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/quantumbfs/sci-brain/know-me-betternpx skills add QuantumBFS/sci-brain --skill know-me-bettergit clone --depth 1 https://github.com/QuantumBFS/sci-brainWrote 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/quantumbfs/sci-brain/know-me-better)<a href="https://agentmods.dev/skills/quantumbfs/sci-brain/know-me-better"><img src="https://agentmods.dev/badge/skills/quantumbfs/sci-brain/know-me-better.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 | $0.00040 | $0.01681 |
| Opus 5 | $0.00020 | $0.00840 |
| Sonnet 5 | $0.00008 | $0.00336 |
| Haiku 4.5 | $0.00004 | $0.00168 |
Grade A, and why
know-me-better 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 yesterday.
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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Know Me Better
Turn an existing paper collection into a structured knowledge base under <project>/.knowledge/ (or an advisor KB). The output uses the same KB format as the survey and how-to-download-ref skills — project and advisor KBs can coexist cleanly.
Step 1 — Identify the researcher and source. First, ask whose papers to index:
"Whose papers should I index? (Give me a name, or leave blank for your own collection.)"
Then ask which source to use:
"Where are the papers?"
- (a) Zotero library
- (b) A PDF folder (give me the path)
- (c) Google Scholar profile (give me the URL)
Note: the Zotero option is only meaningful for indexing your own collection (it's your local DB). For another researcher, choose (b) or (c).
Step 2 — Index the collection.
Zotero:
-
Locate
zotero.sqlite— check in order:~/Zotero/,~/Library/Application Support/Zotero/,~/snap/zotero-snap/common/Zotero/. If not found, usefind ~ -maxdepth 4 -name "zotero.sqlite"as fallback. If still not found, ask for the path. -
Run the bundled script:
python3 <skill-base-dir>/parse_zotero.py <path-to-zotero.sqlite> <output_dir>
The script handles: copying the DB to avoid locking, pivot queries to avoid cartesian products, author extraction, cite key deduplication, topic classification, and generating structured output.
Important — treat <output_dir> as a scratch directory, not the KB. The script writes legacy-format index files (a topic index and a .bib file) into <output_dir>. Pick a temp path (e.g., /tmp/zotero-export-$$/). Steps 3–6 are the authoritative writes — they read those intermediate files from <output_dir> as input data, then emit .raw/{arxiv,doi}/<id>.json into $KB and append to $KB/references.bib. After Steps 3–6 finish, the contents of <output_dir> can be deleted.
-
Review the output — the script's topic classification uses keyword matching and may need manual adjustment. Check the topic distribution it prints and offer to re-classify if the user's field isn't well covered by the default patterns.
-
For papers missing abstracts or DOIs, find the PDF via the
itemAttachmentstable. PDFs are at<zotero-data-dir>/storage/<key>/<filename>.pdf. Read them to extract the abstract.
PDF folder:
- List all PDFs in the given path.
- Read each PDF — extract title, authors, year, abstract, DOI/URL from the content.
- For bulk keyword search:
pdfgrep -r -i "KEYWORD" <folder>(install via package manager if missing, e.g.,apt install pdfgreporbrew install pdfgrep).
Google Scholar:
Note: Google Scholar actively blocks automated access — automated fetching may hit CAPTCHAs or rate limits. If scraping fails, suggest alternatives: export BibTeX manually from the Scholar profile page (Scholar → select all → export BibTeX), use ORCID or DBLP profiles instead (both have machine-friendly APIs), or switch to the PDF folder method with downloaded papers.
- Fetch the profile page.
- Extract paper titles, years, citation counts.
- For each paper, search for the DOI and abstract via web search.
Processing tips:
- Always use bundled scripts (
parse_zotero.pyfor Zotero). Don't try to do it inline with shell commands — even for small libraries, a script is more reliable and easier to debug. - Topic classification in the script uses keyword matching ordered most-specific-first. The default patterns cover quantum computing, physics, CS, and math. For other fields, modify
TOPIC_PATTERNSin the script or ask the user to provide keywords for their domain.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday Changed · +2 tokens per session ff80de3ce95a
- 5d ago First seen · 126 lines · 38 tokens per session scan A 83eb6df9093e
know-me-better is a skill published in the GitHub repository QuantumBFS/sci-brain (86 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,681 once invoked, about $0.0002 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.
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