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 skoowoo/vaultr-notes --skill vaultr-memorygit clone --depth 1 https://github.com/skoowoo/vaultr-notesWrote 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/skoowoo/vaultr-notes/vaultr-memory)<a href="https://agentmods.dev/skills/skoowoo/vaultr-notes/vaultr-memory"><img src="https://agentmods.dev/badge/skills/skoowoo/vaultr-notes/vaultr-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/skoowoo/vaultr-notes/vaultr-memory"><img src="https://agentmods.dev/badge/skills/skoowoo/vaultr-notes/vaultr-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 | $0.00087 | $0.01832 |
| Opus 5.5 | $0.00035 | $0.00733 |
| Sonnet 5.5 | $0.00017 | $0.00366 |
| Haiku 4.5 | $0.00009 | $0.00183 |
Grade A, and why
vaultr-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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vaultr Memory Extract
Extracts personal memories from short notes (/_shorts) and the knowledge base (/_knowledge) into six structured memory files under /_memory/. On first run (no memory files exist yet), scans the last 90 days for a rich initial snapshot. On subsequent runs, scans only the last 2 days. Memories not reinforced over time gradually fade and are eventually removed.
Run all steps to completion without stopping or asking for confirmation. Only speak at the final summary step.
Inputs
- Self-introduction — a brief description the user provides about themselves (name, role, projects, relationships, etc.). Used throughout extraction to identify personal content and disambiguate knowledge units. Ask for this if not provided.
- Extra scan paths — additional vault path prefixes to scan beyond the two defaults (optional).
- Memory directory — where memory files live (default:
/_memory/).
Step 1 — Determine run mode
Check whether any memory file already exists:
vaultr read /_memory/_identity.md
- First run (file not found): set
scan_window = 90days. - Incremental run (file exists): set
scan_window = 2days.
Record today's date as today.
Parse the self-introduction into an author profile to use as a lens throughout extraction:
- Name and known aliases/IDs
- Projects or products the author owns or runs (these may appear as vault directories or knowledge unit titles)
- Roles (creator, host, founder, etc.)
- People the author mentions as part of their personal life
This profile is critical for the knowledge base step: if a source_notes path falls under a directory that belongs to the author's own project (e.g. their own podcast, their own product), treat it as a personal source, not an external one.
Step 2 — Collect content to process
Run both queries in parallel:
vaultr short list --latest <scan_window> --limit 100
vaultr knowledge list --kind knowledge --latest <scan_window> --limit 50
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 · 183 lines · 87 tokens per session scan A 421da4a24a1e
vaultr-memory is a skill published in the GitHub repository skoowoo/vaultr-notes (89 stars, last pushed 27d ago), licensed Apache-2.0. It adds 87 tokens to every session and 1,832 once invoked, about $0.0003 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.
Other skills, from other repositories
maintaining-agent-context
You MUST use this when creating, auditing, optimizing, or maintaining repository agent instructions - AGENTS.md, CLAUDE.md, .claude/rules/, and instruction-related SKILL.md content - covering context architecture, always-loaded context cost, stale, duplicated, or conflicting instructions, and keeping agent memory…
review-agent-memory
Reviews agent memory and state-store configuration for correctness. Use for "review agent memory", "check state store config" or "audit agent persistence".
skill-authoring
Create, revise, or remove reusable procedural and interactive skills; use memorystore for facts and preferences.
copilot-spaces
Use Copilot Spaces to provide project-specific context to conversations. Use this skill when users mention a "Copilot space", want to load context from a shared knowledge base, discover available spaces, or ask questions grounded in curated project documentation, code, and instructions.
signal-friction-scan
Scan recent agent sessions for repeated friction, corrections, approvals, workflow patterns, and packaging candidates. Use when asked to find signals, friction, recurring manual workflows, missing skills, prompt improvements, or automation opportunities from recent Codex/Pi work.
caveman-compress
Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md. Trigger: /caveman-compress FILEPATH or…