engram

Your AI can explain anything. Engram makes sure you still know it next month.

The mix-up worth clearing first: Engram is not an agent-memory plugin. It doesn’t give your agent persistent memory, context, or knowledge of your codebase — memory MCPs and context tools do that, for the agent. Engram points the other way: it’s a learning system for the human. Your agent becomes a tutor that makes you do the thinking, a blind examiner that checks you actually got it, and a scheduler that brings each idea back right before your brain drops it. The agent doesn’t get smarter. You do — measurably, with receipts.

Born as a Claude Code plugin; the same skills and engine now run on nine agentic platforms — including, as of v1.0.8, one that puts the tutor in your chat app, and, as of v1.12.0, both OpenCode generations (the 2.0 beta rebuilt its plugin API; Engram ships adapters for both in one package):

claude plugin marketplace add nagisanzenin/engram
claude plugin install engram@engram

_{OpenCode: opencode.json is read globally (~/.config/opencode/opencode.json) or per-project; pin to source instead of npm with "plugin": ["git+https://github.com/nagisanzenin/engram.git"]. OpenCode 2.0 beta (opencode2) uses a new plugin API — same package name works there too (V2 auto-selects the right adapter): INSTALL-OPENCODE-V2.md.
Antigravity: The due-review session nudge isn’t ported yet, and the architect and smith subagents are currently dropped by AG 1.1.4’s strict installer. Everything else works the same.
OpenClaw: the nudge needs openclaw config set hooks.internal.enabled true (OpenClaw ignores plugin hooks until internal hooks are switched on), and it fires on /new and /reset rather than every session. Engram’s agents aren’t registered — the skills spawn them through sessions_spawn with isolated context instead, which keeps the assessor blind. Details in INSTALL-OPENCLAW.md.
DeepSeek Harness: developer preview — the port uses only stock dsh capabilities (native ~/.agents/skills discovery, the Claude Code hook bridge for the nudge), so harness drift degrades a surface rather than crashing. Needs a DeepSeek API key. Skills-in-session and the full nudge chain are verified on the real runtime; a model-driven session is not yet — first-run reports welcome. Details in INSTALL-DSH.md.
ZCode: Claude Code-compatible plugin surfaces, so the port is manifest + one hook-format switch + docs. ZCode discards plain SessionStart stdout and logs non-JSON runs as failed — so the shared hook script detects ZCode’s plugin context (ZCODE_PLUGIN_ROOT) and emits the JSON shape that runner parses; on every other platform it prints plain text exactly as before. The blind assessor runs as a fresh-context generic Agent child; declared plugin agents are diagnostic-only in ZCode 3.9.2. A live model-driven session is not yet recorded — first-run reports welcome. Details in INSTALL-ZCODE.md.
Pi: no subagent tool by design — the skills spawn the blind assessor as a fresh pi -p process instead (isolation by process boundary). The nudge is one TUI notice at session start plus one injected message on your first prompt (worst case the next one — the probe never blocks startup). Details in INSTALL-PI.md.}

Then, inside your coding assistant (command spelling per your platform’s row above):

/learn kalman filters        ← or music theory, or Rust lifetimes, or anything

That’s the whole onboarding. No config, no account, no cards to write. Requires python3 (stock macOS/Linux one is fine — stdlib only). One state folder, every platform: learn in one tool, review in another, same schedule.

Wait — what is this?

You already ask Claude to explain things. It explains beautifully. You nod, you feel smart, and ten days later it’s gone — because a chat has no memory of you, no test of whether you really got it, and no plan for the forgetting that starts the moment you close the terminal.

Engram is what’s missing around the explanation: a tutor that makes you do the thinking, an examiner that checks you actually got it, and a scheduler that brings each idea back right before your brain drops it.

Concretely, installing it gives you: three commands (/learn, /review, /coach — exact spelling per platform in the table above), a quiet session nudge that tells you when reviews are due (and says nothing otherwise — on every platform except Antigravity, whose hook port is pending; on OpenClaw it needs one config flag and fires on /new), and a state folder at ~/.claude/learning/ that you own, can read, and share across every platform you use.

 recall
 100% ─┐ just reading                100% ─┐ with engram
       │\                                  │\      ●╌╌╌●╌╌╌╌╌●╌╌╌╌╌╌╌●╌╌
       │ \                                 │ \    ╱    ╲╱      ╲╱
       │  \__                              │  ●──╱
       │     \____                         │
       │          \_______                 │   each ● = a 2–4 minute /review,
   0% ─┴──────────────────── day 30    0% ─┴─  booked just before you'd forget

