tier behind feature flag until false_positive_rate < 5% over ≥ 50 runs

When update finds new /goal-adjacent features (Outcomes evolution, new evaluator models, A2A 1.2 cross-vendor signed cards), assess impact against this integration; propose changes to Phase 1.4b predicate or Phase 5.0 synthesis template.

Using /goal to Power the Update Loop Itself (v3.0 recipe)

The /goal command is itself a tool to make /llm-language:update more autonomous. Recommended invocation when the user wants a hands-off update sweep:

> /goal report contains P0/P1/P2/P3 sections, every P0 has source URL + deadline, no fabricated arXiv IDs, and SKILL.md version bumped if any P0/P1 applied or stop after 25 turns

Why this works:

• Separate evaluator (Haiku default) cheaply checks each turn whether report meets the contract — much cheaper than the executor model
• Verifiable finish line — the rubric forces measurable conditions (avoids "make report better" anti-pattern)
• Bound clause (or stop after 25 turns) prevents token blow-up since /goal has no native turn cap
• Cost note — evaluator tokens are billed on small/fast model pool; main-turn spend dominates

⚠️ /goal caveats (must respect):

• Evaluator does NOT call tools — only judges what's surfaced in the transcript. Conditions like "tests pass" only hold if Claude actually ran tests and surfaced output.
• Requires trusted workspace; blocked by disableAllHooks or allowManagedHooksOnly.
• One active goal per session; persists across --resume/--continue (turn count resets).
• Scope creep risk — agent may "fix adjacent messy code". Scope the condition narrowly.

Key Sources to Monitor (v3.0)

Anthropic Official

Academic

Papers to track explicitly (2026 wave, verify before citing):

• MASPO — Joint Prompt Optimization for Multi-Agent Systems
• Tournament of Prompts — Elo + structured debates
• OPTAGENT — Verbal RL for inter-agent communication
• Self-Debate RL (SDRL) — pre-deployment MAD prep
• Iterative Audit Convergence — prompt QA loops
• From Storage to Experience — agent memory survey
• Agentic Memory / AgeMem — memory ops as tool-actions
• Security of Long-Term Memory — Write/Store/Retrieve/Execute/Share lifecycle

Community

YouTube Channels (for yt-learner / Agent B)

• AI Explained, Matthew Berman, Bycloud, David Shapiro, 1littlecoder
• Prompt Engineering, Fireship, Two Minute Papers
• Indy Dev Dan, Cole Medin, All About AI, Sam Witteveen, World of AI

Agent Framework Ecosystem

• OpenAI Agents SDK (openai.github.io/openai-agents-python) — harness + sandbox + configurable memory
• Google ADK + A2A 1.2 (google.github.io/adk-docs) — Event Compaction, signed agent cards
• Microsoft Agent Framework (docs) — Semantic Kernel + AutoGen unified
• CrewAI (docs.crewai.com)
• LangGraph (docs.langchain.com/oss/python/langgraph) — HITL checkpoints, deep-agent templates
• Anthropic Claude Agent SDK — note billing split from 2026-06-15 (separate credit pool)

Anti-Patterns (v3.0)

Research process

• Do NOT auto-apply breaking changes without explicit user confirmation
• Do NOT add papers without verifying arXiv IDs (especially when search returns only titles)
• Do NOT cite vendor marketing copy as authoritative — always seek primary documentation
• Do NOT fabricate YouTube URLs, channel names, or upload dates — YouTube anti-scraping often blocks attribution; report partial findings explicitly
• Do NOT recommend features that conflict with current SKILL.md architecture without documenting the migration
• Do NOT bloat scientific-principles.md beyond its current structure (add, don't duplicate)
• Do NOT ignore sobering findings (e.g., "Prompt Optimization Is a Coin Flip" — auto-optimization variance)
• Do NOT commit to git without user authorization
• Do NOT dispatch more than 4 parallel agents — diminishing returns and rate limits
• Do NOT recommend Opus 4.7-specific features when user's target model is older (check ROSETTA for preferred target)
• NEW v3.0: Do NOT omit a P0 section just because nothing seems urgent — explicit "no current P0 items" is the correct output. The check itself is the value.
• NEW v3.0: Do NOT skip negative-result reporting (e.g., "Sonnet 4.7 does not exist") — surfaces version-confusion that propagates through prompts.

/goal-specific (when using /goal internally)

• Do NOT use vague conditions ("make report comprehensive") — evaluator silently fails
• Do NOT omit the bound clause (or stop after N turns) — no native cap, token blow-up
• Do NOT assume evaluator ran tools — it only sees the transcript
• Do NOT run /goal in disableAllHooks mode — it is itself a hook
• Do NOT chain /goal with /loop — they conflict (/loop retriggers on interval, /goal after each turn)

v3.0 Enhancements over v2.0

• /goal first-class tracking — promoted from "any new commands" bucket to its own monitored primitive, with the Codex-cloned origin documented
• Separate-evaluator pattern recognition — /goal ⇄ Outcomes ⇄ LangGraph HITL ⇄ ADK ⇄ verification-before-completion flagged as cross-vendor convergence
• /goal-driven update loop — explicit recipe to make the update sweep autonomous via /goal
• P0 deprecation watch — Sonnet 4 / Opus 4 retirement (2026-06-15) surfaced as P0 with migration mappings (claude-sonnet-4-20250514 → claude-sonnet-4-6, claude-opus-4-20250514 → claude-opus-4-8 — corrected v3.2: target is 4.8, not 4.7)
• Agent SDK billing split — separate "Agent SDK Credit pool" effective 2026-06-15 documented as P0 operational change
• A2A protocol 1.2 — multi-vendor agent interop standard (Linux Foundation Agentic AI Foundation) added to ecosystem monitor
• Token-compaction tracking — ADK Event Compaction, Anthropic server-side compaction beta, Opus 4.7 tokenizer (1.0–1.35× more tokens than 4.6)
• Outcomes + Dreaming — new Anthropic primitives (Code w/ Claude 2026-05-06 keynote) added to monitor list
• Agent View + background sessions — claude --bg, /bg, claude agents dashboard
• Negative-result reporting — required when version-confusion exists (e.g., no Sonnet 4.7)
• Raw-URL fallback — when WebFetch on github.com/blob returns nav-only, retry with raw/refs/heads/main/...
• Reordered priority tiers — P0 deprecations and billing surface before feature additions
• Confidence notes mandatory in final report — every finding tagged high/medium/low confidence with source count