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 magnus919/agent-skills --skill actuarial-risk-modelinggit clone --depth 1 https://github.com/magnus919/agent-skillsWrote 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/magnus919/agent-skills/actuarial-risk-modeling)<a href="https://agentmods.dev/skills/magnus919/agent-skills/actuarial-risk-modeling"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/actuarial-risk-modeling/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/magnus919/agent-skills/actuarial-risk-modeling"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/actuarial-risk-modeling.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00101 | $0.02562 |
| Opus 5 | $0.00051 | $0.01281 |
| Sonnet 5 | $0.00020 | $0.00512 |
| Haiku 4.5 | $0.00010 | $0.00256 |
Grade A, and why
actuarial-risk-modeling 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.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Actuarial and Financial Risk Modeling
Overview
Apply statistical modeling to uncertain outcomes where distributional assumptions, exposure, dependence, tail behavior, calibration, and decision consequences matter. The skill is methodology-first: it teaches model selection and evidence, not a particular library or rating formula.
When to Use
Load this skill when the task involves:
- insurance pricing, claims, reserving, solvency, risk classification, or experience rating;
- claim frequency, severity, pure premium, medical expenditure, loss, or event-time outcomes;
- linear, generalized linear, two-part, count, survival, panel, longitudinal, or tail models;
- financial returns, volatility, portfolio loss, risk measures, or scenario output;
- calibration, forecast evaluation, backtesting, model comparison, or assumption diagnosis;
- explaining model results, uncertainty, limitations, or use controls to decision-makers.
When Not to Use
- Use the Decision Entry Points table above for adjacent work. In brief:
data-scientistowns general statistical, causal, experimental, and machine-learning methodology;financial-modelingowns deterministic operating, SaaS, fundraising, and cash-flow models. - Use a named tool skill for operating a forecasting, database, or modeling platform.
- Do not present output as licensed actuarial, investment, legal, accounting, or regulatory advice. Escalate consequential decisions to qualified practitioners and applicable standards.
Decision Entry Points
| Starting situation | First move | Load next |
|---|---|---|
| Policy, claim, or loss data | Define grain, exposure, target, and horizon | references/problem-framing.md |
| Claims development triangle or reserve estimate | Identify accident/development/calendar structure and valuation boundary | references/applications-and-governance.md + references/validation-and-calibration.md |
| Financial returns, volatility, or ordered observations | Define information cutoff and forecast horizon | references/model-families.md + references/validation-and-calibration.md |
| Deterministic SaaS, cash-flow, or fundraising model | Route out of this skill | financial-modeling |
| Generic causal, experimental, or ML methodology | Route out of this skill | data-scientist |
What ships with it
15 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.
- evals/evals.json 6.1 KB
- README.md 2.4 KB
- references/applications-and-governance.md 2.8 KB
- references/diagnostics-and-communication.md 2.6 KB
- references/model-families.md 3.6 KB
- references/problem-framing.md 3.0 KB
- references/source-index.md 4.8 KB
- references/validation-and-calibration.md 3.6 KB
- scripts/risk_preflight.py 4.2 KB runs code
- scripts/temporal_split_audit.py 3.5 KB runs code
- scripts/test_risk_scripts.py 1.3 KB runs code
- templates/model-brief.md 971 B
- templates/model-governance-record.md 664 B
- templates/model-report.md 1.4 KB
- templates/validation-plan.md 1.1 KB
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 · 141 lines · 101 tokens per session scan A f1d4db75016b
actuarial-risk-modeling is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 101 tokens to every session and 2,562 once invoked, about $0.0005 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
meter-chargeback
CocoMeter chargeback and FinOps governance. Usage: $meter chargeback refresh | $meter chargeback invoice | $meter chargeback onboarding.
meter-invoice
Generate CocoMeter chargeback invoices. Usage: $meter invoice.
meter-refresh
Refresh CocoMeter chargeback facts. Usage: $meter refresh.
autonomous-researcher
End-to-end research pipeline: scoping, literature review, hypothesis formation, synthesis, empirical validation, and written output.
research-librarian
Organize sources, claims, and open questions so research stays traceable.
ceo-intel
Global, geopolitical, and cross-domain intelligence brief for the CEO - world events, sovereign-tech shifts, macro and threat signals relevant to 31C, rendered as a dated HTML/PDF brief. Use for world-scale situational awareness, not a single entity. Trigger when the user says "world intel", "geopolitical brief"…