The loop

  YOU ──→  /learn transformers
            │
            ▼
  ┌────────────────────────────────────────────────────────────────┐
  │  CURRICULUM ARCHITECT                                          │
  │  breaks the topic into a first-principles concept map:         │
  │  "what must be understood before what" — never chapter order.  │
  │  flags the few THRESHOLD concepts † that unlock everything.    │
  └────────────────────────────────────────────────────────────────┘
            │
            ▼
  ┌────────────────────────────────────────────────────────────────┐
  │  THE TUTOR  (your normal Claude chat, under strict rules)      │
  │                                                                │
  │  per concept:  open a question → you PREDICT → struggle a      │
  │  little (hints, not answers) → resolve → you EXPLAIN IT BACK   │
  │                                                                │
  │  threshold concepts get a generated interactive HTML           │
  │  explorable — sliders and prediction gates, not more text.     │
  └────────────────────────────────────────────────────────────────┘
            │   your answers, verbatim (crash-safe stash on disk)
            ▼
  ┌────────────────────────────────────────────────────────────────┐
  │  THE ASSESSOR  (separate agent — grades blind)                 │
  │  sees only the rubric and your words, never the lesson.        │
  │  every grade becomes a receipt on disk. no receipts,           │
  │  no mastery claim.                                             │
  └────────────────────────────────────────────────────────────────┘
            │   receipts
            ▼
  ┌────────────────────────────────────────────────────────────────┐
  │  THE SCHEDULER  (engram.py — deterministic code, not vibes)    │
  │  FSRS-4.5 fits your personal forgetting curves and books       │
  │  each concept's next review just before you'd lose it.         │
  └────────────────────────────────────────────────────────────────┘
            │
            ▼
  next session:  [engram] 6 reviews due (transformers: 6) · ~4 min
                     │
                     ├──→  /review   2–4 minutes of free recall
                     └──→  /coach    weekly: stats · dashboard · tuning

A real session, condensed

From the first live topic taught with Engram (transformer internals). Note who does the thinking:

◆ node 6/13 · nonlinearity-necessity †

engram   Delete σ for a second. The FFN is now just W₂·(W₁·x).
         What is W₂·W₁ — and what has the two-layer FFN collapsed into?

you      i dont know really

engram   Fine — that's a commitment. Forget matrices. W₁ = "×2",
         W₂ = "×3". Run a number through both, nothing in between.
         Two steps of computation… or secretly one?

you      basically 2x3

engram   That's the whole secret, and you just said it. ×2 then ×3 = ×6:
         two linear layers fuse into ONE. Fifty of them? Still one matrix.
         Depth means nothing until a nonlinearity blocks the fuse —
         and that's why σ is not optional.

At session end, the assessor — which never saw that dialogue — graded the learner’s six answers: 1 recalled · 4 partial · 1 first-retrieval, while the tutor was convinced the session went great. The schedule believed the assessor. That separation is the point: your future reviews are built on verified evidence, not on the tutor’s enthusiasm (or yours).

Three commands, one habit

Everything else is ambient: the session hook nudges when reviews are due and is silent otherwise.

Why it works (the science, in one breath)

Engram implements the four most-replicated findings in learning science — and deliberately skips the popular myths (no “learning styles”; that theory failed every controlled test):

1. Structure — knowledge is a graph, so topics are decomposed by chains of necessity (“why must this be true?”), never by chapter order.

2. Generation — the mind keeps what it makes. You predict, attempt, and explain back before being told. Even failed attempts measurably improve what sticks next (the pretesting effect).

3. Retention — testing is the learning (not the measurement of it), and spacing beats bingeing. Free recall on an FSRS schedule fitted to your own review history.

4. Honest adaptation — it adapts from your measured retention, calibration, and error patterns. Confidence is only recorded when you actually state it; grades only exist as written receipts.

5. Motivation & wisdom, honestly — it makes your real competence growth visible at the moment it happens (the memory that now lasts 4× longer — not points or streaks, which backfire on motivated adults), and it carries you through the hard part: struggle named as encoding, lapses absolved not pitied, backlogs met with amnesty. Two new layers, every claim adversarially verified against the primary source — docs/05-affective-layers.md.

