Borrowing it
Nothing to install: this file belongs to Smart-AI-Memory/attune-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Smart-AI-Memory/attune-ai/main/.agents/skills/personal-memory/SKILL.mdgit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote 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/smart-ai-memory/attune-ai/personal-memory)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/personal-memory"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/personal-memory/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/smart-ai-memory/attune-ai/personal-memory"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/personal-memory.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 | $0.00048 | $0.00903 |
| Opus 5.5 | $0.00019 | $0.00361 |
| Sonnet 5.5 | $0.00010 | $0.00181 |
| Haiku 4.5 | $0.00005 | $0.00090 |
Grade A, and why
personal-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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Personal Memory
IMPORTANT: Start your response with a context preamble.
Call help_lookup(topic="personal-memory", mode="preamble") and
display the returned preamble text as a blockquote. Then tell the
user they can say "tell me more" for a step-by-step guide, or answer
the scoping question below to proceed.
If the MCP call fails, fall back to:
Personal Memory — Deliberately capture and recall curated cross-session knowledge — decisions, preferences, and findings — organized by topic so it persists across projects and sessions.
How this differs from the other memory skills
attune has three memory surfaces; pick the right one:
| You want to… | Use |
|---|---|
| Deliberately save a decision/preference/finding to recall later, by topic | this skill (personal_memory_*) |
| Store a classified or cross-agent pattern in project memory | memory-and-context (memory_*) |
| Pull findings auto-stashed from past sessions + the lessons corpus | recall (session_stash) |
This skill is the curated, intentional store: content is polished and
filed under a topic (and optional kind), not auto-captured.
Scoping
Before running, ask which operation the user needs:
- Operation: "Capture something, recall by query, list your topics, or forget a topic?"
- For capture: "What topic should it live under, and is this global or project-local?"
- For recall: "What should I search for?"
Execution
Call the matching MCP tool:
personal_memory_capture
Save a decision, pattern, finding, or reference to personal memory.
Parameters:
- topic (required): The topic to file this under.
- content (required): The text to store (it is polished before storage).
- kind (optional): A sub-category within the topic (e.g.
decision,preference,reference). - project_local (optional, boolean): Store only for the current project instead of globally. Default is global.
personal_memory_capture(
topic="release-process",
content="Always verify the merge SHA contains the version bump before tagging.",
kind="decision",
)
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 First seen · 121 lines · 48 tokens per session scan A 1c497ad81272
personal-memory is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (11 stars, last pushed yesterday), licensed Apache-2.0. It adds 48 tokens to every session and 903 once invoked, about $0.0002 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 2026-08-31.
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mnemosyne-memory
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