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 agentmods add instructions/toejough/engram/claude-mdgit clone --depth 1 https://github.com/toejough/engramWhat 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 | $0.02166 | $0.02166 |
| Opus 5 | $0.01083 | $0.01083 |
| Sonnet 5 | $0.00433 | $0.00433 |
| Haiku 4.5 | $0.00217 | $0.00217 |
Grade A, and why
engram CLAUDE.md 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 2d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engram
Persistent memory for LLM agents, backed by an agent-memory zettelkasten vault. Two skills — recall and learn — read from and write to the vault on demand; at their write sites they hand off to a fifth skill, write-memory, which composes and executes the vault-write commands (parents judge, the worker writes). A third skill, please, orchestrates end-to-end work by sequencing recall, learn, and other available skills around a user's <ask>, with adversarial review gates over the plan, refactors, docs, and outward prose. A fourth skill, route, encodes the delegate-everything doctrine: it guides subagent selection (agent type, model, effort) rather than doing object-level work itself. please consults it when assigning gate reviewers. A sixth skill, curate, judges engram serve's pending-offer notes against the host vault using the same covered/near/absent reasoning recall's Step 2.5 documents, and is self-contained — it composes and executes engram amend directly rather than handing off to write-memory, since an offer's content already exists as a note file with nothing left to compose from scratch.
Core Principles
- Verify, don't guess. Always read source code and documentation before guessing at formats, paths, or API contracts. Never assume directory structures or payload fields — verify first.
Directory Structure
engram/
├── cmd/engram/ # CLI binary entry point — single-statement main() + checker-thin per-group functions supplying raw I/O primitives (targ check-thin-api-enforced)
├── internal/ # Non-public implementation
│ ├── chunk/ # Splits memory sources (stripped transcripts, markdown) into embedding-sized chunks for the auto-ingested vector space (pure string logic, no I/O)
│ ├── cli/ # CLI command wiring (targ targets) + composition root: cli.NewDeps builds every production adapter from injected cli.Primitives
│ ├── cluster/ # k-means clustering with silhouette-based auto-K, for recall clustering
│ ├── context/ # Transcript processing for LLM agents
│ ├── debuglog/ # Tail-friendly debug logger
│ ├── embed/ # Embedder interface + Hugot/GoMLX backend, sidecar I/O, state classification
│ ├── luhmann/ # Luhmann zettelkasten ID parsing/sorting
│ ├── transcript/ # Claude Code session transcript reading
│ ├── update/ # `engram update` subcommand
│ └── vaultgraph/ # Wikilink graph analysis of the vault
├── agent-instructions/
│ ├── skills/ # Source for the recall, learn, write-memory, please, and route skills
│ └── guidance/ # Source for the deployable ambient guidance docs — recall-firing (`recall.md`), delegation-firing (`delegate.md`), and learn-firing (`learn.md`) — synced by `engram update --with-guidance` to canonical paths in `~/.claude/engram/guidance/` (Claude Code) and `~/.pi/agent/engram/guidance/` (Pi); compat symlinks at old flat paths keep existing `@import` lines in CLAUDE.md / AGENTS.md resolving (ADR-0022 D9); activated via `@import`
├── openspec/ # Primary behavior specs — one per shipped capability; backfilled 2026-07-27 from docs/FEATURES.md surface
├── dev/ # Build tooling (targ definitions, linter configs)
└── docs/ # Organized by charter — see docs/README.md
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.
- 2d ago First seen · 103 lines · 2,166 tokens per session scan A b4eb4f6fc279
engram CLAUDE.md is an instructions file published in the GitHub repository toejough/engram (8 stars, last pushed 3d ago), licensed Apache-2.0. It adds 2,166 tokens to every session, about $0.0108 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 2026-08-31.
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