6. Visuals that earn their keep (v0.5) — interactive explorables are built when the content rewards manipulation (a parameter to drag, a process that unfolds — declared per concept by the curriculum architect, never inferred from a “visual learner” label), always wrapped in predict → act → explain guidance, because the guidance is what carries the effect (scaffolded simulations beat identical unscaffolded ones, g+ = 0.60). You choose the eagerness (visuals eager|threshold|off), and your own review receipts then measure whether the medium actually holds better for you — docs/06-visual-encoding.md.

7. Procedures are practiced, not recited (v1.1) — some knowledge is a skill executed on instances (an integral, a git rebase, a statistical-test choice — any domain, declared per node by the content, never by a topic label). Those nodes enter by the worked-example ladder (study → complete → faded → solve; examples first is the best-replicated novice result in math, g ≈ 0.44) and are reviewed by solving a fresh variant — new numbers, same structure — next to the problems they’re confused with, because practice format must match use format (transfer d = 0.58 congruent vs 0.28 not). Solutions are step-graded with the arithmetic verified by execution, and a slip is priced gentler than a wrong method. Engram stays a general learn-anything system; this layer wakes only where the content is a skill — docs/11-the-procedure-gap.md.

8. The session doesn’t end on a failed retrieval (v1.5) — when a concept comes back wrong, Engram re-derives it, puts something else in between, and asks again, to one correct recall. Then it guarantees the dose: the first two intervals after encoding are capped so at least three spaced sessions land inside the first month. This is the largest effect in the whole evidence base — one correct recall in each of three spaced sessions beats three crammed into one 68% vs 26% at a week (the honest exposure-controlled size is d ≈ 0.7) — and it buys durability of the practiced item, not transfer, which is exactly what it claims. docs/13 §2.5

9. It fits your memory, and refuses to pretend (v1.6) — the schedule’s parameters are fitted from your own reviews (initial stability at 64 usable reviews, the full vector at 400), and a fit that doesn’t beat your current one is refused rather than shipped. The workload/retention trade-off is drawn for you and never recommended: Anki, with the largest review dataset in existence, removed its own auto-recommendation, and Engram’s receipts carry no per-review durations to price the trade honestly.

10. It adapts — on receipts, with your consent, in writing (v1.8, Article 12) — every change the system suggests is computed from your evidence, offered with that evidence quoted, applied only on a yes, logged to an append-only ledger, and reversible. The families it may steer on are closed: assistance level from demonstrated prior knowledge (the one aptitude-treatment interaction that survived its literature), scheduling from fitted forgetting, one specific fading metacognitive prompt, and small real choices. Styles, traits, personality and time-of-day stay dead — a system that adapts to you is one hallucinated correlation away from a horoscope, and the ledger is what keeps the difference visible.

And the strongest external result, stated honestly. Kestin et al., Scientific Reports, June 2025 (Harvard, n=194): an AI tutor built on exactly this dialogue grammar — one step at a time, never reveal the solution, make them attempt first — produced roughly double the learning gains of an active-learning physics classroom, in less time. The caveat is the whole reason Engram exists: its outcome was an immediate post-test. Nobody has ever measured whether AI-tutoring gains survive to thirty days. That is the question this tool is built to answer, on you, with receipts.

The grader is graded (v0.7)

Engram’s central claim is separation of powers: a blind assessor grades your free recall, and its receipts drive mastery, retention, calibration, and the schedule itself. Which raises the question nobody in this space likes: who grades the grader?

Until v0.7, nobody. The oracle was a vibe — an excellent one, unmeasured. And that hole sat directly under the foundation, because if the grader is lenient, every number Engram has ever shown you is inflated, and the system has no way to find out.

So we built the audit and ran it. Then the gold set failed before the grader did, and that turned out to be the more important result.

The one number that survives, and it is the one that matters

That is a claim about safety, and it is the reason the badge above says what it says. A grader that errs low makes you re-drill something you had earned — annoying, and it costs you time. A grader that errs high tells you that you know something you do not, and you stop reviewing. Only one of those is a trap, and this grader has never walked into it.

And now the part we would rather tell you than have you find

v0.7.0 shipped this section with a QWK 0.93 badge. Then an independent post-release reviewer ran the one test nobody had thought to run: it graded the gold set with a correct grader and with a deliberately fooled one.

The fooled grader scored higher. (1.000 vs 0.990.) The gold set was rewarding leniency. The instrument was inverted.

