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/sloemo01/hermes-skills-bundle/memory-setupnpx skills add sloemo01/hermes-skills-bundle --skill memory-setupgit clone --depth 1 https://github.com/sloemo01/hermes-skills-bundleWrote 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/sloemo01/hermes-skills-bundle/memory-setup)<a href="https://agentmods.dev/skills/sloemo01/hermes-skills-bundle/memory-setup"><img src="https://agentmods.dev/badge/skills/sloemo01/hermes-skills-bundle/memory-setup.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 | $0.00027 | $0.00959 |
| Opus 5 | $0.00014 | $0.00479 |
| Sonnet 5 | $0.00005 | $0.00192 |
| Haiku 4.5 | $0.00003 | $0.00096 |
Grade A, and why
memory-setup 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 5d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Setup Skill
Overview
This skill provides a guided first-time setup for new users. When triggered, it runs a 5-question interactive flow using the clarify tool and saves the answers to the user's persistent memory (~/.hermes/memories/memory.md).
Trigger Conditions
Load this skill when the user says:
- "Set up my memory"
Workflow
Step 1: Announce the Setup
I'll ask 5 quick questions to learn your preferences. This runs once and saves forever.
Step 2: Run Questions Sequentially (using clarify tool)
Question 1 — Browser Automation:
clarify(
question="What browser automation do you prefer?",
choices=[
"Kimi WebBridge (uses your real Chrome with your logins)",
"Chrome CDP / Playwright (headless, no login persistence)",
"No preference — whatever works"
]
)
Question 2 — Research Style:
clarify(
question="How do you like deep research done?",
choices=[
"Many tabs (10+) organized in named groups",
"One focused search at a time",
"Just give me the answer/summary"
]
)
Question 3 — Repeat Command:
clarify(
question="What word do you use to mean 'do it again the same way'?",
choices=[
"retry",
"rerun",
"again",
"Other (I'll specify)"
]
)
Question 4 — Privacy Concerns:
clarify(
question="Any privacy/data things I should avoid?",
choices=[
"No public pastes (dpaste, GitHub Gists)",
"Local-only tools, no cloud APIs",
"Delete temp files after",
"No strong preferences"
]
)
Question 5 — UI Patterns:
clarify(
question="Any UI patterns you use repeatedly?",
choices=[
"Preview pane for docs/social/localhost",
"Terminal split for logs + editor",
"Dev server on fixed port (e.g., :3000)",
"No strong patterns"
]
)
Step 3: Save to Memory
After all 5 answers collected, call memory tool with operations array:
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.
- 5d ago First seen · 141 lines · 27 tokens per session scan A 8585469c78ae
memory-setup is a skill published in the GitHub repository sloemo01/hermes-skills-bundle (9 stars, last pushed 1mo ago), licensed MIT. It adds 27 tokens to every session and 959 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
distilly
Build and use evidence-grounded local person profiles with Distilly's exact five-tool workflow. Use when a user asks to research, ingest, distill, update, correct, retrieve, or recall a real or fictional person's profile, voice, boundaries, or evidence.
mnemosyne-context
Load this when working on the mnemosyne memory system — its repo, sync server, memory databases, or CI. Covers architecture, the surface/sync data model, dev workflow (tests/ruff/CI matrix), release policy, and known gotchas that are easy to get wrong. Use for any "mnemosyne" dev or devops task, or when a…
hermes-memory-providers
Install and configure Mnemosyne as a Hermes Agent memory provider — local SQLite with vector search, episodic consolidation, and temporal knowledge graphs.
mnemosyne
Persistent cross-session memory via Mnemosyne — store, recall, and consolidate facts, preferences, and context.
mnemosyne-memory-override
Hard rule override that forces Mnemosyne for all durable memory storage. The legacy memory tool is DEPRECATED for user preferences, credentials, and project conventions. Use memory ONLY for ephemeral session state.
hermes-context-optimization
Use this when the user asks about Hermes prompt/context size, “hello” startup cost, compression behavior, memory/profile bloat, tool-schema overhead, skill loading, session-store/search-index storage, or multimodal/visual-context approaches such as Snapcompact.