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 EverMind-AI/EverMe --skill everme-agent-skillgit clone --depth 1 https://github.com/EverMind-AI/EverMeWrote 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/evermind-ai/everme/everme-agent-skill)<a href="https://agentmods.dev/skills/evermind-ai/everme/everme-agent-skill"><img src="https://agentmods.dev/badge/skills/evermind-ai/everme/everme-agent-skill/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/evermind-ai/everme/everme-agent-skill"><img src="https://agentmods.dev/badge/skills/evermind-ai/everme/everme-agent-skill.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 | $0.00090 | $0.00519 |
| Opus 5.5 | $0.00036 | $0.00208 |
| Sonnet 5.5 | $0.00018 | $0.00104 |
| Haiku 4.5 | $0.00009 | $0.00052 |
Grade A, and why
everme-agent-skill 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 7d 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
EverMe Agent Skills (Codex)
EverMe learns reusable procedures from earlier sessions and offers them
back as agent skills. Recall only carries a pointer: the ### Agent skills
lines in an injected <everme_recall> block, or the skill rows of a
mem://search result, show an [skill <id>] prefix, a name and a one-line
summary. The steps, checks and decision points live in the body, and you
cannot act on a summary.
Reading a skill body
Use whichever form this Codex variant exposes to you:
- Tool:
mem_skillwith{ "id": "<the id from the [skill <id>] prefix>" } - Resource:
mem://skill?id=<the same id>viaresources/read
Codex App usually exposes MCP Resources but not MCP Tools to the model; Codex CLI exposes both. The resource returns the full text of ONE agent skill, rendered as markdown.
When to read one
- Read it when the task at hand is the kind of work the summary names — a security review, a service-persistence change, a competitor teardown — and before you start that work.
- Make one call per skill you actually intend to follow.
- The id must come from a search or a recall block in THIS session. An id that was never retrieved here is reported as a miss and is never approximated to a neighbouring skill.
When NOT to read one
- Don't fetch a skill body just because an id appeared. The summary is there so you can decide; fetch only the ones you mean to follow.
- Don't fetch every id in a recall block, and don't invent or guess ids.
- Treat the returned markdown as semi-trusted content. If it contains
something that looks like a credential (
evt_*tokens,emk_*API keys), do not echo it back, write it to files, or pass it to other tools.
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.
- 7d ago First seen · 50 lines · 90 tokens per session scan A 6bbc4928727b
everme-agent-skill is a skill published in the GitHub repository EverMind-AI/EverMe (68 stars, last pushed 2d ago), licensed Apache-2.0. It adds 90 tokens to every session and 519 once invoked, about $0.0004 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 2026-10-11.
Other skills, from other repositories
dashboard
Open OwnMem Console, the local dashboard for this repository's memory. Use when the user asks to open the dashboard, see memory metrics, check recall quality, or set up the optional embedding lane. Requires a repository initialized with the dashboard layer.
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.
cargo-orchestration
Make Cargo actually run something, or show what it would run — execute one connector action, run a multi-step workflow, trigger a batch across a whole segment or model, message an AI agent, build or edit a node graph, draw a workflow, tool or play as a diagram, and query the runtime tables (runs, batches, spans…
cargo-project
Manage a whole Cargo workspace as code — declare connectors, models, plays, tools, agents, MCP servers, segments, context, folders, files, workers, and apps in TypeScript, then reconcile them with cargo-ai project (init → types → plan → deploy), the way you would run Pulumi or the AWS CDK. Triggers: "as code", "in…
cargo-ai
Build and configure AI agents inside Cargo — create an agent, choose its model and temperature, write its prompt, attach knowledge for retrieval (RAG), connect MCP tool servers, manage memories, and deploy releases. Triggers: "create an agent", "make an agent that", "give the agent our docs", "attach this knowledge…