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 inite-ai/inite-brain-service --skill brain-recallgit clone --depth 1 https://github.com/inite-ai/inite-brain-serviceWrote 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/inite-ai/inite-brain-service/brain-recall)<a href="https://agentmods.dev/skills/inite-ai/inite-brain-service/brain-recall"><img src="https://agentmods.dev/badge/skills/inite-ai/inite-brain-service/brain-recall/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/inite-ai/inite-brain-service/brain-recall"><img src="https://agentmods.dev/badge/skills/inite-ai/inite-brain-service/brain-recall.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 | $0.00081 | $0.01925 |
| Opus 5 | $0.00041 | $0.00962 |
| Sonnet 5 | $0.00016 | $0.00385 |
| Haiku 4.5 | $0.00008 | $0.00193 |
Grade A, and why
brain-recall 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 6d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 6d ago Changed · +4 lines 3903da4866e9
- 10d ago First seen · 141 lines · 81 tokens per session scan A 431ec34f9af4
brain-recall is a skill published in the GitHub repository inite-ai/inite-brain-service (36 stars, last pushed today), licensed AGPL-3.0. It adds 81 tokens to every session and 1,925 once invoked, about $0.0004 per session on Opus 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
lemmalog
Externalize working memory and logical state into the lemmalog Datalog engine (MCP). Use for ANY multi-step task where state should outlive one context window or span agents: long investigations, debugging sessions, audits, multi-agent searches, systematic explorations, planning with many interdependent constraints…
ori-memory
Persistent agent memory with learning retrieval. Knowledge graph on markdown files — capture insights, decisions, research, and learnings during work, then retrieve them weeks or months later. Use when knowledge is too valuable to lose but too much to inject into every prompt.
agent-messaging
Send, poll, wait for, and acknowledge durable addressed agent-to-agent messages in Aionforge Memory (messagesend, messagepoll, messagewait, messageack) and subscribe to room resources. Use to hand a brief to another agent, page a teammate, wait for a reply, coordinate a multi-agent workflow, or drain an inbox …
memory-bootstrap
One-time setup that lays a foundational Aionforge Memory substrate for a fresh project — resolve identity, seed conventions and architecture decisions as captures, stand up a work-item backlog skeleton, and verify recall. Use when a project's memory is empty or new, or when the user asks to set up, bootstrap…
memory-review
Memory hygiene audit -- finds stale facts, contradictions, low-confidence entries, and consolidation candidates in Pensyve memory. Use periodically to maintain memory quality.
session-memory
End-of-session memory capture -- classifies session signals using a tiered taxonomy and stores confirmed items via Pensyve. Use when ending a work session or when the user wants to capture what was learned.