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 Ootto-AI/claude-content-skills --skill ai-braingit clone --depth 1 https://github.com/Ootto-AI/claude-content-skillsWrote 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/ootto-ai/claude-content-skills/ai-brain)<a href="https://agentmods.dev/skills/ootto-ai/claude-content-skills/ai-brain"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/ai-brain/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/ootto-ai/claude-content-skills/ai-brain"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/ai-brain.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00095 | $0.01087 |
| Opus 5.5 | $0.00038 | $0.00435 |
| Sonnet 5.5 | $0.00019 | $0.00217 |
| Haiku 4.5 | $0.00010 | $0.00109 |
Grade A, and why
ai-brain 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Brain — persistent memory for Claude via Obsidian
You maintain a long-term memory for the user in an Obsidian vault — a folder of Markdown notes the
user owns on disk. The vault is the durable store; this skill is how you write to it and read from it.
You reach the vault through the connected Filesystem MCP (read/write files in the vault folder).
Notes live under an AI Brain/ folder; use Obsidian [[wikilinks]] and #tags so memories connect.
One-time setup: in Claude Desktop add the Filesystem connector pointed at your Obsidian vault (Settings → Developer → Edit Config). No browser automation, no login — it's just your local notes.
When the user says "wrap up" / "save this session" / "remember this" — SAVE
-
Review the whole conversation. Pull out only what's worth remembering long-term:
- Decisions made (and the reasoning / alternatives rejected)
- Facts & context about the user, their project, their preferences
- What was built / changed, and where it lives
- Open questions and next steps (TODOs)
- Anything they explicitly said "remember this" Ignore small talk, dead ends, and anything already saved.
-
Write a structured Markdown note in this exact shape (Obsidian-friendly tags + links):
# Session — <YYYY-MM-DD> — <3–6 word title> #ai-brain #<topic> #<topic> Related:: [[<other note>]], [[<project>]] ## Decisions - <decision> — <why> ## Context / facts to remember - <fact> ## Built / changed - <what> (<where>) ## Open / next - [ ] <todo>Keep it tight and skimmable — this is for future retrieval, not prose.
-
Save it to the vault via the Filesystem MCP: write the note to
AI Brain/<YYYY-MM-DD>-<slug>.md. If theAI Brain/folder doesn't exist yet, create it first. Append-only — never overwrite a past session's note. -
Link it in. Add a one-line pointer to a
AI Brain/MOC.mdindex (a map-of-content):- [[<YYYY-MM-DD>-<slug>]] — <one-line hook>so the brain stays browsable in Obsidian's graph.
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 · 83 lines · 95 tokens per session scan A 19a3bd575032
ai-brain is a skill published in the GitHub repository Ootto-AI/claude-content-skills (301 stars, last pushed 10d ago), licensed MIT. It adds 95 tokens to every session and 1,087 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 2026-08-30.
Other skills, from other repositories
run-memory
Shared step of the marketing playbooks (analyze-performance, run-paid-campaigns, research-competitors, email-lifecycle): reads the last run's record, checks whether its recommendations were done and whether they worked, and saves a short record of this run for the next one. Loaded by those playbooks; not for direct…
company-brain
Your team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so an agent can answer on your team's behalf. Team-scope sibling to second-brain — personal notes go there, not here. Modes — capture, compile (wiki pages + INDEX.md), query (trust-weighted, saved to outputs/)…
claude-reflect
Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something for future sessions.
outlier-post-finder
Use when the user wants to find posts, videos, reels, shorts, tweets, or social content that overperformed versus a creator, brand, or competitor baseline. Finds outliers, explains why they worked, extracts hooks and formats, and produces a practical swipe file.
competitor-social-research
Use when the user wants to research competitors' social media strategy, compare brands or creators, find what content is working in a niche, identify content gaps, or produce a practical social strategy brief from public social data.
transcript-intelligence
Use when the user wants to summarize, analyze, or repurpose transcripts from TikTok, Instagram, YouTube, Facebook, X/Twitter, LinkedIn, Rumble, or Reddit video posts. Extracts hooks, claims, quotes, content atoms, themes, and reusable scripts.