AgentOps vs. everything-claude-code

everything-claude-code (affaan-m/everything-claude-code) positions itself as "the performance system for AI agent harnesses" — DRY parity across Claude Code, Cursor, Codex, OpenCode, and Gemini, shipping 48 subagents and 182 skills with a single set of definitions kept in sync across all five runtimes.

Note on social proof. The everything-claude-code README claims 140K+ stars and 21K+ forks. The entire Claude Code plugin ecosystem is below those numbers as of 2026-05-07; AgentOps does not validate that claim. The comparison below evaluates the project on its concrete features, not the unverified social-proof metric.

At a glance

The two products optimize for different axes. everything-claude-code optimizes for "the same skill set works in every harness." AgentOps optimizes for "the same model-routed workflow accumulates context across every session."

The model-independent-per-phase distinction

This is the load-bearing difference, and it is easy to miss.

everything-claude-code's "cross-harness parity" is multi-runtime distribution of a Claude-shaped workflow: one set of skill and subagent definitions, kept DRY across five harnesses, so a developer who lives in Cursor today can move to Codex tomorrow and keep the same skill surface. That is real engineering work — maintaining parity across five runtime conventions is harder than it sounds — and the value is portability across editors.

AgentOps's per-phase model routing is a different shape entirely: inside a single RPI loop, Claude does discovery, Codex implements, fresh Claude validates, all in one workflow with state preserved across the model boundaries. The handoff is the feature.

$ ao rpi "add rate limiting to /login"
[research/claude]    found 3 prior auth changes in .agents/decisions/
[plan/claude]        proposed: token bucket, 5/min per IP, Redis-backed
[pre-mortem/codex]   WARN: Redis unreachable case unhandled
[implement/codex]    wrote middleware/ratelimit.go, 2 tests
[validate/claude]    go test ./... PASS, gate: WARN — missing jitter
[recorded]           .agents/runs/2026-05-07-rate-limit/

The labels in brackets are the entire pitch made literal. Each phase picks the model that is best for that phase; the validation phase sees a fresh context window so it does not rubber-stamp work it just produced. Skills in any harness — including everything-claude-code's parity-shipped catalog — inherit the harness's single active model. They do not compose model choices per phase within one workflow. Nobody else in the ecosystem does this.

Cross-harness parity and per-phase model routing are not the same capability. The first is "my skill set is portable." The second is "my workflow uses multiple models inside one task." A team can want both, but a buyer should not assume one implies the other.

When to pick which

• Pick everything-claude-code if your bottleneck is "I switch between Claude Code, Cursor, and Codex daily and want the same skills everywhere." Cross-harness parity is the headline value, and 182 skills + 48 subagents is a sizeable catalog out of the box.
• Pick AgentOps if your bottleneck is "my agents keep re-learning the same lessons because nothing persists between sessions." The corpus is the moat; the per-phase model routing and council validation are the discipline that makes the corpus trustworthy.
• They are not mutually exclusive. Install both: use everything-claude-code's catalog as portable skill inventory across whichever harness you happen to be in today, and use AgentOps for the context library that compounds across all of them.

Where everything-claude-code wins

Cross-harness DRY parity tooling. Maintaining the same skill and subagent definitions across five runtimes — each with its own conventions for tool surfaces, file locations, and execution model — is real engineering work. If you genuinely live across multiple harnesses and value the same skill being available the same way in each one, that is a concrete win AgentOps does not directly compete with. AgentOps ships skills for four runtimes (Claude Code, Codex, Cursor) but does not market its parity-maintenance pipeline as the headline feature; the headline is the corpus that the skills produce.

Catalog size for a single-developer audience. 182 skills + 48 subagents is a larger pre-built catalog than AgentOps's 77 skills, and a buyer whose question is "how many skills can I install today" will see that number first.

Where AgentOps wins

Persistent corpus. AgentOps's .agents/ directory is a markdown wiki in your repo, version-controlled with your code. Every session writes learnings, decisions, citations, and validation verdicts; future sessions read them through decay-ranked retrieval (ao inject). everything-claude-code ships skills that run in-session; the work ends when the session does.

Multi-model councils. /council --mixed runs Claude and Codex judges in parallel against one evidence packet and returns structured consensus before commit. This is a validation primitive that catches "looks good to one model" failure modes before the bug ships. A skill catalog — however large or harness-portable — does not provide this surface.

Model-independent phase routing inside one RPI loop. Per the section above: pick the model that is best for each phase, with state preserved across the boundaries. This is a workflow-level capability that is orthogonal to (and does not depend on) cross-harness parity.

Off-API daemon on your hardware. ao schedule and ao daemon run dream / evolve / compile / defrag / forge passes against your subscription, off-vendor, overnight. The corpus compounds while you sleep. everything-claude-code is in-session by design; the off-API surface is not part of its category.

Both are open source

Both projects are open source. AgentOps is open source forever — the corpus that compounds in your repo is yours, the schema is portable, and the discipline survives any single vendor's roadmap. If you are evaluating durability of either project, the source code is the receipt; inflated star counts and other unverified metrics are not.

Bottom line

Different optimization targets, not direct competition.

everything-claude-code is selling cross-harness portability: one skill set, five runtimes, kept in DRY parity by the maintainers' tooling. If your friction is editor switching, that is real value.

AgentOps is selling corpus discipline + per-phase model routing: the operational layer that turns each session's research, decisions, validations, and learnings into a markdown wiki your agents read on the next session, and the workflow primitive that lets you compose Claude, Codex, and other models per phase inside one task.

A buyer who lives across harnesses and does not yet care about persistence should pick everything-claude-code. A buyer who is bottlenecked on "my agents forget what we learned last week" should pick AgentOps. A buyer with both bottlenecks should install both — they sit at different layers of the stack.