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 plur-ai/plur --skill plur-create-engramsgit clone --depth 1 https://github.com/plur-ai/plurWrote 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/plur-ai/plur/plur-create-engrams)<a href="https://agentmods.dev/skills/plur-ai/plur/plur-create-engrams"><img src="https://agentmods.dev/badge/skills/plur-ai/plur/plur-create-engrams/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/plur-ai/plur/plur-create-engrams"><img src="https://agentmods.dev/badge/skills/plur-ai/plur/plur-create-engrams.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00066 | $0.03056 |
| Opus 5 | $0.00033 | $0.01528 |
| Sonnet 5 | $0.00013 | $0.00611 |
| Haiku 4.5 | $0.00007 | $0.00306 |
Grade A, and why
plur-create-engrams 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 today.
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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create PLUR Engrams
An engram earns its place by changing a future answer, action, or interpretation. Two failures cost more than a missing memory: one that is too long to be read, and one that is never retrieved when it applies. Most of this skill is about those two.
Read PLUR format and compatibility before serializing. Use the spectrum catalog and examples explained for worked records; load only the ones you need.
The four fields do different jobs
Getting this wrong is the main cause of bloat. Each field has one job:
| Field | Job | Test |
|---|---|---|
statement |
The assertion. What to do or what is true. | Could someone act on this alone? |
rationale |
The mechanism that makes it true — and therefore when it stops being true. | Does it name a condition that could fail? |
source |
Where it came from: incident, date, speaker, document. | Is this a citation rather than a reason? |
tags / domain |
Where it should surface. | See Findability below. |
rationale is not "more explanation". "Because the user said so on 12 March" is a citation and belongs in source. A real rationale is falsifiable: if its mechanism stopped holding, the rule should be retired.
Worked example. A real engram, 1,454 characters:
A TRUNCATED memory injection means you do not have your memory — stop and read it in full before doing anything else. When plur_session_start returns more than the harness tool-result limit, the payload is spilled to a file and you receive only a pointer… Proven 2026-09-07: session_start returned 115,052 chars, ~5,000 were read (4%), and four engrams forbidding what happened next were in the unread 96% — ENG-…, ENG-…. A customer name was then narrated to a live audience. Nothing malfunctioned… Rule: on any spilled injection, slice the whole file in ~80,000-char spans…
Eleven claims in one record. The instruction appears in sentence 1 and again at character 1,180; everything between is justification written to pre-empt disagreement. Rewritten, 198 characters:
What ships with it
5 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.
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.
- today First seen · 151 lines · 66 tokens per session scan A a5fbfe332eef
plur-create-engrams is a skill published in the GitHub repository plur-ai/plur (248 stars, last pushed today), licensed Apache-2.0. It adds 66 tokens to every session and 3,056 once invoked, about $0.0003 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 2026-09-08.
Other skills, from other repositories
mnemo-cortex
Installs and wires Mnemo Cortex (local-first persistent memory) into OpenClaw and other MCP-capable agents. Use for cross-session recall, decision history, or multi-agent shared memory.
honcho-integration
Integrate Honcho memory into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, and accessing Honcho's representation.
honcho-memory
Concepts and strategy for using a connected Honcho as persistent memory of the user — the recall/record loop and session and peer design. Start here to understand how Honcho memory works, then connect — via a first-class integration for your environment if one exists (preferred), or raw MCP tools (covered here) or the…
verify
Build, launch, and drive a local Honcho stack to verify a change at its runtime surface (the /v3 HTTP API and the deriver queue). Use when verifying a diff or confirming a change works in the running app.
honcho-memory
Gives AI agents persistent memory across conversations using Honcho. Automatically saves and retrieves user context so the AI remembers preferences, history, and facts between sessions. Use when you need the AI to remember past conversations, recall what a user has told it, inject relevant context into prompts, or…
honcho-cli
Inspect and debug Honcho workspaces via the honcho CLI. Use when investigating peer representations, memory state, session context, or dialectic quality — any task that requires introspection of a Honcho deployment, including verifying that a recall/record memory loop is actually working.