Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/backnotprop/pstack/arenanpx skills add backnotprop/pstack --skill arenagit clone --depth 1 https://github.com/backnotprop/pstackWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00058 | $0.01186 |
| Opus 5 | $0.00029 | $0.00593 |
| Sonnet 5 | $0.00012 | $0.00237 |
| Haiku 4.5 | $0.00006 | $0.00119 |
Grade A, and why
arena scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arena
Fan out N parallel attempts at the same task. Read every candidate end to end. Pick the strongest as the base. Graft the best ideas from the others into it. Verify the synthesized result.
Start
Open a todolist with one entry per phase before launching anything. The arena runs autonomously and the list keeps phases from silently disappearing.
- Frame
- Fan out
- Cross-judge
- Pick
- Graft
- Verify
Phase A: Frame
The N candidates will receive the same prompt, so the prompt is the contract. Get it right before spawning anything.
- State the artifact each candidate is producing.
- Derive the rubric. State what success looks like for this task, then turn it into 3-6 concrete gradeable criteria. Concrete:
Adds a --dry-run flag that skips writes. Vague:code is correct. The rubric is the picker's tool in Phase D; candidates only see the task. - Pick the runners. Use
arena runnersfrom~/.cursor/rules/pstack-models.mdcwhen present. Otherwise default to one each onclaude-fable-5-thinking-max,gpt-5.6-sol-max,grok-4.6-fast-xhigh,claude-opus-5-thinking-xhigh. Spawn more when the arena covers multiple design directions. Same model N times when the work is generation-bound rather than judgment-sensitive. - Assign output paths. Each candidate writes to its own location (a git worktree where possible, otherwise
/tmp/arena-<slug>/candidate-<n>/). N candidates writing to the same path is shared mutable state and fails the the separate-before-serializing-shared-state principle skill test.
Phase B: Fan out
Spawn all N subagents in one message with run_in_background: true, each with the task, the path to the shared grounding, its own output path, and instructions to produce both the artifact and a short rationale.
The rationale is mandatory. Without it, the parent cannot tell whether a candidate's structure is principled or accidental, which makes Phase E grafting unreliable. Each rationale names the alternatives the candidate considered and what it rejected.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 72 lines · 58 tokens per session scan A a2241e8500a4
arena is a skill published in the GitHub repository backnotprop/pstack (165 stars, last pushed 13d ago), licensed MIT. It adds 58 tokens to every session and 1,186 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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