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 michielinksee/linksee-memory --skill skillgit clone --depth 1 https://github.com/michielinksee/linksee-memoryWrote 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/michielinksee/linksee-memory/skill)<a href="https://agentmods.dev/skills/michielinksee/linksee-memory/skill"><img src="https://agentmods.dev/badge/skills/michielinksee/linksee-memory/skill/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/michielinksee/linksee-memory/skill"><img src="https://agentmods.dev/badge/skills/michielinksee/linksee-memory/skill.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 | $0.00612 | $0.08038 |
| Opus 5.5 | $0.00245 | $0.03215 |
| Sonnet 5.5 | $0.00122 | $0.01608 |
| Haiku 4.5 | $0.00061 | $0.00804 |
Grade A, and why
linksee-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 1mo 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 — 738 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Linksee Memory Skill — Connecting the agent's past and future
🧠 Core Principle
This skill is the only way to persist agent growth across sessions.
Claude Code forgets everything when a session ends. The solution the user taught yesterday, the failure you hit today, the decision made three days ago — all of it is normally lost. linksee-memory is the "memory that doesn't disappear" device.
Writes are handled automatically by the Stop hook (already running). But reads require the agent to actively pull. This skill instills that "go look first" habit in the agent.
JP: Claude Code は session が終わると全部忘れる。linksee-memory は「消えない記憶」を作る装置。書き込みは Stop hook が自動でやってくれるが、読み出しはエージェントが能動的にやる必要がある。
📐 The 6 layers — what goes where
Which layer you record into determines later retrieval accuracy.
| Layer | When to use | Example |
|---|---|---|
🎯 goal |
The user states a clear goal | "want to integrate with freee", "want to npm publish" |
📍 context |
Background on when/why this is happening | "because there's a meeting with company X on Wednesday" |
💭 emotion |
User's temperature / tone | "tired", "excited", "stressed", 「疲れた」「焦ってる」 |
🔧 implementation |
Code written, configured, worked / didn't work | success: "OAuth flow works" / failure: "stopped with auth_expired" |
⚠️ caveat |
Lessons you never want to repeat (auto-protected from forgetting) | "freee OAuth expires in 24h", "never edit this file" |
📈 learning |
Learned something new, prior belief updated | "AST chunking beats line diff for token savings" |
Important: caveat layer is automatically protected from forgetting. Pain records are never deleted.
Pin-via-importance (v0.1.0+): Calling remember with importance: 1.0 pins the memory across all layers, protecting it from auto-forget even outside the caveat layer. Use for "mission-critical goals", "key decisions", etc.:
remember({
entity_name: "KanseiLink", entity_kind: "project",
layer: "goal", content: "Plugin Marketplace submission under review",
importance: 1.0 // pin
})
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.
- 1mo ago Changed · +3 lines 4b28303d6857
- 1mo ago First seen · 735 lines · 612 tokens per session scan A fc0b6d4f9d18
linksee-memory is a skill published in the GitHub repository michielinksee/linksee-memory (15 stars, last pushed 1mo ago), licensed MIT. It adds 612 tokens to every session and 8,038 once invoked, about $0.0024 per session on Opus 5.5. 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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