stat-toolkits-eif AGENTS.md

stat-toolkits-eif AGENTS.md is an instructions file for Codex, OpenCode from maweiruc/stat-toolkits-eif. It costs 1,072 tokens per session, scanned A, original, MIT.

A set of instructions for working on a research toolkit that derives and checks efficient influence functions, mathematical formulas used to study statistical estimators.

In plain words
What is it for?
Use it when deriving, validating, explaining, or implementing efficient influence functions in the stat-toolkits-eif repository.
Why use it?
It gives coding agents a fixed reading order, workflow, and checks for research and LaTeX problems. This reduces the risk of applying a formula under the wrong assumptions.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/maweiruc/stat-toolkits-eif/agents-md
Clone the repo
git clone --depth 1 https://github.com/maweiruc/stat-toolkits-eif

Made for: Codex, OpenCode.

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README.md
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Per session 1,072 This file is loaded in full into every session.
When invoked 1,072 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.01072 $0.01072
Opus 5 $0.00536 $0.00536
Sonnet 5 $0.00214 $0.00214
Haiku 4.5 $0.00107 $0.00107

Measured 4d ago against content hash b7e8be577240, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

stat-toolkits-eif AGENTS.md 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.

AGENTS.md · 104 lines

How it starts

The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent instructions for the EIF toolkit

This repository is a documentation-first toolkit for deriving, validating, and implementing efficient influence functions (EIFs). It is not primarily a Python package yet. Treat the files in agent/, theory/, examples/, and simulations/ as the working system.

First files to read

For any EIF task, read these files first:

  1. agent/eif_agent_task_spec.md
  2. agent/eif_latex_problem_protocol.md
  3. agent/eif_target_triage.md
  4. agent/eif_research_problem_protocol.md
  5. agent/eif_hard_problem_protocol.md
  6. agent/eif_answer_rubric.md
  7. theory/semiparametric_influence_function_guide.md
  8. theory/eif_projection_guide.md
  9. agent/eif_danger_zone.md
  10. examples/eif_examples.md
  11. agent/eif_validation_tests.md
  12. agent/eif_implementation_guide.md

Use examples/eif_formula_registry.yaml only after checking that the observed data, target, model, assumptions, nuisance definitions, and positivity conditions match.

For self-testing or evaluation, use both examples/eif_benchmark_tasks.md and examples/eif_hard_benchmark_tasks.md.

Default workflow

When the user gives a problem, especially in LaTeX:

  1. Normalize the problem statement.
  2. Assign a target triage label.
  3. State the observed-data unit (O).
  4. Separate the scientific target from the observed-data functional.
  5. State identification assumptions.
  6. State the statistical model.
  7. Check pathwise differentiability.
  8. Check whether the model is fully nonparametric or restricted.
  9. Identify nuisance functions and positivity conditions.
  10. Factorize the likelihood and decompose the score.
  11. Derive the IF/EIF component by component.
  12. Verify mean zero and the pathwise derivative identity.
  13. Check special-case reductions.
  14. Provide estimator, standard error, and validation code only when requested or useful.

LaTeX inbox behavior

For LaTeX inbox/problem-folder artifacts, follow agent/eif_problem_artifacts.md.

When the user asks to process a LaTeX problem under problems/latex_inbox/problem_*/problem.tex, check whether notes.md exists in the same folder.

Read the full file on GitHub · 104 lines

Changes

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.

  1. 4d ago First seen · 104 lines · 1,072 tokens per session scan A b7e8be577240

Subscribe to this mod's changes

stat-toolkits-eif AGENTS.md is an instructions file published in the GitHub repository maweiruc/stat-toolkits-eif (12 stars, last pushed 3mo ago), licensed MIT. It adds 1,072 tokens to every session, about $0.0054 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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