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 jnMetaCode/skillet --skill engram-memorygit clone --depth 1 https://github.com/jnMetaCode/skilletWrote 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/jnmetacode/skillet/engram-memory)<a href="https://agentmods.dev/skills/jnmetacode/skillet/engram-memory"><img src="https://agentmods.dev/badge/skills/jnmetacode/skillet/engram-memory.svg" alt="Measured on agentmods" 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 | $0.00045 | $0.00721 |
| Opus 5 | $0.00023 | $0.00360 |
| Sonnet 5 | $0.00009 | $0.00144 |
| Haiku 4.5 | $0.00005 | $0.00072 |
Grade A, and why
engram-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 6d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
engram-memory
You have access to a local, private memory layer (engram). Treat it as your long-term memory: read it before you answer, write to it when something durable happens. Everything stays on the user's machine.
Setup (once)
If the engram_recall / engram_remember MCP tools are available, use those.
Otherwise use the CLI (zero install):
npx @jnmetacode/engram serve & # HTTP API on :7077, or
npx @jnmetacode/engram mcp # as an MCP server
When to RECALL
Before answering anything that may depend on prior context, search memory first:
- "What did we decide about X?" / "Why did we choose Y?"
- Resuming work after a gap ("where were we?")
- Anything referencing a person, project, deadline, or preference you don't see in the current conversation.
npx @jnmetacode/engram recall "pricing decision" --since month
Quote the recalled passage with its citation (file/date) rather than paraphrasing from your own context — the citation is the point.
When to REMEMBER
Persist a memory when the conversation produces something with a shelf life:
- A decision and its why ("we picked Postgres over SQLite because …")
- A user preference ("always use pnpm", "no AI attribution in commits")
- A fact that took effort to establish (a root cause, a benchmark number)
npx @jnmetacode/engram remember "2026-06-10: chose scoped npm names (@org/pkg) because unscoped were taken"
Rules for good memories:
- One fact per memory. Atomic entries rank and recall better.
- Date it. Lead with an absolute date — engram's ranking is time-aware.
- Include the why, not just the what; the why is what future-you needs.
- Don't store what the repo already records (code, git history, docs).
REINFORCE what proved right (self-improving recall)
When a recalled memory turned out to be the correct answer — the fix worked, the user confirmed — say so, and similar future queries will rank that source higher:
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.
- 6d ago First seen · 79 lines · 45 tokens per session scan A 401b4f1a9003
engram-memory is a skill published in the GitHub repository jnMetaCode/skillet (1 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 721 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 2026-08-31.
Other skills, from other repositories
engram-memory
Give the agent durable, local memory with engram — recall past decisions before answering, and persist new decisions, preferences and facts as they happen. Use when work spans sessions or the user says "remember".
remnic-memory-workflow
Shared memory workflow for Claude Code agents connected to Remnic — recall before acting, observe during work, remember at the end. Trigger phrases include "what do you remember about", "save this for later", "any context from last time".
remnic-recall
Search Remnic memories by natural-language query. Trigger phrases include "what do you remember about", "recall anything on", "have we discussed".
remnic-remember
Store a durable memory in Remnic so every connected agent can recall it. Trigger phrases include "remember this", "save this for later", "add a note that".
remnic-search
Run a deep full-text search across every Remnic memory. Trigger phrases include "search memories for", "find anything about", "deep search".
remnic-status
Check the health of the Remnic daemon, stores, and connected clients. Trigger phrases include "is remnic running", "check memory status", "daemon health".