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 BingHanOfUESTC/open_agent_team --skill business-plan-reviewgit clone --depth 1 https://github.com/BingHanOfUESTC/open_agent_teamWrote 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/binghanofuestc/open_agent_team/business-plan-review)<a href="https://agentmods.dev/skills/binghanofuestc/open_agent_team/business-plan-review"><img src="https://agentmods.dev/badge/skills/binghanofuestc/open_agent_team/business-plan-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/binghanofuestc/open_agent_team/business-plan-review"><img src="https://agentmods.dev/badge/skills/binghanofuestc/open_agent_team/business-plan-review.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.00051 |
| Opus 5 | $0.00000 | $0.00026 |
| Sonnet 5 | $0.00000 | $0.00010 |
| Haiku 4.5 | $0.00000 | $0.00005 |
Grade A, and why
business-plan-review 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 12d 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.
What it actually says
Business Plan Review
Review:
customer pain clarity
evidence traceability
market realism
business model coherence
financial assumption clarity
GTM feasibility
risk honesty
investor readability
Output pass/revise and unsupported claims.
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.
- 12d ago First seen · 17 lines · 0 tokens per session scan A 17f1cbe71ba7
business-plan-review is a skill published in the GitHub repository BingHanOfUESTC/open_agent_team (106 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 51 tokens. 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
stellar-forge
Eval-driven coding harness for building production Stellar dApps. Routes tasks to 6 specialist agents, verifies output with structured evals, steers on failure, e2e-tests with Stellar Agentic Kit hooks, and knowledge-graphs every project. Use when building any Stellar app — tokens, DeFi, NFTs, payment APIs, or full…
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 →…
vnpy-export
Export a Vibe-Trading backtest strategy to a runnable vnpy CtaTemplate Python class — supports A-share equities, futures, and crypto via BarGenerator + ArrayManager.
factor-research
Factor research framework with IC/IR analysis, quantile backtesting, and factor combination. Suitable for cross-sectional factor evaluation across multiple instruments.
financial-statement
A framework for reading a company's income statement, balance sheet, and cash-flow statement together. It checks whether reported profits are supported by cash and looks for warning signs of misleading accounts.