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/tonydzi/second-brain-starter-kit/asknpx skills add tonydzi/second-brain-starter-kit --skill askgit clone --depth 1 https://github.com/tonydzi/second-brain-starter-kitWrote 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/tonydzi/second-brain-starter-kit/ask)<a href="https://agentmods.dev/skills/tonydzi/second-brain-starter-kit/ask"><img src="https://agentmods.dev/badge/skills/tonydzi/second-brain-starter-kit/ask.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.00853 |
| Opus 5 | $0.00039 | $0.00426 |
| Sonnet 5 | $0.00015 | $0.00171 |
| Haiku 4.5 | $0.00008 | $0.00085 |
Grade A, and why
ask scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to init-project — 75 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ask — query the Second Brain
🧒 When reporting to a non-technical operator: end with a child-simple "In plain words" recap in their language.
Deterministic-first (the operator's token law): RAG retrieves the smallest relevant slice; synthesize ONLY that — never dump the corpus.
Wrong lane? /ask = semantic MEANING over the curated vault. For exact WORDS inside chats → /search; an exact PERSON name → /find; a CHAT by name → /chat (all 0 tokens, deterministic). Use those when the question isn't conceptual.
🖥️ Visual search (a live server — the operator works visually)
For interactive eyes-on search: python "$IMPORTS_ROOT/ask_server.py" (or start_ask.bat) → open http://127.0.0.1:8770. It loads e5 + reranker ONCE, after which each query takes ~3s. A search box + filter chips (my writing only / concepts / insights / leads / people / conversations / notes), cards with the rerank score, type, date and a ⏳ staleness flag. CPU by default (does not fight the GPU fleet, holds 0 VRAM); --gpu when the GPU is free. This is the GUI for the operator; --ask below is for me synthesizing an answer in chat.
Run (CLI — for synthesizing an answer in chat)
python "$IMPORTS_ROOT/brain_ask.py" "<question>"
Scope with filters (cheaper + sharper):
--anton(only the owner's own writing) ·--concepts(distilled "what I think about X") ·--insights--person <name>·--conv(conversations) ·--leads(CRM) Returns top-K chunks (chunked + tagged + edit-aware index); may flag⚠ STALEon volatile facts >90d.
Answer
- Synthesize the returned hits into a DIRECT answer; cite the note titles so the operator can open them.
- If a hit is
⚠ STALE, say so and offer to re-verify (per the epistemic-decay layer). - If retrieval is thin/irrelevant → say so, suggest a sharper query or a filter; do NOT pad with guesses.
- Keep it tight; end with 🧒 recap.
Note
The index is kept fresh by the reindex routine. If results feel stale right after a big import, mention a reindex may be due (brain_embed_update.py) — but don't reindex unprompted.
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.
- 3d ago First seen · 50 lines · 77 tokens per session scan A dd381ee70209
ask is a skill published in the GitHub repository tonydzi/second-brain-starter-kit (5 stars, last pushed 4d ago), licensed MIT. It adds 77 tokens to every session and 853 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to init-project, differing in 75 lines, and is treated as a copy.
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autoresearch
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wiki-retrieve
Build and query a vault-local contextual BM25 retrieval index with optional multilingual Nomic cosine reranking; use for retrieve, hybrid retrieval, BM25, rerank, contextual retrieval, chunk search, vault search, semantic search, find relevant passages, or retrieval diagnostics. Derived caches stay under .vault-meta…
wiki
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defuddle
Plan and, with explicit network consent, use an optional external Defuddle cleaner to extract article-like HTTPS pages as Markdown. Use for defuddle, clean this URL, strip page clutter, readable Markdown from a web page, or preparing a web source for later wiki ingestion.