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/karrolcia/hippocampus/pseo-performancenpx skills add karrolcia/hippocampus --skill pseo-performancegit clone --depth 1 https://github.com/karrolcia/hippocampusWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/karrolcia/hippocampus/pseo-performance)<a href="https://agentmods.dev/skills/karrolcia/hippocampus/pseo-performance"><img src="https://agentmods.dev/badge/skills/karrolcia/hippocampus/pseo-performance.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00069 | $0.02455 |
| Opus 5 | $0.00034 | $0.01228 |
| Sonnet 5 | $0.00014 | $0.00491 |
| Haiku 4.5 | $0.00007 | $0.00246 |
Grade A, and why
pseo-performance 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 6d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 6d ago First seen · 253 lines · 69 tokens per session scan A 2d5f4f49677c
pseo-performance is a skill published in the GitHub repository karrolcia/hippocampus (13 stars, last pushed 3d ago), licensed AGPL-3.0. It adds 69 tokens to every session and 2,455 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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deep-review
Multi-dimensional code review. Use when the user asks to review, evaluate, or audit a PR, diff, branch, or pasted change — including informal review asks like "look at this change for problems" — via light mode: one independent reviewer against the dimension quick checklists. Not for explain-only questions about what…
acceptance
End-to-end verification and self-evidence for a delivery in any repository, with or without a preconfigured verify plan. Discover an existing plan when one was handed to this run; otherwise author checks and publish a standalone acceptance. Pick the proving surface (CLI / web / desktop / iOS Simulator), drive the real…
ux-audit
Audit a page / surface against the Designing Interfaces pattern language + the ux skill checklists, then land findings. Three layers — static (code), visual (screenshots), dynamic (automated user journey + perf). Use to run a repeatable, standards-based UX review of one screen.
ux
LobeHub product design values / principles / checklists. Use whenever the work touches user-interface features or implementation — designing or building any user-facing flow — to get better UX results.