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 rules/andyzengmath/soliton/risk-factorsgit clone --depth 1 https://github.com/andyzengmath/solitonWrote 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/rules/andyzengmath/soliton/risk-factors)<a href="https://agentmods.dev/rules/andyzengmath/soliton/risk-factors"><img src="https://agentmods.dev/badge/rules/andyzengmath/soliton/risk-factors.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 | $0.00000 | $0.00299 |
| Opus 5 | $0.00000 | $0.00150 |
| Sonnet 5 | $0.00000 | $0.00060 |
| Haiku 4.5 | $0.00000 | $0.00030 |
Grade A, and why
risk-factors 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 4d 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
Risk Scoring Factors
Invariant: Factor weights must sum to 100%. Current total: 25 + 20 + 20 + 15 + 10 + 10 = 100%.
Factors
| Factor | Weight | Scoring |
|---|---|---|
| Blast radius | 25% | min(100, importerCount * 10) |
| Change complexity | 20% | controlFlowLines / totalAddedLines * 100 |
| Sensitive paths | 20% | 100 if any match, 0 otherwise |
| File size/scope | 15% | <50 lines=10, 50-200=30, 200-500=60, 500-1000=80, >1000=100 |
| AI-authored signals | 10% | Weighted sum of detected signals |
| Test coverage gap | 10% | (gapFiles / totalProdFiles) * 100 |
Risk Levels
| Level | Score | Agents Dispatched |
|---|---|---|
| LOW | 0-30 | correctness, consistency |
| MEDIUM | 31-60 | + security, test-quality |
| HIGH | 61-80 | + hallucination, cross-file-impact |
| CRITICAL | 81-100 | + historical-context |
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.
- 4d ago First seen · 30 lines · 0 tokens per session scan A 025d8bb70ef6
risk-factors is a cursor rule published in the GitHub repository andyzengmath/soliton (1 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 299 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 2026-08-31.
Other cursor rules, from other repositories
openehr-context
When working with openEHR (archetypes, templates, AQL, ADL, CKM, compositions), load guides from the MCP server before answering or editing.
cursor
Always-on my-adhd-caveman. Off with "stop adhd-caveman" or "normal mode".
delta-briefing
Make a recurring brief report what changed since the last edition instead of restating everything. Use when a weekly or monthly report keeps repeating itself, when setting up a scheduled monitor or digest, or when asked to make a recurring update delta-aware. Produces a changes-first brief plus the state record the…
voice-agent-design
Design a voice AI agent for phone or in-app conversations — call flows, interruption handling, escalation to humans, and the metrics that catch a bad voice experience. Use when asked to design a voice agent, automate a phone line, spec an IVR replacement, or review why callers hate an existing voice bot. Produces a…
ai-content-audit
Audit a content library, docs site, or blog for AI-generated filler that's eroding trust and search performance — and triage what to fix, rewrite, or delete. Use when asked to find slop in a content library, audit AI-written content quality, explain why content engagement or rankings dropped after scaling with AI, or…
investor-update
Write a structured monthly or quarterly investor update. Use when asked to write an investor update, investor newsletter, board update, or startup progress report for investors. Produces a clear, credible update with highlights, metrics, challenges, and asks — in the format investors actually want to read.