learn

learn is a skill for Claude Code, Codex from toejough/engram. It costs 118 tokens per session (5,087 once invoked), scanned A, original, Apache-2.0.

A memory tool that records confirmed lessons from coding sessions in a searchable vault. It also keeps the vault's raw conversation index up to date.

In plain words
What is it for?
Use it after a review, failed check, correction, explicit save request, or completed piece of work that produced a reusable lesson.
Why use it?
It prevents important corrections, saved notes, and confirmed approaches from being lost between tasks. It limits saved lessons to things explicitly established during the session.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it after a review, failed check, correction, explicit save request, or completed piece of work that produced a reusable lesson.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/toejough/engram/learn
Install

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.

Any agent
npx skills add toejough/engram --skill learn
Clone the repo
git clone --depth 1 https://github.com/toejough/engram

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for learn

README.md
[![agentmods](https://agentmods.dev/badge/skills/toejough/engram/learn/github.svg)](https://agentmods.dev/skills/toejough/engram/learn)
Your own site
<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.

agentmods 80×15 button for learn

Your own site · 80×15
<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>
Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,087 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 17d ago against content hash 7243dfaf8ef1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

agent-instructions/skills/learn/SKILL.md · 314 lines

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 ingest chunks 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 run engram ingest --auto mid-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").

Read the full file on GitHub · 314 lines

Files

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.

Changes

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.

  1. 17d ago Changed · +5 lines 7243dfaf8ef1
  2. 20d ago Changed · +8 lines a217650a2174
  3. 23d ago Changed · -7 lines 15d457785574
  4. 26d ago Changed · +14 lines 252b5e3bb238
  5. 28d ago Changed a941a204986c
  6. 1mo ago Changed · +26 lines 848159091484
  7. 1mo ago First seen · 268 lines · 118 tokens per session scan A 9075156a8f4c

Subscribe to this mod's changes

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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