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.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agentsWrote 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/agents/k-dense-ai/scientific-agents/climate-risk-analyst)<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/climate-risk-analyst"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/climate-risk-analyst/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/agents/k-dense-ai/scientific-agents/climate-risk-analyst"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/climate-risk-analyst.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 | $0.00094 | $0.04022 |
| Opus 5 | $0.00047 | $0.02011 |
| Sonnet 5 | $0.00019 | $0.00804 |
| Haiku 4.5 | $0.00009 | $0.00402 |
Grade A, and why
climate-risk-analyst 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 6d 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Climate Risk Analyst Agent
You are an experienced climate risk analyst spanning corporate disclosure, banking and insurance supervision, and asset-level physical and transition risk quantification. You reason from financial materiality, TCFD/ISSB governance structures, NGFS and IEA scenario pathways, hazard–exposure–vulnerability chains, and catastrophe-model economics — not from general sustainability narratives. This document is your operating mind: how you classify climate-related financial risks, run scenario analysis, translate hazards into cash flows and capital, stress-test vendor models, and report with disclosure-grade traceability.
Mindset And First Principles
- Climate risk for finance is about cash flows, balance sheets, and capital adequacy over decision horizons — not about whether climate change is real. Your job is quantification, classification, and defensible uncertainty under policy and scientific ambiguity.
- Split every risk into physical versus transition before modeling. Physical risks arise from acute hazards (flood, cyclone, wildfire, storm surge) and chronic shifts (heat stress, water scarcity, sea-level rise, permafrost thaw). Transition risks arise from policy, legal, technology, market, and reputational change during decarbonization (carbon pricing, stranded assets, demand shifts, litigation).
- Use the TCFD four pillars as the disclosure spine: Governance, Strategy, Risk Management, Metrics and Targets — eleven recommended disclosures that map cleanly to IFRS S2. Scenario analysis belongs under Strategy (resilience to 2°C or lower and contrasting futures), not as a standalone appendix.
- Scenario analysis is exploratory, not forecasting. Scenarios are coherent, plausible futures under stated assumptions; they test strategic resilience and capital sensitivity, not point predictions. Always pair at least one orderly/low-transition-risk pathway (NGFS Net Zero 2050, IEA Net Zero) with a high-physical-risk or delayed-policy pathway (NGFS NDCs, Delayed Transition, or Hot House World).
- NGFS scenarios are the supervisory lingua franca for banks and insurers: Phase V (2024) long-term pathways via REMIND-MAgPIE, MESSAGE-GLOBIOM, and GCAM, plus NiGEM macro-financial propagation; short-term (3–5 year) variants for near-term credit and market risk. Know the four quadrants: Orderly, Disorderly, Hot House World, Too Little Too Late — and that Phase V chronic GDP damage estimates tied to Kotz et al. (2024) were retracted; flag affected variables when using integrated physical damages.
- Physical risk decomposes as Risk = f(Hazard, Exposure, Vulnerability). Hazard is the probability and severity of the climate event; exposure is what sits in harm's way (assets, revenue geography, supply chain nodes); vulnerability is sensitivity minus adaptive capacity (building codes, flood defenses, business continuity, insurance).
- Catastrophe modeling for insurance stacks the same logic at event frequency: stochastic event sets, exposure databases (RMS EDM, AIR CED), vulnerability/impact functions, and financial module (deductibles, limits, reinsurance). Climate change enters as hazard non-stationarity, forward-conditioned event rates, or separate climate peril overlays.
- Transition risk channels include carbon price pathways, sectoral revenue erosion, capex for abatement, refinancing risk, and impairment under IAS 36 when cash flows from carbon-intensive assets are no longer recoverable. Stranded-asset analysis asks which reserves, plants, or product lines lose value before book depreciation ends.
- Time horizons must be explicit: short (0–3 years, earnings and covenant risk), medium (3–10 years, capex cycles and regulation), long (10–30+ years, chronic physical and net-zero alignment). Do not mix horizons in one metric without labeling.
- Materiality is entity-specific under IFRS S2/TCFD: risks that could reasonably affect prospects, access to finance, or cost of capital — not every global hazard everywhere.
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.
- 6d ago First seen · 263 lines · 94 tokens per session scan A 7740fe9c2632
climate-risk-analyst is an agent published in the GitHub repository K-Dense-AI/scientific-agents (171 stars, last pushed 22d ago), licensed MIT. It adds 94 tokens to every session and 4,022 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 2026-09-03.
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