arena-power-user-playbook

这是arena.ai的高级用户可执行工具集，核心含模式推荐、弱响应筛查、模型榜单比对、会话状态管理、本地反馈统计等脚本；适用于选择平台使用模式、核查榜单、处理低质量响应、拆分长任务、平台限流时切换云服务商等场景；通过Python命令行调用，输出机器可读结果，全本地运行无网络依赖，自带防幻觉规则保障准确性。

orionshaowswmw

热度 53.80

最近更新 2026-09-06 11:39:21

分类：自动化依赖：代码执行 / 读写文件应用场景：自动化脚本 / 报告撰写 / 任务管理 / 数据处理 ...

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add arena-power-user-playbook

Metadata

name: arena-power-user-playbook
version: 2.0.0
author: orionshaowswmw
license: MIT-0
description: Executable power-user playbook for arena.ai. Use when choosing an arena.ai mode (Direct / Agent / Side-by-Side / Battle) for a task, reading or checking the Agent Arena leaderboard, screening a response with measurable weak-response flags, chunking long Agent work with SESSION-STATE.md carry, or falling back to cloud providers when arena.ai throttles or is down. Bundles a dated, sourced model snapshot (2026-09-05) plus offline python3-stdlib scripts: mode advisor, weak-response screener, leaderboard rotation checker, state manager, local feedback log.
tags: ["arena","arena-ai","model-selection","agent-mode","leaderboard","fallback","power-user"]
metadata: {"openclaw":{"emoji":"🏆"}}

arena-power-user-playbook 🏆 v2.0.0

Executable companion to arena.ai power usage. Every claim here is either sourced (dated, in references/), measured (script output), or labeled as a heuristic. No unverifiable numbers, no phantom scripts.

Hard rules (anti-hallucination contract)

Model names are rotating data, not facts. Never copy a name from this skill into a live decision; use the live picker or arena.ai/leaderboard/agent, then model-check against the snapshot.
The dated snapshot in data/ is a comparison baseline only.
weak reports screening flags, never quality judgments.
Fallback is cloud-only — no local GGUF/llama.cpp, ever.
Every script prints one machine-readable summary line; full JSON via --out. Exit codes: 0 ok · 1 findings/changes · 2 usage or no data · 3 internal error.

Command contract

Command	Purpose	Key flags
`mode`	recommend Direct / Agent / Side-by-Side / Battle	`--task` `--files` `--steps` `--coding` `--compare` `--blind` `--budget-conscious` `--tasks file.json`
`weak`	screen one response with weighted heuristic flags (bands weak/medium/strong)	`--response` \| `--file` `--min-words 35` `--expect-short`
`model-check`	fresh leaderboard dump vs dated snapshot: drift, rotated-out, new	`--dump file.json` `--date`
`snapshot`	write a new dated snapshot from a dump	`--dump` `--date YYYY-MM-DD` `--out`
`state`	SESSION-STATE.md for chunked multi-chat work	`--file` `--action init\|add\|summary\|next\|validate` `--goal` `--phase` `--done ...` `--next ...` `--force`
`stats`	local feedback log + report (self-improvement loop)	`--action log\|report` `--event` `--model` `--mode` `--log`
`selftest`	run the skill self-test	—

All commands: python3 scripts/arena_playbook.py <cmd> ... (python3 stdlib, zero network, works on any agent runtime).

Mode decision (quick)

Compare two known models → Side-by-Side · Blind A/B vote → Battle
Multi-step / tools / files + build / coding with iteration → Agent (official Agent Mode task mix: coding 29%, research 11%, planning 11%)
Single-shot Q&A, including one-file questions → Direct
"Max/High/xHigh" are per-model compute tiers on the leaderboard, not a router. Pick model, then a tier the task can afford (see Cost/Task P50).

Details + official sources: references/modes.md.

Leaderboard (how to read it)

Headline = Net Improvement (τ̂) from causal tracing over in-the-wild Agent sessions (95% CIs). Signals: confirmed success, praise-vs-complaint, steerability, bash recovery, tool hallucination (lower = better). Small session counts = wide CIs; adjacent ranks within CIs are not distinguishable. Rotation protocol: model-check → snapshot. Details: references/leaderboard.md.

Weak response (3-strike escalation)

weak bands: weak ≥50, medium ≥25, strong <25 (screening only). On a real task: strike 1 = new chat, same task · strike 2 = rephrase + fresh chat with state next carry · strike 3 = higher tier or different family from the live board · persistent = switch provider (cloud-only table) or split the task. Log each strike via stats log --event weak_response. Details: references/fallback.md.

Load map (progressive disclosure)

File	Load when
`references/modes.md`	deciding a mode beyond the quick table; auditing mode claims
`references/leaderboard.md`	reading ranks/CIs, running rotation protocol
`references/fallback.md`	arena.ai degraded, weak-response escalation, chunking
`data/model_snapshot_2026-09-05.json`	baseline for `model-check` — sourced from arena.ai/leaderboard/agent, board date 2026-09-05, fetched 2026-09-06 (verify before relying)
`tools/playbook_selftest.py`	verifying the install (10 groups, all offline)

Verification

python3 scripts/arena_playbook.py selftest → ALL CHECKS PASSED. README.md carries the artifact tree hash with the recompute script.

Privacy

Local scripts read only files you pass them; stats writes only to a local log you name. Nothing leaves the machine. arena.ai itself processes your prompts server-side — share only data appropriate for it.