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 20alexl/claude-engram --skill engramgit clone --depth 1 https://github.com/20alexl/claude-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/20alexl/claude-engram/engram)<a href="https://agentmods.dev/skills/20alexl/claude-engram/engram"><img src="https://agentmods.dev/badge/skills/20alexl/claude-engram/engram/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/20alexl/claude-engram/engram"><img src="https://agentmods.dev/badge/skills/20alexl/claude-engram/engram.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 27 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
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 | $0.00041 | $0.02256 |
| Opus 5 | $0.00020 | $0.01128 |
| Sonnet 5 | $0.00008 | $0.00451 |
| Haiku 4.5 | $0.00004 | $0.00226 |
Grade A, and why
engram 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 10d 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.
Claude Engram — Quick Reference
Automatic (hooks, zero invocation)
- Edit/error/decision tracking, loop warnings, compaction survival
- Session mining: background indexing after every session PLUS debounced live ticks at turn end — search/extractions/code-index stay fresh mid-session (CLAUDE_ENGRAM_LIVE_MINE, default 300s)
- Embeddings: resident daemon on cpu (zero VRAM parked); bulk jobs (512+ texts) run in a transient GPU worker that exits after the job (CLAUDE_ENGRAM_DEVICE forces one device; status shows the daemon's device)
- Smart session start: last session context + recurring patterns
- Predictive context: related files + likely errors before edits
- Pre-edit import/export check: proposed imports verified against the per-project code index (AST, LLM-free) —
<engram-precheck>banner with closest-name suggestions - Blast-radius: editing a shared module lists its importers —
<engram-blast-radius> - Read context: before Read of an indexed file, code-index orientation + that file's memories (
<engram-read-context>, once per file per session) - Error deja-vu: a failure matching a known recurring error gets the past fix injected inline at failure time ("Deja vu: TypeError hit in 3 past session(s) - fix: ...")
- Known-good test commands: session start lists the project's tracked test commands that currently pass
- Mistake hygiene: stale machine-written one-off mistakes (3+ weeks, never recurred, away from current work) auto-archive in the background — restorable via
memory(restore); failing TEST runs are never logged as mistakes (TDD-aware) - Lessons bridge (opt-in): dated entries in curated note files sync as protected
lessonmemories with code-index triggers — enable withlessons_globsin ~/.claude_engram/config.json - Session-start patterns are project-scoped: recurring errors/struggles filter to the sub-projects the last session touched; errors quiet 30 days drop out
- Outcome feedback loop: tracks which injection kinds (memory/prediction/precheck/blast) precede passing tests AND feeds back a bounded (0.8-1.2x) memory-injection multiplier; see
session_mine(reflect) - Tool duration tracking: slow tools surfaced in handoffs
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.
- 10d ago First seen · 110 lines · 41 tokens per session scan A 9c146c1c1bde
engram is a skill published in the GitHub repository 20alexl/claude-engram (16 stars, last pushed 13d ago), licensed MIT. It adds 41 tokens to every session and 2,256 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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