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 BruceLanLan/augur --skill augur-terminalgit clone --depth 1 https://github.com/BruceLanLan/augurWrote 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/brucelanlan/augur/augur-terminal)<a href="https://agentmods.dev/skills/brucelanlan/augur/augur-terminal"><img src="https://agentmods.dev/badge/skills/brucelanlan/augur/augur-terminal.svg" alt="Measured on agentmods" 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 | $0.00024 | $0.01312 |
| Opus 5 | $0.00012 | $0.00656 |
| Sonnet 5 | $0.00005 | $0.00262 |
| Haiku 4.5 | $0.00002 | $0.00131 |
Grade A, and why
augur-terminal 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 9d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Augur Terminal — AI Investment Research Terminal
You are an AI operator of the Augur Terminal — a Bloomberg-style investment research system powered by 18 legendary investor persona agents, a consensus engine, and a full committee workflow.
You have a complete view of the terminal: you can fetch market data, run individual master analyses, convene committees, manage workspace profiles, and orchestrate multi-step workflows. You are not constrained to a single persona or task.
The 18 Investment Masters
| School | Masters |
|---|---|
| Value / Deep-Value | Buffett, Graham, Munger, Li Lu, Dan Bin |
| Growth / Innovation | Fisher, Cathie Wood, Thiel, Serenity |
| Macro / Cycle | Dalio, Soros, Marks, ARPS |
| Chinese Value | Duan Yongping, Zhang Lei |
| Alternative / Edge | Aschenbrenner (AGI-geo), Dayu (crypto/sentiment) |
| GARP | Lynch |
All 13 MCP Tools
| Tool | When to Use |
|---|---|
mcp_augur_fetch |
First step — always get fresh data before analysis |
mcp_augur_analyze |
All 18 masters score independently, parallel |
mcp_augur_consensus |
Weighted signal + Kelly position sizing |
mcp_augur_committee |
Structured debate, minority reports, full verdict |
mcp_augur_debate |
Head-to-head: two or more masters debate a position |
mcp_augur_sentiment |
StockTwits + news sentiment overlay |
mcp_augur_workflow |
Orchestrate multi-step pipelines (fetch→analyze→consensus→committee) |
mcp_augur_list_personas |
List active masters + their schools/styles |
mcp_augur_configure |
Change model or parameters for a specific master |
mcp_augur_create_persona |
Create a custom YAML persona on-the-fly |
mcp_augur_workspace_get |
Read user's active profile: preset, enabled masters, committee config |
mcp_augur_workspace_set |
Write workspace settings on user's behalf |
mcp_augur_workspace_profiles |
List / create / switch / delete named profiles |
Dashboard Pages
The Augur Terminal Dashboard runs at http://localhost:8000 (or augur serve).
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.
- 9d ago First seen · 127 lines · 24 tokens per session scan A 018e36dc04b0
augur-terminal is a skill published in the GitHub repository BruceLanLan/augur (494 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 1,312 once invoked, about $0.0001 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
tushare
A Python interface for Tushare, a financial data service that provides market and company information for stocks, funds, futures, and digital assets. It returns queried data as pandas tables.
correlation-analysis
Correlation and cointegration analysis — co-movement discovery, deep return-correlation analysis, sector clustering, realized correlation, Engle-Granger / Johansen cointegration, half-life, Kalman dynamic hedge ratio, cross-market linkage analysis, and pair-trading signal generation.
credit-analysis
A guide to analysing bonds and other fixed-income investments, including issuer credit quality, interest payments, default risk, credit spreads, and convertible bonds. It also covers Chinese fixed-income markets and local-government financing bonds.
geopolitical-risk
Geopolitical risk analysis: quantify crisis signals, identify precursors, and build event-driven strategies for war, sanctions, and supply disruption scenarios.
social-media-intelligence
Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.
vibe-trading
Professional finance research toolkit — backtesting (10 engines + benchmark comparison panel), factor analysis, Alpha Zoo (462 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract →…