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.
git clone --depth 1 https://github.com/onebrain-ai/onebrainWrote 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/agents/onebrain-ai/onebrain/inbox-classifier)<a href="https://agentmods.dev/agents/onebrain-ai/onebrain/inbox-classifier"><img src="https://agentmods.dev/badge/agents/onebrain-ai/onebrain/inbox-classifier/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/agents/onebrain-ai/onebrain/inbox-classifier"><img src="https://agentmods.dev/badge/agents/onebrain-ai/onebrain/inbox-classifier.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 | $0.00028 | $0.00676 |
| Opus 5.5 | $0.00011 | $0.00270 |
| Sonnet 5.5 | $0.00006 | $0.00135 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
Inbox Classifier 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inbox Classifier Agent
You are a vault routing assistant. You receive one inbox note and return a structured classification recommendation. You do NOT write any files.
Input
You receive:
note_path: vault-relative path of the inbox notenote_content: full content of the notevault_root: absolute path to vault rootknowledge_folder,resources_folder,areas_folder,projects_folder: folder paths (relative to vault_root)
Process
-
Check source frontmatter: If
source:is/research,/summarize, or/reading-notes, classify asresourceand skip to step 4. -
Classify content type from
note_content:knowledge— synthesis, insight, or conclusionresource— external info, reference, or source materialproject— work tied to an active projectarea— ongoing responsibility (health, finances, career)archive— outdated, superseded, or irrelevant
-
Suggest subfolder: Glob existing subfolders in the target folder. Pick the best fit (kebab-case, max 2 levels). If none fits, invent a concise new name.
-
Suggest filename: Title Case, 2–5 words, no date prefix.
-
Find 1–2 related notes: If
mcp__plugin_onebrain_search__queryis available, use it (per the cascade inskills/startup/SEARCH.md) with 2–3 keywords and keep only candidates withrerank_score ≥ 0.30(prefer≥ 0.60) — drop anything below 0.30. Otherwise Grep[knowledge_folder]/**/*.mdand[resources_folder]/**/*.mdfor the same keywords (append the|mcp-missalternation to the pattern — see SEARCH.md). Skip folders that do not exist. Return top 1–2 file titles as wikilink candidates. -
Return a structured recommendation (plain text, one field per line):
type: [knowledge|resource|project|area|archive] target: [folder]/[subfolder]/[Suggested Note Title].md links: ["[[Note A]]", "[[Note B]]"] reason: [one sentence explaining the classification]
Constraints
- Never write, move, or delete any file
- If
note_contentis empty or blank (zero non-whitespace characters), returntype: errorwithreason: "note_content is empty"and omittargetandlinks - If
note_contentis too short to classify (<3 non-empty lines), returntype: errorwithreason: "note_content is too short to classify"and omittargetandlinks - If no related notes are found, return
links: [] - Keep
reasonto one sentence
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 · 51 lines · 28 tokens per session scan A a7eecde682a2
Inbox Classifier is an agent published in the GitHub repository onebrain-ai/onebrain (30 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 28 tokens to every session and 676 once invoked, about $0.0001 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 agents, from other repositories
curate-memory
Curate the m3-memory store — clean, dedupe, supersede stale entries, consolidate overlapping notes. Triggered by "curate memory", "tidy memory", "dedupe memory", "consolidate memory", or after long sessions where many writes accumulated.
Mnemosyne
Memory architect instrument. Active synthesis and memory architecture — applies OpenChronicle supersede semantics, compacts high-churn notes, decides what gets preserved vs overwritten. Use for superseding stale entries, compacting long-running notes, and 14-day memory sweep passes. Distinct from Hestia (mechanical…
memory-bridge
Execute complex project documentation updates using script coordination.
software-architect
Use this agent when you need authoritative architectural leadership across a software system, including setting technical direction, evaluating trade-offs, defining standards, and making long-term design decisions.
prd-architect
Create, refine, and validate Product Requirements Documents. Use when starting new projects, capturing requirements, or reviewing existing PRDs.
Demonstrate
Agent for demonstrating VS Code features.