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 skills add CamusGIT/EvoQuant --skill evo-memorygit clone --depth 1 https://github.com/CamusGIT/EvoQuantWrote 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/camusgit/evoquant/evo-memory)<a href="https://agentmods.dev/skills/camusgit/evoquant/evo-memory"><img src="https://agentmods.dev/badge/skills/camusgit/evoquant/evo-memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/camusgit/evoquant/evo-memory"><img src="https://agentmods.dev/badge/skills/camusgit/evoquant/evo-memory.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00186 | $0.03871 |
| Opus 5.5 | $0.00074 | $0.01548 |
| Sonnet 5.5 | $0.00037 | $0.00774 |
| Haiku 4.5 | $0.00019 | $0.00387 |
Grade A, and why
evo-memory 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 1mo 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.
This is a copy
100% identical to evo-memory — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evo-Memory
A persistent learning layer that accumulates research knowledge across ideation and experimentation cycles. Maintains two memory stores and implements three evolution mechanisms that feed learned patterns back into future research.
When to Use This Skill
- User has completed an
research-ideationand needs to update Ideation Memory - User has completed (or failed) an
experiment-pipelineand needs to update memory - User is starting a new research cycle and wants to load prior knowledge
- User asks about research memory, learned patterns, or cross-cycle knowledge
- User mentions "evo-memory", "update memory", "what worked before", "research history", "evolution"
The Learning Layer
Research is iterative. Each cycle — from ideation through experimentation — generates knowledge that should inform the next cycle. Without persistent memory, every new project starts from scratch, repeating mistakes and rediscovering patterns.
Evo-memory solves this by maintaining two structured memory stores and three evolution mechanisms that extract, classify, and inject knowledge across cycles.
Two Memory Stores
Ideation Memory (M_I)
Location: /memory/ideation-memory.md
Records what you've learned about research DIRECTIONS — which areas are promising and which are dead ends.
Two sections:
| Section | What It Contains | Example Entry |
|---|---|---|
| Feasible Directions | Directions that showed promise in prior cycles | "Contrastive learning for few-shot classification — confirmed feasible, top-3 in tournament cycle 2" |
| Unsuccessful Directions | Directions that were tried and failed, with failure classification | "Autoregressive generation for real-time video — fundamental failure: latency constraint incompatible with autoregressive decoding" |
Each entry records: Direction name, one-sentence summary, evidence (which cycle, what results), classification (feasible / implementation failure / fundamental failure), date.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 238 lines · 186 tokens per session scan A 1d301e615715
evo-memory is a skill published in the GitHub repository CamusGIT/EvoQuant (141 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 186 tokens to every session and 3,871 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to evo-memory, differing in 12 lines, and is treated as a copy.
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