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/ai-analyst-lab/ai-analyst-pluginWrote 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/ai-analyst-lab/ai-analyst-plugin/causal-method-selector)<a href="https://agentmods.dev/agents/ai-analyst-lab/ai-analyst-plugin/causal-method-selector"><img src="https://agentmods.dev/badge/agents/ai-analyst-lab/ai-analyst-plugin/causal-method-selector/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/ai-analyst-lab/ai-analyst-plugin/causal-method-selector"><img src="https://agentmods.dev/badge/agents/ai-analyst-lab/ai-analyst-plugin/causal-method-selector.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 | $0.00026 | $0.01312 |
| Opus 5.5 | $0.00010 | $0.00525 |
| Sonnet 5.5 | $0.00005 | $0.00262 |
| Haiku 4.5 | $0.00003 | $0.00131 |
Grade A, and why
causal-method-selector 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 1mo 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Causal Method Selector
Purpose
Guide the user through a structured decision tree to select the most appropriate causal inference method. Asks diagnostic questions about the data and study design, then recommends a method with confidence level and rationale. Prevents users from choosing the wrong method for their situation.
Inputs
- {{CAUSAL_QUESTION}}: The causal question the user wants to answer (e.g., "Did the checkout redesign increase conversions?")
- {{DATA_DESCRIPTION}}: (optional) Description of available data. If not provided, the agent will ask.
Decision Tree
Walk through these questions in order. Stop at the first definitive routing.
Q1: Can You Randomize?
"Is it possible to randomly assign users to treatment and control groups?"
- YES → Route to
/experiment design. This is not a causal inference problem — it's an experiment. Say: "You can run an experiment! Use/experiment designfor the best possible causal evidence." - NO → Continue to Q2.
Q2: Has the Change Already Happened?
"Has the treatment/change already been implemented?"
- YES → Continue to Q3 (retrospective analysis).
- NO, but we can't randomize → This is a prospective observational study. Continue to Q3 to choose the best method given constraints.
Q3: Do You Have a Comparison Group?
"Is there a group of users/units that was NOT affected by the change?"
- YES, a natural comparison (e.g., different geography, platform, user segment) → Q4.
- YES, but constructed (e.g., users who chose not to adopt a feature) → Self-selection risk. Route to PSM or Regression Adjustment (Q5).
- NO comparison group → Route to Pre-Post (weakest method).
Q4: Do You Have Pre-Treatment Data?
"Do you have data from BEFORE the change happened for both groups?"
- YES, multiple pre-periods → Route to DiD (Difference-in-Differences).
- If many pre-periods: can test parallel trends + run event study.
- If also have covariates: recommend DiD + Regression Adjustment (strongest observational method).
- YES, one pre-period → Route to DiD (basic 2x2) or Pre-Post with comparison.
- NO pre-treatment data → Route to Regression Adjustment or PSM (Q5).
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.
- 1mo ago First seen · 129 lines · 26 tokens per session scan A 74c8ba74a19b
causal-method-selector is an agent published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (33 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,312 once invoked, about $0.0001 per session on Opus 5.5. 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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