The cause was five lenient adjudications by the gold set’s own author, every one of the same species: crediting an adjacent fact as partial credit. Majority is not intersection. Consonance is not pitch-set arithmetic. The history of a theory is not its mechanism. The grader had caught all five, three runs out of three — including on a fluent-but-empty item, which means the author was fooled by fluency in the very category built to catch being fooled by fluency.

Correcting them moves agreement from 0.889 to 0.965 and QWK to 0.978. And here is the thing:

That rise is not evidence the grader got better. It is evidence the instrument had been measuring the author’s inconsistency.

Worse — the corrections were prompted by the grader’s own disagreements. So the QWK that follows is circular: an authored gold set cannot validate a grader from the same model family, because when the two disagree and the author concedes, the agreement that follows measures only the author’s willingness to concede. The engine now says so on every single audit, in the read string, until someone who is not the author has adjudicated the set.

That is why the badge is no longer a QWK. 0/258 graded up is a safety property that does not depend on the gold being perfectly calibrated — and correcting the gold downward only made it a stronger claim, because it lowered the bar the grader had to not exceed. It still never did.

One genuine disagreement (g_054) is deliberately left in, because the reviewer read both readings and judged the gold’s defensible. An instrument with no disagreement left in it measures nothing.

The gold set is public — gold/assessor-gold.jsonl, 89 items: fluent-but-empty, terse-but-correct, confident-and-wrong, right-answer-wrong-reason, paraphrase, partial-credit boundary, — new in v1.1 — right-answer-wrong-method, slip-vs-conceptual, fluent-wrong-step, terse-but-correct-solution, and — new in v1.14 — alignment-halo, alignment-surface, alignment-partial (a beautiful analogy-alignment sentence must not lift an empty production, and vice versa). Every corrected item carries a disputed record with its original grade, so the correction is auditable rather than laundered. Run it yourself: /coach audit. Dispute an item — drop it in gold/local-gold.jsonl and it overrides ours (the audit will say it did).

What would actually fix this: one human, who is not us, adjudicating 89 items — and the two categories where the author and the grader now disagree in every run (right-answer-wrong-method, procedure-partial-boundary) are where that human would earn their keep first. That is the highest-value contribution anyone could make to this repository, and until it happens the engine will keep saying so out loud. Since v1.4 there is a procedure for doing it — a 10-anchor calibration gate, then QWK and ordinal Krippendorff’s α with a bootstrap CI, against thresholds fixed before anyone reads the file: docs/ADJUDICATION.md, scored by engram.py adjudication-stats. One external rater corroborates the authored gold; replacing it would take two who agree with each other, and the engine keeps saying that too.

One more thing the literature insists on, and the engine enforces: high consistency is not correctness. A judge has been measured at test–retest 0.992 with a position bias of 0.192 — perfectly reproducible and systematically wrong (docs/07 §3). Engram’s assessor is prompted to be a skeptic, so it is self-consistent by construction — precisely the profile that failure mode wears. So the engine refuses to certify on consistency: above 0.95 test–retest it demands the leniency bias be strictly under the ceiling, fewer than three runs cannot pass at all, and three identical runs are flagged as measuring nothing.

The Commons (v1.0)

The evidence base of learning science is built on undergraduates, word pairs, and 20-minute retention intervals. Almost nothing tests self-directed adults, on hard conceptual material, at 30–90 day horizons, with blind-graded free recall.

That is not a gap anyone chose. It is a gap because, until roughly 2026, grading free recall at scale was impossible — you needed a human to read every answer.

Engram produces exactly that data as a byproduct of being useful, on hundreds of machines, with a measured grader behind every verdict. And the open question is sitting right there: Kestin et al. (Harvard, Scientific Reports, 2025) found an AI tutor built on this exact dialogue grammar produced ~2× the learning gains of an active-learning classroom, in less time — measured on an immediate post-test. Nobody has ever measured whether AI-tutoring gains survive to thirty days.

python3 scripts/engram.py export --contributor "@you"     # writes a FILE. Sends nothing.

Then read the file. It is short, it is yours, and nothing has left your machine.

