helixir-memory

A persistent graph-based memory system for AI agents. It stores connected facts and decisions so an agent can recall them across tasks and conversations.

In plain words
What is it for?
Use it to recall project history, save durable outcomes, trace why facts are connected, and share memory between agents.
Why use it?
It reduces repeated re-derivation and helps preserve earlier decisions, context, and relationships between facts.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/nikita-rulenko/helixir/helixir-memory
Any agent
npx skills add nikita-rulenko/Helixir --skill helixir-memory
Clone the repo
git clone --depth 1 https://github.com/nikita-rulenko/Helixir

Made for: Claude Code, Codex.

Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,693 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00058 $0.02693
Opus 5 $0.00029 $0.01347
Sonnet 5 $0.00012 $0.00539
Haiku 4.5 $0.00006 $0.00269

Measured 2d ago against content hash 02298307f41c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

helixir-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.

helixir/skills/helixir-memory/SKILL.md · 238 lines

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.

Helixir memory

Treat Helixir as persistent, reasoning-aware memory shared by agents. Recall before re-deriving, and capture durable decisions at the moment they are made.

Establish identity

Choose one stable identity before the first call:

  1. Use the principal configured by the host, normally HELIXIR_RBAC_ACTOR (claude, codex, or cursor after onboarding).
  2. Otherwise use an explicitly assigned agent name, then the OS user as a last resort.
  3. Use the same lower-case value as actor_id on every tool that accepts it.
  4. Use one stable user_id for memory ownership. It may equal actor_id, but it is not an authorization credential.
  5. If identity is uncertain, read the onboarding/client configuration or ask the operator. Only a global admin may use list_users; never silently adopt another principal.

Recall, work, capture

At the start of every non-trivial request, and immediately after a summary or context compaction, call:

search_memory(query="<current topic>", user_id="<stable owner>", actor_id="<principal>")

If personal recall is empty, retry once with scope="collective". Use mode="full" when an expected older fact is absent.

Store decisions, constraints, preferences, goals, outcomes, and hard-won gotchas with add_memory. Do not store secrets, ephemeral chatter, or facts trivially derivable from code or git.

Interpret write results exactly:

  • ok:true is success and must not be retried.
  • Non-empty updated contains ids of existing memories changed by the decision matrix.
  • memories_added:0 plus non-empty deduped means already known.
  • status:"accepted" plus pending_id means buffered success; poll with get_add_status only when the outcome is needed immediately.
  • needs_clarification means the charter refused a silent conflict. Ask the suggested question or apply an established standing rule.
  • Only ok:false or status:"failed" is failure.

Presence is explicit: transport initialization and ordinary reads never create or refresh a lease. A root agent, worker, or sub-agent calls agent_heartbeat immediately on start and at meaningful progress boundaries, using its stable logical actor_id plus a concrete agent_id; writes carrying that agent_id refresh the same lease. One-shot agents call agent_farewell(actor_id, agent_id) when done; that terminal status remains inactive until another explicit heartbeat or attributed write, while the heartbeat window is the crash and idle fallback.

Read the full file on GitHub · 238 lines

Changes

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.

  1. 2d ago First seen · 238 lines · 58 tokens per session scan A 02298307f41c

Subscribe to this mod's changes

helixir-memory is a skill published in the GitHub repository nikita-rulenko/Helixir (86 stars, last pushed 8d ago), licensed MIT. It adds 58 tokens to every session and 2,693 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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