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 ezesubu/VERA --skill memorygit clone --depth 1 https://github.com/ezesubu/VERAWrote 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/ezesubu/vera/memory)<a href="https://agentmods.dev/skills/ezesubu/vera/memory"><img src="https://agentmods.dev/badge/skills/ezesubu/vera/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/ezesubu/vera/memory"><img src="https://agentmods.dev/badge/skills/ezesubu/vera/memory.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 | $0.00000 | $0.00220 |
| Opus 5.5 | $0.00000 | $0.00088 |
| Sonnet 5.5 | $0.00000 | $0.00044 |
| Haiku 4.5 | $0.00000 | $0.00022 |
Grade A, and why
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.
What it actually says
Persistent memory
You have persistent memory that survives across conversations. Use it deliberately.
Recall before you act
At the start of any task that might relate to prior context — project conventions,
the user's preferences, past decisions, naming schemes, file paths, known gotchas —
call recall with a short query to check what you already know before assuming.
Remember what's durable
When you learn something worth keeping for next time, call remember with a concise,
factual statement and a few tags. Good things to remember:
- project conventions and structure
- the user's stated preferences and decisions
- naming schemes, important paths, environment quirks
- gotchas you hit and how you resolved them
Keep each memory short and factual — one fact per memory. Do NOT store transient chatter, secrets, or anything that's only true within the current task.
Keep memory honest
If a stored memory turns out to be wrong or outdated, use forget with its id to
remove it. Use list_memories to review everything currently stored.
What ships with it
2 files 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 · 24 lines · 0 tokens per session scan A d6cc92b2250f
memory is a skill published in the GitHub repository ezesubu/VERA (21 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 220 tokens. 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
llm-wiki
Use when building or maintaining a self-contained personal knowledge base (an LLM wiki) in plain markdown. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki health, auditing article claims against their sources, critiquing a wiki source's reasoning, superseding stale knowledge, 'add to…
authoring-claude-md
Creating and maintaining CLAUDE.md project memory files and .claude/rules/ rule files that provide non-obvious codebase context. Use when (1) creating a new CLAUDE.md for a project, (2) adding architectural patterns or design decisions to existing CLAUDE.md, (3) capturing project-specific conventions that aren't…
graphify
Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community…
facts
Manage .facts files — atomic, validatable truth statements about a project. Install, check, list, add, edit, remove, and lint facts via the CLI. ALWAYS read this skill when the user mentions facts in any capacity.
mem0-vercel-ai-sdk
Mem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider). TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider", "createMem0", "retrieveMemories", "addMemories", "getMemories", "searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory", or is using generateText/streamText with mem0. Also…
pause
Pause Mem0 memory capture on this machine. Use when the user wants to stop memories being recorded, for example for private work or experiments.