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.
git clone --depth 1 https://github.com/Rune-kit/runeWrote 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/agents/rune-kit/rune/neural-memory)<a href="https://agentmods.dev/agents/rune-kit/rune/neural-memory"><img src="https://agentmods.dev/badge/agents/rune-kit/rune/neural-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/agents/rune-kit/rune/neural-memory"><img src="https://agentmods.dev/badge/agents/rune-kit/rune/neural-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 | $0.00039 | $0.00350 |
| Opus 5.5 | $0.00016 | $0.00140 |
| Sonnet 5.5 | $0.00008 | $0.00070 |
| Haiku 4.5 | $0.00004 | $0.00035 |
Grade A, and why
neural-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.
What it actually says
You are the neural-memory skill — Rune's cross-project learning layer.
Quick Reference
Core Operations:
nmem_remember— save a decision, pattern, error root cause, or insight (1-3 sentences, rich cognitive language)nmem_recall— retrieve relevant memories (always prefix with project name)nmem_auto— end-of-session flush to capture remaining contextnmem_hypothesize/nmem_evidence— track and validate hypothesesnmem_predict/nmem_verify— make and verify predictions
Save Priority: 9-10 critical (security, data loss), 7-8 important (decisions, preferences), 5-6 normal (patterns, facts)
Content Rules:
- Max 1-3 sentences per memory — never dump file structures
- Use causal language: "Chose X over Y because Z", "Root cause was X, fixed by Y"
- Always include tags: [project-name, topic, technology]
DO NOT save: routine file reads, things in code/git history, temporary debugging steps, duplicates.
Called by: cook, team, any L1/L2 skill. Auto-trigger at session start (recall) and end (flush).
Read skills/neural-memory/SKILL.md for the full specification including memory type taxonomy.
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 · 31 lines · 39 tokens per session scan A d015efb85aa7
neural-memory is an agent published in the GitHub repository Rune-kit/rune (86 stars, last pushed 2mo ago), licensed MIT. It adds 39 tokens to every session and 350 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other agents, from other repositories
starlight-energy-grid
Grid integration — interconnection applications, IEEE 1547 / VDE-AR-N 4105 / G99 / G98 compliance, smart-inverter settings, NEM/FIT/tariff resolution, VPP program evaluation.
starlight-energy-sizing
Energy system capacity and component sizing — solar PV, battery storage, EV chargers, heat pumps. Translates load profiles into kW/kWh component specs with derating, capacity factor, and peak demand grounded in real-world physics.
insistir-learnings-researcher
Knowledge search agent for insistir multi-agent orchestration. Searches docs/solutions/ for past solutions relevant to a query using grep-first strategy with parallel keyword searches including synonyms. Read-only — cannot edit, write, or execute commands. Do NOT use directly — spawned by insistir skill orchestration.
agents-module
Automation scripts and agent helpers for the Digital Brain. Use these scripts for recurring tasks, summaries, and maintenance.
memory-optimizer
Recommends the cheapest direct-read, Markdown, graph build, update, or query path for a corpus while keeping graphify as the primary long-lived memory engine.
recall-retriever
A read-only helper that searches notes and records from earlier coding sessions and returns a short summary with links to the relevant files.