roster

name	roster
description	Enumerates the peer AI agent CLIs installed on this machine (codex, pi, goose, opencode, claude, cursor-agent, grok, agy, hermes, oracle) and how to invoke each headlessly. A capability map, not a quota: useful for fresh-context adversarial review on a different model family, second opinions, competing attempts, and wide benches. Use when: "ask codex", "ask another model", "second opinion", "cross-model review", "what AI tools do I have", "other agents", "different model family", "adversarial critique from another provider". Trigger: /roster.
argument-hint	[provider] [task]

/roster

You are not the only frontier agent on this machine. These CLIs are installed and each runs headlessly. They are options, not obligations: native subagents remain the default delegation path, and a peer harness earns a lane only when you can name what it adds — usually a decorrelated model family or a genuinely fresh context.

When a peer harness beats a native subagent

• Adversarial critique of your own work. A reviewer from a different model family has decorrelated failure modes and no loyalty to your reasoning. Critics get the artifact and the oracle only — never the author's reasoning trail (shared AGENTS.md: Fresh context beats self-review).
• Second opinion on a contested judgment — architecture call, risk assessment, "is this idiomatic" — where one model's taste shouldn't decide alone.
• Competing attempts at the same bounded problem, graded blind.
• Wide bench — high-stakes adversarial review (a P0, a security or data-loss surface, a pre-ship "be exhaustive" pass). Fan the artifact across several distinct open-model families via Pi/OpenRouter, each critic on its own lens; distinct families surface distinct real findings instead of confirming each other. See Adversarial bench below.

A native subagent is still better for exploration, scoped builds, and anything where harness identity doesn't matter — it shares your tools, needs no cold start, and the orchestrator is trained on it.

The CLIs

Verified installed and probed 2026-06-14. Each row is the headless form; add the prompt as the argument or via stdin.

CLI	Stack	Headless invocation
codex	OpenAI Codex (gpt-5.5)	codex exec "<task>" (--model, --config model_reasoning_effort=)
pi	Pi over OpenRouter (Kimi, DeepSeek, …)	pi -p --no-extensions --provider openrouter --model <id> "<task>"
goose	Goose over OpenRouter	goose run --no-session --quiet --provider openrouter --model <id> --text "<task>"
opencode	OpenCode over OpenRouter	opencode run --model openrouter/<id> --format json "<task>"
claude	Claude Code (Opus/Fable)	claude -p "<task>" (--model, --effort)
cursor-agent	Cursor (composer)	cursor-agent -p "<task>"
grok	xAI Grok	grok -p "<task>" (--model, --effort)
agy	Antigravity (Gemini)	agy --print "<task>"
hermes	Hermes agent	hermes -z "<task>" (-m <model>)
oracle	Oracle browser consult	npx -y @steipete/oracle --engine browser --model gpt-5.5-pro -p "<task>" --file <paths>
Current model ids, pricing, context windows, and freshness dates:
references/model-provider-harness-index.md. Open-model facts rot in days —
check the review-due date before quoting them.

Adversarial bench

High-stakes review is a model-family spread, not one second opinion: distinct families each catch distinct real bugs (in one P0 pass, one family found a startup-bricking blocker, another a null-timestamp escape, another a write-path gap — no overlap).

• 3–5 distinct families (Kimi/Moonshot, DeepSeek, MiniMax, Qwen, GLM/Zhipu when listed). Same-family variants don't decorrelate — a wide bench of one family is waste.
• Discover slugs live: pi --provider openrouter --list-models <family>. Slugs rot in days; substitute when a family isn't listed.
• One distinct lens per critic (correctness/data-loss, durability, security, perf, API-shape — primitives/shared/references/lenses.md). Optionally load that lens or a domain skill into the critic: pi --skill <path>.
• Cold, bounded, artifact-only: inline diff + oracle + context, run pi -p --no-extensions --no-tools --provider openrouter --model <slug> "<prompt>", background the lanes, synthesize the verdicts yourself. Add one or two native critics on their own lenses (a further family), then re-review the delta after you fix.

Scale to stakes — a routine diff gets one well-aimed cross-family critic, not the full bench.

Judgment

• One well-aimed critic beats three vague ones. Aim it at the claim that would embarrass us in production, with explicit "ignore style/naming" bounds.
• Peer output is evidence, not authority. You weigh it, accept or reject it, and own the result.
• A failed or rambling lane is a result too — report it, don't re-roll silently.
• Oracle is browser-mode only in Harness Kit. Use it for high-context consults through a signed-in ChatGPT session; do not use Oracle API mode or multi-model API panels from the Harness Kit roster default.
• For a bounded lane whose evidence should outlive the session, write the receipt to Powder — a run, comment, or link on the card the lane's work feeds. Optional for quick second opinions; useful when the lane's verdict feeds a ship decision.
• Heavy, long-running, or isolation-needing lanes run on sprites (/sprites) regardless of which CLI executes them.

Gotchas

• Peer CLIs run cold: no conversation history, no local skills unless the harness loads them itself. Inline everything the lane needs.
• Match the bench to the stakes (see Adversarial bench). A routine second opinion is one well-aimed cross-family critic; fanning wide there is theater. The real waste is a wide bench of the same family — variants don't decorrelate — not a wide bench of distinct ones, which a P0 or security/data-loss surface earns.
• Auth rots independently per CLI. A lane failing instantly with an auth error means re-login locally, not a provider verdict.

Prompting frontier lanes

For Fable/GPT-5.5-class lanes (and the lead's own operation), load primitives/shared/references/prompting-frontier.md: goal-not-steps briefs fenced by house rules, executable bars (delegate metric-invention when the bar is fuzzy), builder-never-grades verification against the REAL output, loop-until-the-bar with a live status artifact, prior traces as fuel, and budgets instead of permission-asks. Cheaper models still earn more mechanism — tier the prescription to the model.