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 skills/plur-ai/plur/effective-memorynpx skills add plur-ai/plur --skill effective-memorygit 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/effective-memory)<a href="https://agentmods.dev/skills/plur-ai/plur/effective-memory"><img src="https://agentmods.dev/badge/skills/plur-ai/plur/effective-memory.svg" alt="Measured on agentmods" 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 | $0.00044 | $0.00792 |
| Opus 5 | $0.00022 | $0.00396 |
| Sonnet 5 | $0.00009 | $0.00158 |
| Haiku 4.5 | $0.00004 | $0.00079 |
Grade A, and why
Effective Memory 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 4d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Effective Memory
Your agent has memory. These habits make it actually useful.
Without them, memory is a growing pile of assertions nobody retrieves. With them, memory compounds — each session builds on the last, corrections stick, and the agent gets measurably better over time.
This pack is pinned in PLUR 0.9.4+. Engrams here bypass keyword gating and are always eligible for injection at session start. They cover the meta-rules every agent needs regardless of domain: how to capture corrections, when to recall before answering, what "verified" means, how to stay safe with destructive actions, and why never to type a weekday from memory.
Install
npx @plur-ai/[email protected] packs install effective-memory
(In 0.9.4+, plur init auto-installs this pack — manual install is rarely needed.)
What's inside
12 engrams covering:
- Capture — call
plur_learnimmediately on corrections; detect correction-shaped phrases. - Recall —
plur_recallbefore factual answers; don't confabulate. - Session lifecycle — bookend with
plur_session_start/plur_session_end;plur_feedbackon injected engrams;plur_timelinefor long-horizon agents. - Verification — artifact-first; never bulk-mark as done from narrative text.
- Safety — irreversible actions need actual user confirmation and one-item dry-runs.
- Discipline — read before edit; don't ask "want to continue?" mid-task.
- Time — never type a day-of-week from memory.
Why pinned
Pinned engrams (introduced in PLUR 0.9.4) bypass the keyword-relevance gate in scoreEngram and per-pack/per-domain caps in fillTokenBudget. They are always eligible for injection regardless of how the user's query keywords overlap with the engram statement. Use this for cross-cutting meta-rules only; pinning everything defeats the purpose.
Versioning
| Version | Changes |
|---|---|
| 1.1.0 | Consolidated plur-required meta-rules into this pack. All engrams now pinned: true. Added verification, safety, discipline, and time-handling rules. Engram count 8 → 12. |
| 1.0.0 | Initial pack — 8 engrams covering session bookends, learning triggers, and feedback loops. |
What ships with it
1 file 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.
- 4d ago First seen · 59 lines · 44 tokens per session scan A bb98da8a3114
Effective Memory is a skill published in the GitHub repository plur-ai/plur (245 stars, last pushed today), licensed Apache-2.0. It adds 44 tokens to every session and 792 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.
Other skills, from other repositories
emulo
Use when the user explicitly asks to run, set up, update, re-mine, or deepen Emulo from real local AI coding-session history and native emulo:mine is not available. This is the cross-agent skills.sh bootstrap, not the native namespaced plugin.
emulo
Load the user's Emulo profile, mined from their local Claude Code, Codex, and OpenCode session logs, so you work like them instead of a cold start. Use before working on their task.
mengram-memory
Long-term memory with 3 types (facts, events, workflows). Remember user preferences, past conversations, and learned procedures across sessions. Use when recalling what the user said before, saving important info, getting user context, or tracking completed workflows.
marm-init
Guided MARM MCP setup. Invoke after running marm-memory init on the CLI to configure MARM memory across your agent. Drives transport choice, runtime choice, MCP config writing, multi-agent linking, and server start. Works on Claude, Codex, Gemini, Qwen, Cursor, VS Code, and other MCP-capable agents.
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…