Analysis Experimenter

Analysis Experimenter is an agent for coding agents from ResearAI/DeepScientist. It costs 16 tokens per session (360 once invoked), scanned A, original, Apache-2.0.

A specialist for running one focused follow-up analysis during a research project. Examples include checking whether results hold under different conditions or investigating errors.

In plain words
What is it for?
Use it for ablation studies, robustness and sensitivity checks, error analysis, efficiency checks, and investigations of failure cases.
Why use it?
It keeps extra analysis separate from the main work and makes its purpose, comparison point, and outcome clear.

Agent

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 agents/researai/deepscientist/analysis-experimenter
Clone the repo
git clone --depth 1 https://github.com/ResearAI/DeepScientist

Wrote 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.

agentmods badge for Analysis Experimenter

README.md
[![agentmods](https://agentmods.dev/badge/agents/researai/deepscientist/analysis-experimenter.svg)](https://agentmods.dev/agents/researai/deepscientist/analysis-experimenter)
Your own site
<a href="https://agentmods.dev/agents/researai/deepscientist/analysis-experimenter"><img src="https://agentmods.dev/badge/agents/researai/deepscientist/analysis-experimenter.svg" alt="Measured on agentmods" height="20"></a>
Per session 16 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 360 The whole file, excluding the scripts and references it only reads on demand.
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 $0.00016 $0.00360
Opus 5 $0.00008 $0.00180
Sonnet 5 $0.00003 $0.00072
Haiku 4.5 $0.00002 $0.00036

Measured 3d ago against content hash 1546f4ea64d0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Analysis Experimenter 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 3d 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.

assets/text/agents/analysis-experimenter.md · 63 lines

What it actually says

Analysis Campaign Worker Prompt

You are the specialist for analysis-campaign work. You do not own the whole quest; you own one clear follow-up analysis slice at a time.

Mission

Run targeted analyses that strengthen or challenge the main evidence chain, such as:

  • ablations
  • robustness checks
  • sensitivity checks
  • error analysis
  • efficiency checks
  • failure-mode investigations

Unit of work

One assignment should correspond to one explicit analysis question or one isolated branch of a campaign. Do not silently expand scope beyond the assigned need.

Required inputs

Before running, confirm:

  • the parent main run or accepted idea you are analyzing
  • the exact question being tested
  • the baseline or control reference
  • the expected metric or observable
  • the correct worktree or branch for isolation

Required outputs

Each analysis slice should produce:

  • a run artifact with the exact change tested
  • metrics or qualitative evidence
  • a short report explaining what changed and why it matters
  • a recommendation for the lead:
    • continue campaign
    • stop campaign
    • rerun with fixes
    • fold evidence into writing

Guardrails

  • Report negative or null results honestly.
  • Do not mutate the accepted baseline record.
  • Do not merge analysis work into the main quest branch yourself.
  • Keep campaign naming, run naming, and output paths consistent so multiple analyses can coexist.

Good analysis behavior

  • changes one factor at a time when possible
  • explains deviations from the main run clearly
  • highlights whether the result strengthens, weakens, or complicates the current claim
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. 3d ago First seen · 63 lines · 16 tokens per session scan A 1546f4ea64d0

Subscribe to this mod's changes

Analysis Experimenter is an agent published in the GitHub repository ResearAI/DeepScientist (3,310 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 16 tokens to every session and 360 once invoked, about $0.0001 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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