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 Vertiso/memory-vscode --skill wrap-upgit clone --depth 1 https://github.com/Vertiso/memory-vscodeWrote 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/vertiso/memory-vscode/wrap-up)<a href="https://agentmods.dev/skills/vertiso/memory-vscode/wrap-up"><img src="https://agentmods.dev/badge/skills/vertiso/memory-vscode/wrap-up/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/vertiso/memory-vscode/wrap-up"><img src="https://agentmods.dev/badge/skills/vertiso/memory-vscode/wrap-up.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00073 | $0.03626 |
| Opus 5.5 | $0.00029 | $0.01450 |
| Sonnet 5.5 | $0.00015 | $0.00725 |
| Haiku 4.5 | $0.00007 | $0.00363 |
Grade A, and why
wrap-up 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 18d 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 — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wrap-up
Close out a finished session: close the completed work in Vertiso Memory — complete the actions that shipped, archive the intents and projects whose full scope is done — journal the stable state, and ensure every deferred follow-on is tracked — as a vmem ACTION (a concrete piece of work) or, for a measurable outcome to achieve or maintain, an intent.
Announce: "Using the wrap-up skill to close out this session."
CLI fallback: when you call vmem through the vmem CLI instead of the MCP tools, run it as
vmem --agent vscode <command>, so your writes are attributed to this client.
Consent boundary
Invoking this skill at the user's request, or affirmatively accepting an agent's offer to run it, authorizes one capture. Invocation authorizes the wrap-up memory and its checkpoint-backed transcript gathering, including selected attributed verbatim excerpts sent to Vertiso Memory. Once invoked, do not ask a second permission question for that capture. It does not authorize creating, completing, or archiving actions, intents, or projects; those additional mutations retain the explicit confirmation step below.
Do not ask whether to write the wrap-up memory; the user's invocation already authorized it. When follow-on or closure candidates exist, ask only whether to create or close those named actions/intents/projects, and state that the wrap-up memory will be written regardless. If both lists are empty, proceed without another question.
One invocation does not grant standing authorization for later wrap-ups. Omit and do not persist passwords, API keys, authentication tokens, payment information, illegal or illicit materials, or sensitive third-party information the user is not authorized to store.
Secret stripping
Before composing the memory, inspect every selected source, including the conversation, transcript, work artifacts, tool output, title, tags, and metadata, for secrets. Treat passwords, API keys, bearer or refresh tokens, OAuth codes, session cookies, private keys, recovery codes, and credentialed connection strings as secrets.
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.
- 18d ago Changed · +3 lines be1dfbff1293
- 28d ago Changed fd3d63847e20
- 1mo ago First seen · 265 lines · 73 tokens per session scan A 7ae20a50800d
wrap-up is a skill published in the GitHub repository Vertiso/memory-vscode (0 stars, last pushed 2d ago), licensed MIT. It adds 73 tokens to every session and 3,626 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 2026-08-31.
Other skills, from other repositories
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Cortex
LifeOS memory, prior-session recall, and learning capture. USE WHEN capture this learning, document this learning, what did we learn recently, recent learnings, cortex, knowledge, knowledge base, search knowledge, what do we know about, archive, harvest, knowledge status, develop note, add to knowledge, add a people…
Profile
View, edit, or clear the LifeOS User Model (PRINCIPALIDENTITY.md) — persistent identity context loaded at session startup. Three intents: View, Edit (identity, goals, constraints, preferences), Clear (reset). Read by Algorithm OBSERVE and skills for personalization. USE WHEN profile, show profile, view profile, who am…
wiki-dashboard
Create dynamic, queryable Obsidian dashboard views using Bases or Dataview. Use for auto-updating tables, indexes, or structured views of vault content; not for ordinary question answering.
relevance-coarse-filter
Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitoronly, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.
session-compact
AI DevKit · Compact a historical AI coding session with Jev when context is long, work is handed off or resumed, or durable continuation facts and memory candidates need extraction.