Ecosystem Update - 2026-06-14

TL;DR

• No safe automatic harness Quick Wins passed today: every useful new signal either already exists locally, needs a new script/skill, or requires explicit policy/runtime design.
• The strongest new research signal is TRACE: compile repeated user corrections into runtime checks instead of relying on memory alone.
• Current setup already covers most practical daily community advice: hooks, route classification, read-only reviewers, planner/validator agents, conservative profiles, execpolicy guards, omni-mem, and strict skill audit.

Quick Wins

Auto-Implemented

• No harness Quick Wins were auto-implemented.
• Report/state artifacts only: wrote this digest and updated ~/.codex/state/ecosystem-update-last-run.json.

Build Queue

• TRACE-style correction enforcement spike (agent-pattern) - https://arxiv.org/abs/2606.13174 - Prototype a bounded local adapter that turns repeated user corrections into deterministic closure checks. This fits the current AgentOps evidence contract, but implementation needs a design pass because it would generate or modify runtime checks.
• HyperTool-style deterministic tool subroutine evaluation (mcp) - https://arxiv.org/abs/2606.13663 - Evaluate whether local MCP-heavy workflows would benefit from batching deterministic subroutines behind a single executable wrapper; do not add a new tool layer until a recurring multi-tool bottleneck is measured.
• DailyReport-style research-agent eval adapter (research) - https://arxiv.org/abs/2606.12871 - Add a future eval mode for daily search/research tasks with subtask rubrics and attribution. Useful for ecosystem-update and daily-tech-brief quality, but not a one-file Quick Win.
• Codex PATH/app version reconciliation review (Codex-md) - https://github.com/openai/codex/releases - PATH codex is 0.133.0 while Codex.app bundles 0.140.0-alpha.2; keep watching for a stable upgrade path instead of auto-upgrading or pinning alpha globally.
• Selective external skill catalog audit (skill) - https://github.com/grahama1970/agent-skills and https://github.com/heilcheng/awesome-agent-skills - Run codex-skill-audit --strict only on specific candidate skills that solve a real gap; reject bulk import.

Research

• Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents - Directly relevant to converting repeated user corrections into enforceable checks, complementing omni-mem rather than replacing it.
• HyperTool: Beyond Step-Wise Tool Calls for Tool-Augmented Agents - Useful for thinking about MCP/tool-call granularity when deterministic multi-tool flows waste context.
• DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks - Relevant to evaluating daily ecosystem/research digests with interpretable rubrics instead of coarse pass/fail.
• AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility - Already seen on June 13, still relevant as a background signal for agent-agnostic assessment interfaces.
• Reward Modeling for Multi-Agent Orchestration - Already seen on June 13, still maps to future orchestration-quality scoring.

Already Have

AGENTS.md/runtime-reference split, byte-identical global/project policy docs, OpenAI developer docs MCP, live web search posture, hooks enabled with current [features].hooks, PreToolUse Bash safety guard, PostToolUse verification/failure-context hooks, SessionStart readiness/config-posture hooks, UserPromptSubmit route classification, Stop/PreCompact omni-mem hooks, hook statusMessage and timeout usage, custom agent TOMLs with model/reasoning/sandbox/nickname metadata, read-only explorer/planner/reviewer/validator roles, implementation worker role, agents.max_depth = 1, conservative/review profiles, destructive git/rm execpolicy rules, strict skill audit command, session transcript search, omni-mem memory workflow, runtime doctor, config posture checker, prompt telemetry disabled, apps/plugins enabled with destructive connector actions disabled, plugin hooks disabled, native Codex memories disabled, broad local skill library, Browser/Chrome/Computer Use plugins, and AgentOps direct/lightweight/autonomous contract posture.

Rejected

• Enable features.codex_hooks from community docs - rejected because official Codex docs now identify features.hooks as current and features.codex_hooks as deprecated; local config already uses hooks = true.
• Enable native Codex memories - rejected because local durable memory is intentionally handled through omni-mem and native memories remain disabled by policy.
• Wholesale install shared/awesome agent-skill catalogs - rejected because bulk imports duplicate existing skills and add supply-chain/prompt-scope risk; use strict selective audit instead.
• Adopt “agents replace human code review” as policy - rejected because the local review posture intentionally keeps evidence-backed verification and review pressure for R3/R4 work.
• Auto-upgrade Codex CLI or switch PATH to alpha app binary - rejected because stable posture avoids global alpha adoption without explicit rollback notes and validation.
• Add TRACE/tellonce as a Quick Win - rejected because it would create new runtime checks or skill surfaces; worthwhile as a designed Build Queue item, not an automatic daily harness edit.
• Add HyperTool-style executable MCP wrapper immediately - rejected because it would introduce a new tool layer without measured recurring bottleneck evidence.
• Edit AGENTS.md as a Quick Win - rejected by ecosystem-update hard limits; policy changes require explicit user direction.

Sources checked: https://github.com/hesreallyhim/awesome-claude-code, https://howborisusesclaudecode.com/, https://github.com/shanraisshan/codex-cli-best-practice, https://raw.githubusercontent.com/shanraisshan/codex-cli-best-practice/main/best-practice/codex-hooks.md, https://raw.githubusercontent.com/shanraisshan/codex-cli-best-practice/main/best-practice/codex-subagents.md, https://raw.githubusercontent.com/shanraisshan/codex-cli-best-practice/main/best-practice/codex-config.md, https://raw.githubusercontent.com/shanraisshan/codex-cli-best-practice/main/best-practice/codex-skills.md, https://developers.openai.com/codex/config-reference, https://arxiv.org/search/?searchtype=all&query=LLM+agent+coding&order=-announced_date_first, https://arxiv.org/abs/2606.13174, https://arxiv.org/abs/2606.13175, https://arxiv.org/abs/2606.13663, https://arxiv.org/abs/2606.12871, https://github.com/grahama1970/agent-skills, https://github.com/heilcheng/awesome-agent-skills Tier 2 fetched: yes Tier 3 fetched: no - skipped because tier3_last_run was 2026-06-13T06:34:22-04:00 Run at: 2026-06-14T06:30:45-04:00