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 agentmods add skills/itechmeat/open-second-brain/embeddings-setupnpx skills add itechmeat/open-second-brain --skill embeddings-setupgit clone --depth 1 https://github.com/itechmeat/open-second-brainWhat 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 | $0.00105 | $0.01368 |
| Opus 5 | $0.00053 | $0.00684 |
| Sonnet 5 | $0.00021 | $0.00274 |
| Haiku 4.5 | $0.00011 | $0.00137 |
Grade A, and why
embeddings-setup 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 2d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Embeddings setup
Open Second Brain ships with two search paths: keyword-only (always
on, no credentials) and semantic via embedded vectors (opt-in). This
SKILL walks the activation flow for the semantic path. The flow is
proactive — when the user mentions semantic search or o2b search check surfaces missing pieces, take the user through this list
rather than waiting for explicit instruction.
Step 1 — Always start with o2b search check
o2b search check
The output names every missing piece and ends with a
recommendations: block listing the exact commands to fix each.
Read both before suggesting next steps. The recommendations field
is also present in the --json output for headless callers.
Branch on what the report shows:
embedding_key: MISSING→ go to step 2.vec_extension: unavailableon macOS → go to step 3.vec_extension: unavailableon Linux → go to step 4.- Everything OK but
semantic_enabled: false(no embeddings yet) → go to step 5.
Step 2 — Provider and API key
Ask the user which provider they want. The default is
text-embedding-3-small from OpenAI (about $0.02 per 1M tokens,
which covers tens of thousands of vault pages). Any OpenAI-compatible
endpoint works — Groq, Together, a local LM Studio server, etc.
Required env vars (write to ~/.hermes/.env or the configured env
file, never to a tracked file):
OPEN_SECOND_BRAIN_EMBEDDING_PROVIDER=openai-compat
OPEN_SECOND_BRAIN_EMBEDDING_MODEL=text-embedding-3-small
OPEN_SECOND_BRAIN_EMBEDDING_KEY=<placeholder; user pastes the key>
# Optional — only when not using OpenAI:
# OPEN_SECOND_BRAIN_EMBEDDING_BASE_URL=https://api.together.xyz/v1
Never invent or echo the key. Write a placeholder, then ask the
user to paste their key in place of it. Recheck with o2b search check after the user confirms.
Step 3 — macOS: install Homebrew SQLite
Apple ships /usr/lib/libsqlite3.dylib with
SQLITE_OMIT_LOAD_EXTENSION, so the optional sqlite-vec extension
cannot load against the system SQLite. Homebrew's sqlite formula
is built with extension loading enabled.
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.
- 2d ago First seen · 154 lines · 0 tokens per session scan A 7efc472d7680
embeddings-setup is a skill published in the GitHub repository itechmeat/open-second-brain (385 stars, last pushed 5d ago), licensed MIT. It adds 105 tokens to every session and 1,368 once invoked, about $0.0005 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…