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/surveynpx skills add QuantumBFS/sci-brain --skill surveygit clone --depth 1 https://github.com/QuantumBFS/sci-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 | $0.00057 | $0.02995 |
| Opus 5 | $0.00028 | $0.01497 |
| Sonnet 5 | $0.00011 | $0.00599 |
| Haiku 4.5 | $0.00006 | $0.00299 |
Grade A, and why
survey 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Choose the mode
- Explore and build the knowledge base: run Topic Survey below.
- Write up an existing survey: when the user asks to "write up the survey", "write a review", assess a technology/field, or otherwise already has a populated project KB, skip Topic Survey and start at Survey Report.
- Explore, fetch, and write: complete Topic Survey, invoke
download-ref --from-bib, then continue directly to Survey Report in this same skill when the user wants the write-up.
Topic Survey
Before starting this mode, check which MCP servers are available (arxiv, paper-search, Semantic Scholar, Sci-Hub, etc.). Present the detected servers to the user and let them choose which ones to use for this session (multi-select in chat). If none are configured, warn the user that the survey will rely on web search only.
If the user already provided a research topic or question, skip the clarification step.
Step 1 — Clarify. Ask one question to narrow the research topic. Give 2-4 choice options.
Step 2 — Pick strategies & search. Present the strategy menu to the user as a multi-select question. Recommend 3-4 strategies based on the topic context, but let the user choose. Then run one search worker per selected strategy in parallel when available, or sequentially otherwise. Each worker uses broad web search only at this stage — fast and exploratory.
Strategy menu:
| # | Strategy | When to use |
|---|---|---|
| 1 | Landscape mapping | First iteration default — broad field overview |
| 2 | Adjacent subfield | Deep-dive into a neighboring cluster identified in prior iteration |
| 3 | Cross-vocabulary | Abstract away jargon, search other fields for the same structural problem |
| 4 | Cross-method | Same problem, different computational or experimental approaches |
| 5 | Historical lineage | Who tried before, what failed, what changed since |
| 6 | Negative results | Search for papers showing what does not work |
| 7 | Benchmarks and datasets | What evaluation infrastructure exists |
What ships with it
2 files 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.
- 2d ago First seen · 189 lines · 57 tokens per session scan A d81ec917f9c9
survey is a skill published in the GitHub repository QuantumBFS/sci-brain (81 stars, last pushed 6d ago), licensed MIT. It adds 57 tokens to every session and 2,995 once invoked, about $0.0003 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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