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 toejough/engram --skill learngit clone --depth 1 https://github.com/toejough/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/toejough/engram/learn)<a href="https://agentmods.dev/skills/toejough/engram/learn"><img src="https://agentmods.dev/badge/skills/toejough/engram/learn/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/toejough/engram/learn"><img src="https://agentmods.dev/badge/skills/toejough/engram/learn.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 | $0.00118 | $0.05087 |
| Opus 5.5 | $0.00047 | $0.02035 |
| Sonnet 5.5 | $0.00024 | $0.01017 |
| Haiku 4.5 | $0.00012 | $0.00509 |
Grade A, and why
learn 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 17d 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 — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn — Sweep Raw Memory, Crystallize Explicit Lessons
Two jobs, in order: (1) mechanically true up the chunk index so every conversation and doc is searchable memory, and (2) write a vault note for every explicit lesson this session — user corrections, explicit save-requests, presented conclusions that were later overturned, and confirmed approaches (user-praised or self-validated). Nothing else.
Raw event memory is AUTOMATIC.
engram ingestchunks and embeds session transcripts and markdown itself — no summaries, no episode notes, no arc detection. The agent's only writing job is crystallizing EXPLICIT lessons. Do not reconstruct the old episode workflow (engram transcript,engram learn episode) — it is gone.
Mid-cycle capture (fast path). When you fire at a CORRECTION moment mid-task — a review, a failing check, or the user just rejected your approach or named a different convention — or at a subagent return whose
LESSONS:line carries a lesson that clears the bar, this is a focused single-note capture, NOT the open/close of a cycle. SKIP Step 1 (sweep) and Step 1.5 (vocab); go straight to Step 2 and crystallize the ONE confirmed correction (hand off to write-memory as always). Do NOT runengram ingest --automid-task — the sweep is a cycle-boundary job, it is wasteful here and can block on a large corpus, and losing the capture to a hung sweep is the failure mode. The closing learn still sweeps.
Step 1 — Sweep (first; the closing learn always sweeps)
engram ingest --auto
That's it. The binary stats every known source (repo markdown, .claude dirs, all session
transcripts), re-chunks and re-embeds only what changed — within one source, existing chunks are
never deleted (append-only history). Across sources, byte-identical content is deduplicated: only
one canonical copy is indexed, and a duplicate's index is removed only once its retained twin is
verified to cover its records (not on hash-match alone) — see engram prune --duplicates for the
retroactive cleanup mode. Unchanged corpus → returns in seconds. Report the one-line tally it
prints (or "memory index up to date").
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- tests/baseline-autonomous-trigger.md 3.8 KB
- tests/baseline-clean-write.md 2.1 KB
- tests/baseline-confirmed-approach.md 2.8 KB
- tests/baseline-hindsight-framing.md 2.2 KB
- tests/baseline-information-not-knowledge.md 2.0 KB
- tests/baseline-project-specific.md 2.3 KB
- tests/baseline-vocab-refit.md 2.6 KB
- tests/README.md 2.7 KB
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.
- 17d ago Changed · +5 lines 7243dfaf8ef1
- 20d ago Changed · +8 lines a217650a2174
- 23d ago Changed · -7 lines 15d457785574
- 26d ago Changed · +14 lines 252b5e3bb238
- 28d ago Changed a941a204986c
- 1mo ago Changed · +26 lines 848159091484
- 1mo ago First seen · 268 lines · 118 tokens per session scan A 9075156a8f4c
learn is a skill published in the GitHub repository toejough/engram (8 stars, last pushed yesterday), licensed Apache-2.0. It adds 118 tokens to every session and 5,087 once invoked, about $0.0005 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.
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