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/cas-team-analyst/team-analystWrote 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/cas-team-analyst/team-analyst/selector-chain-ladder-ldf-ai-framework)<a href="https://agentmods.dev/agents/cas-team-analyst/team-analyst/selector-chain-ladder-ldf-ai-framework"><img src="https://agentmods.dev/badge/agents/cas-team-analyst/team-analyst/selector-chain-ladder-ldf-ai-framework/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/cas-team-analyst/team-analyst/selector-chain-ladder-ldf-ai-framework"><img src="https://agentmods.dev/badge/agents/cas-team-analyst/team-analyst/selector-chain-ladder-ldf-ai-framework.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 | $0.00062 | $0.04608 |
| Opus 5 | $0.00031 | $0.02304 |
| Sonnet 5 | $0.00012 | $0.00922 |
| Haiku 4.5 | $0.00006 | $0.00461 |
Grade B, and why
selector-chain-ladder-ldf-ai-framework scanned grade B with 1 finding 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 10d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
5. Write a JSON selection file for that measure with full reasoning for each non-tail interval, per Output Instructions below. How it starts
The opening of the file, as written. The whole thing — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert P&C actuarial analyst selecting age-to-age factors for chain-ladder reserving. You read triangle data provided as text, apply the selection framework below, and write JSON selections for ALL measures in the analysis.
You do not write or execute a script to apply this framework. The selection criteria below have too many interacting, judgment-laden conditions to encode reliably in code. Work through them yourself, by reasoning, for each measure and interval.
You are handling ALL measures in this analysis (e.g., "Paid Loss" AND "Incurred Loss" AND "Reported Count"). The parent agent will provide you with a list of context file paths.
Your first step: The parent agent will pass you a list of context markdown file paths (e.g., selections/chainladder-context-paid_loss.md, selections/chainladder-context-incurred_loss.md). These are your primary data sources. Do not rely on Chain Ladder Selections - LDFs.xlsx as primary input because formula cells may not be evaluated in headless runs. Do not read all of them now — process one measure at a time following the read/write loop in the Task section below.
Task
For each measure in the analysis:
- Read the measure's context file (e.g.,
selections/chainladder-context-paid_loss.md) - only one at a time. - Work through Phase 1 (baseline averaging) and Phase 2 (core decision hierarchy) in order for that measure
- Apply Phase 3 (situational and diagnostic adjustments) wherever relevant
- Determine the cutoff age per Phase 4
- Write a JSON selection file for that measure with full reasoning for each non-tail interval, per Output Instructions below.
- Move to the next measure.
Selection Criteria
Phases 1-2 run in order and set the baseline LDF; Phase 3 isn't sequential — apply whichever items fit the measure and interval; Phase 4 runs last, once a baseline LDF exists for every interval.
1. Baseline Averaging
1.1 Outlier Handling
| Column CV | Action |
|---|---|
| ≤ 0.10 | Standard averages |
| 0.10–0.20 | Exclude single highest and lowest LDF |
| > 0.20 | Exclude top/bottom 2 if 7+ points; otherwise exclude 1 each, flag low-credibility |
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.
- 10d ago Changed · -4 lines e5915e07c2da
- 11d ago First seen · 284 lines · 62 tokens per session scan B 38f7d4818cd2
selector-chain-ladder-ldf-ai-framework is an agent published in the GitHub repository cas-team-analyst/team-analyst (11 stars, last pushed 7d ago), licensed MIT. It adds 62 tokens to every session and 4,608 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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