msgvault is an offline archive for email, chats, meetings, and related message history, with local search and analytics over the collected data. It is used to preserve and query communication records from services such as Gmail, Google Calendar, Slack, Discord, Microsoft Teams, and IMAP without relying on ongoing mailbox access.
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 kenn-io/msgvault --skill claude-codegit clone --depth 1 https://github.com/kenn-io/msgvaultWrote 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/kenn-io/msgvault/claude-code)<a href="https://agentmods.dev/skills/kenn-io/msgvault/claude-code"><img src="https://agentmods.dev/badge/skills/kenn-io/msgvault/claude-code/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/kenn-io/msgvault/claude-code"><img src="https://agentmods.dev/badge/skills/kenn-io/msgvault/claude-code.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 | $0.00084 | $0.01261 |
| Opus 5.5 | $0.00034 | $0.00504 |
| Sonnet 5.5 | $0.00017 | $0.00252 |
| Haiku 4.5 | $0.00008 | $0.00126 |
Grade A, and why
msgvault-query 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
msgvault-query
Run SQL against the msgvault archive via msgvault query. The analytics cache is DuckDB over Parquet — queries run in milliseconds. No DuckDB binary or Parquet path knowledge required.
The analytics cache is built automatically when stale or missing.
Quick Start
# Top 10 senders, table format
msgvault query --format table "SELECT from_email, message_count FROM v_senders ORDER BY message_count DESC LIMIT 10"
# Messages from a domain in 2024, CSV output
msgvault query --format csv "SELECT subject, sent_at, from_email FROM v_messages WHERE from_domain = 'example.com' AND year = 2024 ORDER BY sent_at DESC"
# Label distribution as JSON
msgvault query "SELECT name, message_count, total_size FROM v_labels ORDER BY message_count DESC"
Available Views
See references/views.md for full column schemas.
Base views (direct Parquet data)
| View | Description |
|---|---|
messages |
Raw message metadata partitioned by year |
participants |
Email addresses with domain and display name |
message_recipients |
Message-to-participant links (from/to/cc/bcc) |
labels |
Gmail label names |
message_labels |
Message-to-label links |
attachments |
Attachment metadata (filename, size) per message |
conversations |
Thread grouping |
sources |
Synced accounts |
Convenience views (pre-joined aggregates)
| View | Description |
|---|---|
v_messages |
Messages with resolved sender (from_email, from_name, from_domain) and labels as JSON array |
v_senders |
Per-sender aggregates: message_count, total_size, attachment stats, first/last message |
v_domains |
Per-domain aggregates: message_count, total_size, sender_count |
v_labels |
Per-label aggregates: message_count, total_size |
v_threads |
Per-conversation aggregates: message_count, date range, participant_emails as JSON array |
Output Formats
msgvault query "..." # JSON (default): {"columns":[...], "rows":[...], "row_count":N}
msgvault query --format csv "..." # CSV with header row
msgvault query --format table "..." # Aligned text table with row count
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.
- 1mo ago First seen · 157 lines · 84 tokens per session scan A 776782d62f28
msgvault-query is a skill published in the GitHub repository kenn-io/msgvault (2,163 stars, last pushed today), licensed MIT. It adds 84 tokens to every session and 1,261 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
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…