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 agents/rune-kit/rune/neural-memorygit clone --depth 1 https://github.com/Rune-kit/runeWhat 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 | $0.00039 | $0.00350 |
| Opus 5 | $0.00019 | $0.00175 |
| Sonnet 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 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.
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.
- 2d 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 (84 stars, last pushed 17d ago), licensed MIT. It adds 39 tokens to every session and 350 once invoked, about $0.0002 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.
Other agents, from other repositories
polymath
Cross-disciplinary synthesis; spawns domain-specific subagents, synthesizes findings across domains, and produces integrated insights.
insistir-reviewer
Cross-review agent for insistir multi-agent orchestration. Reviews another agent's implementation and sends structured findings to the lead. Read-only — cannot edit or write files. Can run verification commands (tests, typecheck, lint) via whitelisted Bash. Enforces quality without gatekeeping. Do NOT use directly …
insistir-researcher
Research agent for insistir orchestration. Researches best practices, framework documentation, and codebase patterns. Read-only — cannot edit or write files. Do NOT use directly — spawned by insistir skill orchestration.
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.
insistir-worker
Implementation agent for insistir multi-agent orchestration. Implements a task or applies review fixes, commits, reports to lead. Do NOT use directly — spawned by insistir skill orchestration.
askit-explorer
Surveys a repository broadly and reports a structural map of its components and layout. Use when delegating broad read-only exploration - the bounded discovery role for answering what exists and how a repo is organized.