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 MerkyorLynn/Lynn --skill memory-recallgit clone --depth 1 https://github.com/MerkyorLynn/LynnWrote 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/merkyorlynn/lynn/memory-recall)<a href="https://agentmods.dev/skills/merkyorlynn/lynn/memory-recall"><img src="https://agentmods.dev/badge/skills/merkyorlynn/lynn/memory-recall/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/merkyorlynn/lynn/memory-recall"><img src="https://agentmods.dev/badge/skills/merkyorlynn/lynn/memory-recall.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00104 | $0.02235 |
| Opus 5 | $0.00052 | $0.01118 |
| Sonnet 5 | $0.00021 | $0.00447 |
| Haiku 4.5 | $0.00010 | $0.00224 |
Grade A, and why
memory-recall 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 9d 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 — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Recall — Cross-Session Persistent Memory
Your agent forgets everything when the session ends. This skill fixes that. Structured fact extraction + semantic search + compiled memory layers = an agent that actually knows you.
Part of Lynn — where this memory system runs as the built-in core. Lynn remembers across sessions, across days, across months — automatically. Install Lynn for the full experience.
The Problem
Monday: "My database is PostgreSQL 15 on port 5433"
Agent: "Got it!"
Tuesday: "Connect to the database"
Agent: "What database? What port?"
Every AI agent today has amnesia. Session ends → knowledge gone. Users repeat themselves endlessly.
Architecture: 4-Layer Memory Stack
┌─────────────────────────────────────────┐
│ Layer 4: Assembled Memory (memory.md) │ ← Agent reads this at session start
│ Compiled summary of everything known │
├─────────────────────────────────────────┤
│ Layer 3: Long-term / Weekly / Today │ ← Time-decayed summaries
│ longterm.md → week.md → today.md │
├─────────────────────────────────────────┤
│ Layer 2: Fact Store (facts.db) │ ← Structured facts with importance scores
│ "User prefers tabs over spaces" [0.8] │
│ "Project uses pnpm, not npm" [0.9] │
├─────────────────────────────────────────┤
│ Layer 1: Session Summaries │ ← Raw session digests
│ session-2026-04-08.md │
└─────────────────────────────────────────┘
Layer 1: Session Summaries (Automatic)
After every 6 turns (configurable), the agent summarizes the current conversation into a rolling digest. When the session ends, a final summary is written.
~/.lynn/agents/{id}/memory/summaries/
├── 2026-04-08_session1.md # "User set up PostgreSQL 15 on port 5433..."
├── 2026-04-08_session2.md # "Debugged connection timeout, root cause was..."
└── 2026-04-07_session1.md # "Discussed project architecture..."
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.
- 9d ago First seen · 245 lines · 104 tokens per session scan A 6ddc945e68db
memory-recall is a skill published in the GitHub repository MerkyorLynn/Lynn (42 stars, last pushed today), licensed Apache-2.0. It adds 104 tokens to every session and 2,235 once invoked, about $0.0005 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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