Four things make that a promise rather than a hope:

• The payload is a WHITELIST. Every field is constructed by name. There is no code path by which a production could arrive — not “we remembered to delete it.” A blacklist is a promise you must keep every release; a whitelist is one you keep by construction.
• The stripped list ships INSIDE the file, so the promise is verifiable by the person making it, not merely asserted at them.
• An unaudited grader cannot contribute. export refuses — a refusal, not a warning. A finding aggregated from unaudited oracles is not a finding; it is noise with a schema. v0.7 gates v1.0.
• The engine has no network code. Not “none by default” — none. A permanent selftest parses the engine’s own AST (not a grep — the first draft found the word curl in its own comment) and fails the build if anyone ever adds import socket to make one thing convenient. export writes a file and stops; the agent posts, via gh, only on an explicit yes.

And it is ATTRIBUTED — we are not going to lie to you about that. gh posts from your account. A “salted anonymous hash” riding inside a signed envelope would be theatre the moment the envelope is signed. You cannot have one-keystroke upload and anonymity; pick one, and say which out loud. Attribution is also the stronger science: a retention study lives on longitudinal linkage — following the same learner across months is the question — so attributed n=100 beats anonymous n=500.

This is not telemetry. It is a consenting, named, informed participant in an open study — which is what every good study has always had. Withdrawal is: it’s a GitHub post, delete it. That is the entire mechanism, deliberately.

Read the whole thing before you decide: CONTRIBUTING-DATA.md.

What it looks like

Your mastery map, any time (/learn shows it, /coach renders the full dashboard):

transformers — Transformers from first principles
██▒▒▒▒▒▒▒▒▒▒▒░░░░░░░░░░░  1 retained · 6 learning · 6 untouched

● contextual-meaning        due 2026-07-09   S=3.7d
◐ residual-stream        †  due 2026-07-06   S=1.4d
◐ nonlinearity-necessity †  due 2026-07-06   S=1.4d
· depth-necessity        †  due —            S=—

Interactive explorables — self-contained HTML with prediction gates (content stays locked until you commit a guess), guided manipulable models, and embedded retrieval prompts. Built for threshold concepts by default; set visuals eager and they’re also built whenever a concept’s own structure rewards manipulation (the curriculum architect declares this per node — features you can drag, processes that unfold); or just ask mid-lesson: “make it visual.” A local HTML dashboard (/coach dashboard) with per-topic maps, retention-by-strength bars vs. the 85% target band, honest calibration, an encoding-medium comparison (do explorable-encoded concepts hold better for you? — your own receipts answer), and your next-7-days forecast. Both live in ~/.claude/learning/artifacts/ — no network, ever.

FAQ

Is this a memory plugin for my agent? No — the other direction, and this is the most common mix-up. Agent-memory tools (memory MCPs, context managers, “give Claude persistent memory” plugins) store knowledge for the agent. Engram uses the agent to install knowledge in you: it tutors, blind-grades your free recall, and schedules reviews against your measured forgetting curve. If you want your agent to remember things, you want a different tool. If you want to still know things a month after the chat, you want this one.

How is this different from just asking Claude to explain? Asking produces understanding; understanding decays on the same curve as everything else. Engram adds the three things a chat can’t: verification (did you actually get it?), memory across sessions (a learner model in files, not context), and a future (every concept has a scheduled next encounter). The explanation is the easy 20%.

Is this Anki? Anki schedules cards you write and grades yourself. Engram teaches the material, writes the assessment from the dialogue, grades it blind, and schedules concepts on the same family of algorithm (FSRS) — with an actual tutor attached. If you love Anki, think: Anki where the deck builds itself from a Socratic lesson and the grader isn’t you.

Non-code topics? Yes — the engine doesn’t care. History, music theory, statistics, anatomy (it routes memorization-heavy content to mnemonics instead of derivation-theater).

Math and STEM specifically? Yes, and since v1.1 with a layer built for the half of STEM that is doing: the architect marks skill-type nodes as procedure, which swaps in worked-example acquisition and solve-a-fresh-variant reviews, step-graded with execution-checked arithmetic (a dropped sign is priced as a slip, not a forgotten idea; a right answer over a wrong method is capped — the answer is not the knowledge). It is not a “math mode”: the same kind fires on a git workflow or a conjugation drill, concepts still run the Socratic grammar, and a topic with no procedure nodes behaves exactly as before. Theory and the adversarial verification behind it: docs/11-the-procedure-gap.md.

What if I just want the answer? Say “just tell me” — it complies immediately, no lecture. It also quietly schedules that concept for earlier review, because told-not-derived decays faster. Your call, honestly priced.

