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 HBarefoot/engram --skill engram-memorygit clone --depth 1 https://github.com/HBarefoot/engramWrote 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/hbarefoot/engram/engram-memory)<a href="https://agentmods.dev/skills/hbarefoot/engram/engram-memory"><img src="https://agentmods.dev/badge/skills/hbarefoot/engram/engram-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/hbarefoot/engram/engram-memory"><img src="https://agentmods.dev/badge/skills/hbarefoot/engram/engram-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 | $0.00152 | $0.01594 |
| Opus 5.5 | $0.00061 | $0.00638 |
| Sonnet 5.5 | $0.00030 | $0.00319 |
| Haiku 4.5 | $0.00015 | $0.00159 |
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 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engram Memory
Engram gives you memory that survives the end of this conversation. Treat it like a colleague's notebook: read it before you start, write in it when you learn something the next session will need. A session that starts with recall feels like continuity; a session that ends with a write-back makes the next one smarter.
The rhythm
Session start — recall before acting:
engram_recall { query: "<the project or topic you're about to work on>", limit: 5 }
Scope with namespace when you know the project (see Namespaces). If you're about to do substantial work in a known context, prefer engram_context — it returns a pre-formatted block of the most relevant memories in one call:
engram_context { query: "<topic>", namespace: "<project>", max_tokens: 500 }
During the session — write when durable knowledge appears (see "What deserves a memory").
Session end — after completing significant work, store the outcome: what shipped, what broke, what was decided and why. This is the single highest-value write; it's what makes the next session start from "here's where we left off" instead of zero.
What deserves a memory
Store it when a future session would act differently for knowing it:
- The user tells you to — "remember", "don't forget", "for future reference". Always store, confidence 1.0.
- Decisions with their why — "we chose X over Y because Z". The rationale is the valuable part; a decision without its why gets relitigated.
- Corrections — the user corrects a wrong assumption you made. These are gold: they prevent the same mistake in every future session.
- Preferences — how the user likes to work, tools they favor, styles they reject.
- Setup and infrastructure facts — versions, hosts, ports, conventions, account structures, "the staging DB is the one named prod2" landmines.
- Outcomes — what a completed piece of work produced, including failures and dead ends ("tried X, doesn't work because Y" saves the next session from repeating it).
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 · 88 lines · 152 tokens per session scan A 41f9e440719a
engram-memory is a skill published in the GitHub repository HBarefoot/engram (9 stars, last pushed 3mo ago), licensed MIT. It adds 152 tokens to every session and 1,594 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
crystalline-capture
Use when durable knowledge is learned while working, when the user asks to update a Crystalline domain, or before writing or editing an engram so it is deduplicated, well-formed and linked correctly.
crystalline-intelligence
Use when the crystalline MCP tools are available and the task involves recalling, storing or sharing knowledge - searching domains, capturing engrams or sharing with a team.
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.
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.
build-with-tinybase
Scaffold, extend, and verify reactive local-first JavaScript or TypeScript applications with TinyBase. Use when choosing TinyBase for in-memory tabular or key-value state, generating an app with create-tinybase, adding schemas or UI bindings, resolving TinyBase import paths, configuring browser or database…