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 chadixearth/graphyloop --skill swarm-memorygit clone --depth 1 https://github.com/chadixearth/graphyloopWrote 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/chadixearth/graphyloop/swarm-memory)<a href="https://agentmods.dev/skills/chadixearth/graphyloop/swarm-memory"><img src="https://agentmods.dev/badge/skills/chadixearth/graphyloop/swarm-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/chadixearth/graphyloop/swarm-memory"><img src="https://agentmods.dev/badge/skills/chadixearth/graphyloop/swarm-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 | $0.00056 | $0.00778 |
| Opus 5 | $0.00028 | $0.00389 |
| Sonnet 5 | $0.00011 | $0.00156 |
| Haiku 4.5 | $0.00006 | $0.00078 |
Grade A, and why
swarm-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 5d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Swarm memory
Memory is only useful if it is read before deciding and written after learning. A store that is written but never searched is a diary; one that is searched but never corrected repeats its mistakes forever.
When to activate
- START of any non-trivial task, before planning.
- END of any task that produced a decision, a gotcha, or a reusable approach.
- When a past decision is being revisited ("why is it done this way?").
Procedure
- Recall first.
memory_searchwith 2-4 concrete keywords from the task (component names, error text, feature nouns). Optionaltypefilter:decision,pattern,lesson,event,task.- Results exist → use them, and say which memory informed the plan.
- Nothing → say nothing and proceed. Never invent a recalled memory.
- Store at the end, one entry.
memory_storewith the right type:decision— a choice made and why (the alternative rejected matters more than the choice).lesson— a gotcha hit and how it was resolved. This is the highest-value type.pattern— a reusable approach with the file that demonstrates it.event— something that happened (a deploy, a migration, an incident).
- Correct, do not accumulate. A wrong or outdated memory gets
memory_forgetwith its id, then a corrected entry. Leaving both means the next session gets contradictory advice with no way to pick.
What makes an entry worth keeping
Good: one dense line, searchable nouns, an anchor.
decision: state lives in <project>/.graphyloop/state.json (was .opencode/graphyloop) — three of four harnesses are not opencode; migration in lib/engine.mjs
lesson: node --test hangs when a failing assert skips stdin.end() on a spawned server — the child holds the stdio pipes; close it in a finally
pattern: file-to-file credential sync (env_sync) keeps values out of the model context — see lib/secrets.mjs
Bad, and why:
"fixed the bug"— unsearchable, no anchor, no cause.- A pasted 200-line diff — the repo already has it; memory is for the why.
"user prefers dark mode"when it was said once about one screen — over-generalized preferences become wrong rules.
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.
- 5d ago First seen · 64 lines · 56 tokens per session scan A 51f7db59c282
swarm-memory is a skill published in the GitHub repository chadixearth/graphyloop (2 stars, last pushed 23d ago), licensed MIT. It adds 56 tokens to every session and 778 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 2026-09-03.
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durable-memory
Git-versioned memory stored in the Aikito workspace, separate from any Agent's built-in memory. Use to search, retrieve, or persist cross-project and project-scoped durable knowledge, historical decisions, user preferences, or verified architecture constraints.
memory
When and how to use Gobby's persistent memory system effectively. Covers decision frameworks for what to remember, how to write durable memories, and maintenance patterns.
mnemon
Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.
mnemon
Persistent memory for MiniMax Code. Recall durable context, store important facts and decisions, and link related memories with the mnemon CLI.
durable-session-state
Persist plans, scope decisions, evidence, and reviewer/critic verdicts to durable files during long or multi-phase tasks so work survives context compaction, session resumes, and handoffs. Use for swarm-mode tasks, before context grows large, when recording approval gates, and when resuming after compaction or a…