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 agentmods add skills/aiming-lab/autoresearchclaw/statistical-problem-formulationnpx skills add aiming-lab/AutoResearchClaw --skill statistical-problem-formulationgit clone --depth 1 https://github.com/aiming-lab/AutoResearchClawWrote 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/aiming-lab/autoresearchclaw/statistical-problem-formulation)<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/statistical-problem-formulation"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/statistical-problem-formulation.svg" alt="Measured on agentmods" 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 | $0.00030 | $0.00609 |
| Opus 5 | $0.00015 | $0.00304 |
| Sonnet 5 | $0.00006 | $0.00122 |
| Haiku 4.5 | $0.00003 | $0.00061 |
Grade A, and why
statistical-problem-formulation 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 4d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Problem Formulation
Overview
Use this skill before any method design, theory, experiment, or report writing. The goal is to transform a broad topic into a precise statistical problem.
Required Formulation Elements
| Element | Questions |
|---|---|
| Observed data | What is observed? What is the sample size? Are samples iid, dependent, clustered, censored, or selected? |
| Data model | What family of distributions or data-generating processes is considered? |
| Target | What parameter, decision, prediction, or risk is the object of study? |
| Assumptions | What must hold for the target to be identifiable or the method to work? |
| Hypotheses | What claims should be supported, refuted, or made inconclusive? |
| Criteria | What metrics define success or failure? |
| Theory target | What property should be derived: bias, variance, consistency, rate, coverage, error bound, robustness, or impossibility? |
Handoff Schema
The problem formulation should be precise enough to support this structured handoff:
topic_id: TXX
title: ""
research_question: ""
observed_data:
notation: ""
sampling: iid | dependent | clustered | time_series | selected | unknown
data_model:
notation: ""
family: ""
target:
name: ""
notation: ""
type: estimand | decision | prediction | risk | descriptive_quantity
truth_source: analytic | simulation | oracle | empirical_reference | not_applicable
assumptions:
structural: []
sampling: []
regularity: []
identifiability: []
claims:
- id: C1
statement: ""
formal_statement: ""
evaluation_criteria:
- name: ""
direction: ""
theory_targets:
- identifiability
- bias
- consistency
blocking_ambiguities: []
Template
# Problem Formulation
## Research Question
...
## Observed Data
Let ...
## Data-Generating Model
Assume ...
## Target / Estimand
Define ...
## Candidate Procedure Class
We consider procedures ...
## Assumptions
1. ...
## Claims / Hypotheses
- ...
## Evaluation Criteria
- ...
## Theoretical Questions
- ...
## Experimental Questions
- ...
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.
- 4d ago First seen · 111 lines · 30 tokens per session scan A 8e2bc671e730
statistical-problem-formulation is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,325 stars, last pushed 16d ago), licensed MIT. It adds 30 tokens to every session and 609 once invoked, about $0.0002 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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