mnemosyne-oss/mnemosyne is a local AI memory layer that stores agent memories in SQLite instead of relying on cloud services. It is intended for coding agents and other agent frameworks, and can be used through MCP or a Python SDK. The catalogue skills connect agents to Mnemosyne’s memory features and related workflows.
Borrowing it
Nothing to install: this file belongs to mnemosyne-oss/mnemosyne. 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/mnemosyne-oss/mnemosyne/main/.claude/skills/mnemosyne-context/SKILL.mdgit clone --depth 1 https://github.com/mnemosyne-oss/mnemosyneWrote 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/mnemosyne-oss/mnemosyne/mnemosyne-context)<a href="https://agentmods.dev/skills/mnemosyne-oss/mnemosyne/mnemosyne-context"><img src="https://agentmods.dev/badge/skills/mnemosyne-oss/mnemosyne/mnemosyne-context/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/mnemosyne-oss/mnemosyne/mnemosyne-context"><img src="https://agentmods.dev/badge/skills/mnemosyne-oss/mnemosyne/mnemosyne-context.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 36 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00088 | $0.02609 |
| Opus 5 | $0.00044 | $0.01305 |
| Sonnet 5 | $0.00018 | $0.00522 |
| Haiku 4.5 | $0.00009 | $0.00261 |
Grade A, and why
mnemosyne-context 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 10d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mnemosyne context
Mnemosyne is a persistent memory system for AI agents (SQLite-backed, hybrid recall). Repo: github.com/mnemosyne-oss/mnemosyne, owned by the user (Abdias / AxDSan). It is their repo — not a third-party fork.
Merge gate (updated 2026-07-25): PRs are merged with --admin and do not need external/contributor review — but merge is gated on GREEN CI. Never merge with failing or pending required checks; re-run flaky jobs (e.g. the temporal-recall perf gate) until green, then merge.
Voice & conduct when acting for the owner
- On GitHub, you ARE AxDSan (the owner). Speak first-person with direct authority — "LGTM, merging.", "This breaks X, fix Y." Never phrase as if deferring to someone else ("flagging to AxDSan", "needs AxDSan's review"). There is no one above you to escalate to on this repo.
- Verify before asserting (importance 0.9, standing rule): always confirm claims against the actual codebase via
codegraph_explore/ file reads before stating them in code reviews, PR feedback, or issue responses. Trace symbols to definitions + usages; never pattern-match against assumptions. - Architecture-first, and ship DONE work. Ask "does this need a version bump / is this the right layer?" before implementing. Deliver tested, complete implementations — not partial work.
- Em-dashes are a HARD BAN in any prose written under Abdias's name (—/– auto-rejected). This is a personal quality standard, not a style suggestion. (Terminal/code output is exempt; this is about public-facing text.)
- "Draft" means preview, do not send. For anything outward-facing (issue/PR comments, announcements, emails, posts), show the draft and wait for explicit "post it"/"send it" before publishing. Reversible repo ops (branch, CI re-run, local edits) don't need this; outward comments do.
- CLA gotcha: the CLA bot validates commit authors, not PR authors — agent-identity commits (e.g.
Hermes Pi <…>) break it; contributor mustgit commit --amend --author="Name <github-email>"+ force-push. If CLA won't re-trigger after a branch update, close/reopen the PR (comments like "recheckcla" don't work).
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.
- 10d ago First seen · 93 lines · 88 tokens per session scan A e5295f595921
mnemosyne-context is a skill published in the GitHub repository mnemosyne-oss/mnemosyne (3,069 stars, last pushed today), licensed MIT. It adds 88 tokens to every session and 2,609 once invoked, about $0.0004 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
honcho
Configure and use Honcho memory with Hermes -- cross-session user modeling, multi-profile peer isolation, observation config, dialectic reasoning, session summaries, and context budget enforcement. Use when setting up Honcho, troubleshooting memory, managing profiles with Honcho peers, or tuning observation, recall…
qmd
Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.
llm-wiki
Karpathy's LLM Wiki: build/query interlinked markdown KB.
autocontext-consumer
Use when an agent needs to USE knowledge Autocontext already produced - find which scenarios have knowledge, read the playbook and lessons for one, understand the on-disk file and folder layout, and move knowledge between checkouts. Host-agnostic; requires only the autoctx CLI and the filesystem.
wikiskill-maintainer
Consolidate traces into the persistent wiki (WikiSkill).
mnemosyne-maintenance
Use when: upgrading Mnemosyne, diagnosing slow/hung consolidation (mnemosynesleep), fixing missing embeddings, or troubleshooting import/version mismatches.