I’m a visual learner — will it build me visuals? Careful — two different things are true. “Visual learner” as a learning style is a debunked theory (matching instruction to a diagnosed style has failed every controlled test), so Engram will never route content by that label. But interactive visuals as a medium are real and measured — strongest exactly when the concept itself is manipulable (a parameter to drag, a process that unfolds) and when the interaction is guided, which is how Engram builds them. So: the content decides what qualifies (each concept carries a declared visual affordance), you decide the eagerness — say “build visuals eagerly” or run python3 scripts/engram.py visuals eager (or threshold/off; you can also just ask “make it visual” on any concept mid-lesson) — and then your own review receipts quietly measure whether explorable-encoded concepts actually hold better for you. /coach shows the verdict with honest sample sizes. Preference honored, evidence in charge: docs/06-visual-encoding.md.

I have ADHD / I keep getting bored and quitting — is there a mode for that? Yes: an opt-in Focus profile. It doesn’t add a game — no XP, streaks, or badges (the evidence says those backfire on motivated adults; see docs/05). It turns up dials Engram already has: one node per session so you can’t drift, your real memory-growth surfaced every review (“this now lasts ~4× longer” — a true stability number, not points), and amnesty whenever you return to a backlog instead of a guilt pile. Two ways to switch it, whichever you like:

• Just say so in /learn or /coach — “I have ADHD, turn on focus mode.”
• Run it yourself: python3 scripts/engram.py focus on (or off, or status).

It’s stored as a declared need in your learner model, honored across all three commands — not a “learning style” (Engram rejects those). Works for anyone who wants it; ADHD just gets the intensity. Full rationale and evidence: docs/05-affective-layers.md.

Where’s my data? ~/.claude/learning/ — learner model, concept graphs, grade receipts, misconception log, artifacts. Human-readable JSON. Your learning state never leaves your machine: the engine (engram.py) is stdlib-only with no network code, and the dashboard is a local file. The one exception is the curriculum architect, which uses web search on the topic and goal you give it when building a new map — so keep secrets out of the goal line, or ask for an offline map. (Override the location with ENGRAM_HOME.)

Why does it keep testing me? Because retrieval is the treatment, not the measurement. A century of memory research in four words: testing is the learning.

.claude-plugin/     plugin.json, marketplace.json          (Claude Code)
.codex-plugin/      plugin.json                            (Codex)
.agents/plugins/    marketplace.json                       (Codex marketplace)
skills/             learn / review / coach  (+ _shared: dialogue grammar, Explorable Contract, subagent spawning)
agents/             engram-curriculum-architect · engram-assessor · engram-artifact-smith  (Claude Code)
codex/agents/       *.toml ports of the three subagents     (Codex)
hooks/              SessionStart re-anchor (Claude Code/Codex) · pre_llm_call port (Hermes) — self-resolving; silent when nothing is due
hooks/engram-due/   HOOK.md + handler.js                    (OpenClaw hook pack — same nudge, same silence)
scripts/engram.py   deterministic core: FSRS-4.5, state, receipts, stats, dashboard, selftest
docs/               theory · prior art · architecture · roadmap
INSTALL-CODEX.md · INSTALL-HERMES.md · INSTALL-OPENCLAW.md · INSTALL-ZCODE.md    per-platform glue, at the repo root

Documents

More from the same workshop

Four Claude Code plugins from the same workshop. Most share one habit: let a deterministic core decide, and never let the producer of work grade it.

• effortmining — benchmark-calibrated per-subagent reasoning effort: dispatch the cheapest tier a blind grader still accepts. ~64.7% fewer output tokens at equal quality, pre-registered.
• idiolect — human-voice writing engine: 60+ measured voices plus a deterministic AI-tell scanner and a blind auditor, so text reads like a person, not a model.
• production-grade — turns “build me X” into a gated multi-agent pipeline (architecture → tests → security → CI/CD) with a receipt for every phase. Engram’s verification patterns started here.
• less — a minimal comms protocol for Claude: a per-turn hook makes replies answer-first, pick-list-driven, and calm, without touching the work.

Stars

If Engram earned its keep, a star helps the next person find it.

_{GitHub restricted the stargazer-timeline API to repo collaborators, so the live history chart no longer renders inline. Click the badge for the interactive graph.}

_{An engram is the physical trace a memory leaves in neural tissue (Semon, 1904; experimentally located by Josselyn, Tonegawa et al. in the 2010s). Building durable ones is literally this plugin’s job. · MIT license · changelog}