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 davistroy/claude-marketplace --skill brain-entrygit clone --depth 1 https://github.com/davistroy/claude-marketplaceWrote 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/davistroy/claude-marketplace/brain-entry)<a href="https://agentmods.dev/skills/davistroy/claude-marketplace/brain-entry"><img src="https://agentmods.dev/badge/skills/davistroy/claude-marketplace/brain-entry/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/davistroy/claude-marketplace/brain-entry"><img src="https://agentmods.dev/badge/skills/davistroy/claude-marketplace/brain-entry.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 | $0.00065 | $0.01231 |
| Opus 5.5 | $0.00026 | $0.00492 |
| Sonnet 5.5 | $0.00013 | $0.00246 |
| Haiku 4.5 | $0.00006 | $0.00123 |
Grade A, and why
brain-entry scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
allowed-tools: Bash(curl:*), Bash(echo:*), Bash(tail:*), Bash(sed:*), Bash(python3:*) How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brain Entry
Create a capture in Open Brain from any instruction. The argument tells you what content to generate and send.
Examples
/brain-entry summarize everything we have done so far in this session and send it to my open-brain
/brain-entry log a decision: we chose Cloudflare Email Routing for email ingestion because it adds zero infrastructure
/brain-entry capture this idea: add a weekly email digest that summarizes all captures from the past 7 days
/brain-entry record that I had a productive call with Butch about the CFA merger timeline
Implementation
Step 1: Generate the content
Based on the user's instruction in the arguments:
- If the instruction says "summarize this session" or similar — review the full conversation context and produce a clear, structured summary of what was accomplished, decisions made, and outcomes.
- If the instruction describes a decision, idea, observation, task, or other capture — write it up clearly and concisely.
- If the instruction references specific context from the conversation — include the relevant details.
Write the content in first person (Troy's voice) since this is his personal knowledge base. Be substantive and specific — include names, dates, technical details, and outcomes. No filler.
Step 2: Classify the capture
Determine the best capture_type from: decision, idea, observation, task, win, blocker, question, reflection.
Determine the best brain_view from: career, personal, technical, work-internal, client.
Use your judgment based on the content. When in doubt, use observation and personal.
Step 3: POST to the captures API
Use the Bash tool with curl and a heredoc to POST to the captures API. Always use curl, not Python urllib — Cloudflare blocks Python's default user-agent with 403.
Use a heredoc for the JSON body to avoid shell escaping issues with quotes and newlines in the content. Bound the call with --max-time/--connect-timeout so a hung connection cannot block indefinitely, and capture the HTTP status alongside the body so success is verified before confirming anything to the user:
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 · 111 lines · 65 tokens per session scan A 85d3232b943b
brain-entry is a skill published in the GitHub repository davistroy/claude-marketplace (5 stars, last pushed 18d ago), licensed MIT. It adds 65 tokens to every session and 1,231 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
Other skills, from other repositories
Migrate
Intakes external content (notes, CLAUDE.md, Cursor rules, Obsidian/Notion/Apple Notes exports), classifies chunks against the LifeOS taxonomy, and commits with provenance after confidence-tiered approval. USE WHEN /migrate, migrate content, import from other LifeOS, bring in old notes, import Cursor rules, import…
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.