Ecosystem Update - 2026-06-10

TL;DR

• One safe harness Quick Win landed: default.rules now blocks additional destructive git clean variants that were not covered by yesterday's git clean -fd guard.
• Today's community Codex/Claude sources mostly reinforce existing local posture: concise AGENTS policy, feature-specific subagents, progressive-disclosure skills, hook-backed validation, command rules, and cautious plugin/skill intake.
• Tier 2 research points toward better long-horizon memory, state-step scoring, deterministic integrity gates, visual-feedback QA, and adversarial benchmark hardening, but none should be auto-wired globally.

Quick Wins

Auto-Implemented

• Backed up config.toml, hooks.json, all agent TOMLs, and rules/default.rules to /Users/chadsimon/.codex/backups/2026-06-10/.
• Updated /Users/chadsimon/.codex/rules/default.rules with forbidden exact-prefix rules for git clean -df, git clean -xdf, git clean -xfd, and git clean -f -d.
• Verified hooks.json with python3 -m json.tool and config.toml with tomllib.
• Verified execpolicy decisions through constructed argv checks: git clean -fd, -df, -xdf, -xfd, and -f -d . resolve to forbidden; git clean -n remains unmatched.
• Ran python3 /Users/chadsimon/.codex/bin/codex_config_posture.py --mode warn; result: Codex config posture ok.

Build Queue

• Codex 0.137+ upgrade and smoke (Codex-md) - https://github.com/shanraisshan/codex-cli-best-practice - Local codex --version reports codex-cli 0.133.0, while the best-practice repo references 0.137.0 and newer session/archive/history behavior. Verify against official OpenAI releases before upgrading; do not auto-upgrade from this daily skill.
• Tool-response context budget adapter (agent-pattern) - https://arxiv.org/list/cs.AI/new - The enterprise MCP context-overflow paper suggests a local adapter for truncating, summarizing, and staleness-checking verbose tool outputs before they poison long turns. Current hooks record verification but do not budget tool-response payloads.
• Visual-feedback QA harness for generated UI artifacts (skill) - https://arxiv.org/list/cs.AI/new - The visual artifact self-distillation work supports a reusable screenshot/layout QA skill for clipped text, overflow, contrast, and alignment. This belongs in a scoped frontend skill or Playwright helper, not a global hook.
• PerspectiveGap-style orchestration prompt eval (research) - https://arxiv.org/list/cs.MA/recent - Build a small eval pack for planner/reviewer/worker prompt disagreement and role misalignment before changing agent TOMLs.
• PACE-style anytime-valid acceptance checks (research) - https://arxiv.org/list/cs.MA/recent - The acceptance-test framing maps well to autonomous slice closure, but should be integrated through auto_runtime.py evidence contracts after a design pass.
• State-based personal-agent eval generator (research) - https://arxiv.org/list/cs.AI/new - STAGE-Claw-style stateful scenario generation could improve local agent evaluations, but it is too broad for a Quick Win.

Research

• ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning - Relevant to omni-mem and precompact consolidation: separate compact planner-visible gists from background memory accumulation.
• 3SPO: State-Score-Supervised Policy Optimization for LLM Agents - Useful for future route/slice scoring: step-level state scores are a better signal than terminal-only success.
• Deterministic Integrity Gates for LLM-Assisted Clinical Manuscript Preparation - Reinforces the local verification posture: halt-on-failure, cheapest sufficient deterministic checks, and evidence manifests.
• The Consistency Illusion: How Multi-Agent Debate Hides Reasoning Misalignment - Watch item for reviewer/planner consensus: agreement alone is not evidence of aligned reasoning.
• PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting - Candidate eval source for role-specific agent instructions and orchestration prompts.
• Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops - Supports adversarial evals for bug-miner, security review, and harness guard regression tests.
• Fact-Augmented Lookahead Planning for LLM Agents - Maps to Codex's memory/context flow: extract atomic facts, validate, then condition planning rather than relying on raw long history.

Already Have

PreToolUse Bash safety guard, PostToolUse verification ledger, PostToolUse failure-context hook, UserPromptSubmit route classifier, Stop omni-mem save hook, PreCompact omni-mem hook, SessionStart runtime checks for startup/resume/clear/compact, features.hooks = true, prompt telemetry disabled, plugin hooks disabled by default, native Codex memories disabled in favor of omni-mem, OpenAI developer docs MCP with parallel calls enabled, Browser/Chrome/Computer Use plugins, OpenAI Developers plugin, app destructive actions disabled by default, read-only explorer/planner/reviewer/validator agents, feature-specific Python and TypeScript reviewers, bounded max_threads, max_depth, and runtime caps, conservative profiles, AGENTS.md as the Codex-owned project-doc fallback, session search via codex-session-search, skill-audit workflow, large local skill library, default.rules command policy, force-push/hard-reset/destructive-checkout/destructive-clean guards, config posture validation, and the AgentOps contract checker.

Rejected

• Auto-upgrade Codex CLI from the ecosystem run - version drift is real, but upgrades require official release verification and a smoke pass outside this safe Quick Win lane.
• Wholesale install from awesome-claude-code, Boris workflow collections, agent-skills, awesome-agent-skills, or community plugin catalogs - outside skills/plugins require strict audit and Codex-owned adaptation.
• Enable plugin hooks globally - still a trust-boundary decision; current features.plugin_hooks = false remains the safer default.
• Enable native Codex memories globally - conflicts with the omni-mem-first memory policy and the current prompt-telemetry posture.
• Add global auto-format or visual-QA hooks - useful for frontend tasks, but too broad as a machine-wide hook because repo formatters and UI stacks vary.
• Retire planning/alignment in favor of always-on auto mode - Boris's updated Claude guidance is useful context, but it conflicts with this machine's non-trivial-work alignment and AgentOps contract.
• Wire unsupported hook events such as PostCompact, PermissionRequest, SubagentStart, SubagentStop, PreSkillUse, or PostSkillUse without existing scripts and verified runtime support - report as watch items only.
• Enable Fast Mode or global service_tier - this is a spend/performance preference, not a safety or quality harness fix.
• Use AGENTS.override.md or edit AGENTS.md as a Quick Win - constitutional policy docs and project-doc loading behavior require explicit direction.

Sources checked: https://github.com/hesreallyhim/awesome-claude-code, https://howborisusesclaudecode.com/, https://github.com/shanraisshan/codex-cli-best-practice, https://arxiv.org/search/?searchtype=all&query=LLM+agent+coding&order=-announced_date_first, https://arxiv.org/list/cs.AI/new, https://arxiv.org/list/cs.MA/recent, WebSearch "Codex new hooks agents skills site:github.com 2026", WebSearch "arxiv.org LLM agent coding autonomous 2026 site:arxiv.org", OpenAI developer docs MCP config reference search Tier 2 fetched: yes Tier 3 fetched: no - skipped by weekly gate because tier3_last_run was 2026-06-07T06:36:00-04:00 Run at: 2026-06-10T06:33:30-04:00