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 nikita-rulenko/Helixir --skill helixir-memorygit clone --depth 1 https://github.com/nikita-rulenko/HelixirWrote 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/nikita-rulenko/helixir/helixir-memory)<a href="https://agentmods.dev/skills/nikita-rulenko/helixir/helixir-memory"><img src="https://agentmods.dev/badge/skills/nikita-rulenko/helixir/helixir-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/nikita-rulenko/helixir/helixir-memory"><img src="https://agentmods.dev/badge/skills/nikita-rulenko/helixir/helixir-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00058 | $0.02693 |
| Opus 5.5 | $0.00023 | $0.01077 |
| Sonnet 5.5 | $0.00012 | $0.00539 |
| Haiku 4.5 | $0.00006 | $0.00269 |
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 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.
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:
- Use the principal configured by the host, normally
HELIXIR_RBAC_ACTOR(claude,codex, orcursorafter onboarding). - Otherwise use an explicitly assigned agent name, then the OS user as a last resort.
- Use the same lower-case value as
actor_idon every tool that accepts it. - Use one stable
user_idfor memory ownership. It may equalactor_id, but it is not an authorization credential. - 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:trueis success and must not be retried.- Non-empty
updatedcontains ids of existing memories changed by the decision matrix. memories_added:0plus non-emptydedupedmeans already known.status:"accepted"pluspending_idmeans buffered success; poll withget_add_statusonly when the outcome is needed immediately.needs_clarificationmeans the charter refused a silent conflict. Ask the suggested question or apply an established standing rule.- Only
ok:falseorstatus:"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.
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 · 58 tokens per session scan A 02298307f41c
helixir-memory is a skill published in the GitHub repository nikita-rulenko/Helixir (88 stars, last pushed 2mo ago), licensed MIT. It adds 58 tokens to every session and 2,693 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 skills, from other repositories
weekly-digests
Generate a serial week-by-week narrative digest of a project's full claude-mem timeline. Splits the timeline into per-ISO-week files, then runs one consecutive subagent per week — each receiving the prior week's carry-forward block — to produce one chapter per ISO week of data. Use when asked for "weekly digests"…
cloud-sync
Set up or check claude-mem cloud sync with cmem.ai Pro. Use when the user says "set up cloud sync", "sync my memories", "cmem pro", "cloud backup", "sync status", or wants their memory database backed up or synced to their cmem.ai account.
screenpipe
Search screen and audio history recorded by Screenpipe and optionally save selected context into claude-mem. Use when the user asks to recall desktop activity, a meeting, or something seen in another app through Screenpipe.
memory-retain
Save what was learned to Hindsight long-term memory. Use when a task finishes, when the user states a fact, decision or preference, and when you discover something a later run or another Bot would need.
hivemind-memory
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.