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 panaversity/agentfactory-business-plugins --skill ifrs9-scenariosgit clone --depth 1 https://github.com/panaversity/agentfactory-business-pluginsWrote 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/panaversity/agentfactory-business-plugins/ifrs9-scenarios)<a href="https://agentmods.dev/skills/panaversity/agentfactory-business-plugins/ifrs9-scenarios"><img src="https://agentmods.dev/badge/skills/panaversity/agentfactory-business-plugins/ifrs9-scenarios/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/panaversity/agentfactory-business-plugins/ifrs9-scenarios"><img src="https://agentmods.dev/badge/skills/panaversity/agentfactory-business-plugins/ifrs9-scenarios.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 | $0.00092 | $0.02344 |
| Opus 5 | $0.00046 | $0.01172 |
| Sonnet 5 | $0.00018 | $0.00469 |
| Haiku 4.5 | $0.00009 | $0.00234 |
Grade A, and why
ifrs9-scenarios 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 11d 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IFRS 9 SCENARIO FRAMEWORK REQUIREMENTS
IFRS 9.5.5.17 requires: reasonable and supportable information about future economic conditions, including forward-looking information. This is not optional. Single-scenario ECL is non-compliant with IFRS 9.
SCENARIO STRUCTURE — MINIMUM AND BEST PRACTICE
Minimum (IFRS 9): base + 1 upside + 1 adverse Best practice: 4–5 scenarios with explicit probability weights
| Scenario | Typical Weight | Key Feature |
|---|---|---|
| Upside | 10–20% | Above-trend growth, falling unemployment |
| Base | 35–50% | Central forecast, moderate conditions |
| Adverse | 25–35% | Mild recession, rising unemployment |
| Severe | 10–20% | Deep recession, sharply falling asset prices |
Weights must: sum to 1.0; reflect management's genuine probability assessment; be documented and approved by the IFRS 9 Governance Committee. Equal weighting (25% each) is RARELY defensible and will be challenged by auditors.
KEY MACROECONOMIC VARIABLES BY ASSET CLASS
Retail mortgages: House Price Index (HPI), unemployment rate, base rate Consumer loans: Unemployment rate, disposable income index, base rate SME loans: GDP growth, SME default index, unemployment rate Corporate loans: GDP growth, corporate default rates, sector-specific indices Commercial Real Estate: CRE capital value index, vacancy rates, GDP growth
CREDIT CYCLE ADJUSTMENT (CCA) — CONVERTING TTC TO PIT PD
PIT PD = TTC PD x CCA CCA is estimated from a satellite model. Typical satellite model form: ln(CCA) = a + b1(GDP_growth) + b2(Unemployment) + b3(HPI_growth) + e
Example CCA values: Severe recession: CCA = 1.8–2.5 (PDs 80–150% above long-run average) Adverse: CCA = 1.2–1.5 Base: CCA ~ 1.0 (by definition — TTC PD already reflects long-run average) Upside: CCA = 0.7–0.9 (PDs below long-run average)
SATELLITE MODEL DETAIL
Model Structure
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.
- 11d ago First seen · 193 lines · 92 tokens per session scan A f02ca3a05bff
ifrs9-scenarios is a skill published in the GitHub repository panaversity/agentfactory-business-plugins (29 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 92 tokens to every session and 2,344 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.
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