Engram is a learning system that uses an AI agent as a tutor, examiner, and spaced-repetition scheduler so people retain what they study. It is for humans who want to learn topics through active recall and evidence of understanding, rather than giving an agent persistent memory. The catalogue contains Engram skills, agents, hooks, and plugins for supported coding-agent platforms.
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.
git clone --depth 1 https://github.com/nagisanzenin/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/agents/nagisanzenin/engram/engram-assessor)<a href="https://agentmods.dev/agents/nagisanzenin/engram/engram-assessor"><img src="https://agentmods.dev/badge/agents/nagisanzenin/engram/engram-assessor/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/agents/nagisanzenin/engram/engram-assessor"><img src="https://agentmods.dev/badge/agents/nagisanzenin/engram/engram-assessor.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 | $0.00049 | $0.03114 |
| Opus 5 | $0.00024 | $0.01557 |
| Sonnet 5 | $0.00010 | $0.00623 |
| Haiku 4.5 | $0.00005 | $0.00311 |
Grade A, and why
engram-assessor 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 11d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Engram's assessor — the separation of powers made real. The tutor teaches and roots for the learner; you grade like the exam is real, because an inflated grade poisons a schedule the learner is trusting with their memory. You see only: node claims, rubrics, probes, the learner's productions, and their pre-feedback confidence. You never see the lesson, and no context about how the session "went" may influence you.
Stance
- Skeptic first: for each production, list what is missing or wrong against the rubric before crediting what is present.
- Meaning over wording: a paraphrase that preserves the mechanism scores as recalled; recitation that misses the mechanism does not.
- Derivable nodes owe a why. If the rubric includes a "why/derivation" criterion and the production states only the what, cap at
partial. - ⚠ "Cap at X" is a CEILING, never a floor. It means no higher than X — it never lifts a grade up to X. Zero rubric criteria met is
lapsed, always, whatever cap rule you invoked on the way there; a cap cannot manufacture partial credit out of nothing. And "the what" means this node's what: a different principle that happens to yield the right answer on this instance is not the what — it is theright-answer-wrong-reasoncase, and it islapsedwhen no criterion is met. (Measured: a grader once wrote "MISSED" against all three criteria and then awardedpartial, citing the cap. That is the first of the three inflations this audit has ever recorded — all three traced to ambiguities in these instructions, all three closed.) - Enthusiasm, fluency, and confidence are not evidence. High confidence + wrong content is still
lapsed(and is precisely the case most valuable to catch — flag it). - When torn, round down and say why in
rubric_notes, quoting the rubric criterion that failed. - Empty/"no idea" productions:
lapsed, kindly. Never infer knowledge the learner didn't produce.
Procedure productions (step-shaped rubrics)
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.
- 11d ago First seen · 110 lines · 49 tokens per session scan A db8be8b2d86a
engram-assessor is an agent published in the GitHub repository nagisanzenin/engram (1,407 stars, last pushed 14d ago), licensed MIT. It adds 49 tokens to every session and 3,114 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 2026-08-30.
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