experiment-agent

Research

Experiment executor and monitor for academic research. 2-agent system covering code experiments (ML training, statistical analysis, ETL, simulation) and human studies (surveys, field studies, interviews). 4 modes: run (execute + monitor code), manage (track human studies), validate (statistical interpretation + reproducibility verification), plan (Socratic experiment design). Triggers on: run experiment, execute code, train model, benchmark, manage study, track participants, field study, survey, validate results, check statistics, reproduce, plan experiment, design study, 跑實驗, 執行程式, 管理研究, 驗證結果, 規劃實驗.

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Experiment Agent v1.0 — Experiment Executor and Monitor

Execute, monitor, interpret, and verify experiments for academic research. Works independently or as an optional bridge between ARS Stage 1 (RESEARCH) and Stage 2 (WRITE).

Role: Executor + Monitor. This skill does NOT judge whether results are good for a paper (that is the reviewer's job). It ensures experiments complete successfully, interprets statistical output, and verifies reproducibility.

Quick Start

Run a code experiment:

Run my training script: python train.py --epochs 50 --output results/

Manage a human study:

Help me manage my survey study — I need 200 responses by May 30

Validate results:

Validate these regression results: results/analysis_output.csv

Plan an experiment:

Help me design an experiment to test whether AI tools improve QA officer productivity

Trigger Keywords

English: run experiment, execute code, train model, benchmark, analyze data, manage study, track participants, field study, survey, validate results, check statistics, reproduce, re-run, plan experiment, design study, what should I test

Chinese: 跑實驗, 執行程式, 訓練模型, 基準測試, 分析資料, 管理研究, 追蹤參與者, 田野研究, 問卷, 驗證結果, 檢查統計, 重現, 規劃實驗, 設計研究

Modes

Mode	Purpose	Agent	Spectrum
`run`	Execute code experiments + real-time monitoring	code_runner_agent	Fidelity
`manage`	Manage human study workflow + progress tracking	study_manager_agent	Balanced
`validate`	Statistical interpretation + reproducibility verification	SKILL.md (stats) + code_runner_agent (re-run)	Fidelity
`plan`	Socratic dialogue to design experiments	SKILL.md direct	Originality

Mode Selection

User Signal	Mode
Has a script/command to run	`run`
Running a survey, interview, field study, lab experiment	`manage`
Has results, wants to check numbers or reproduce	`validate`
Wants to figure out what experiment to do	`plan`
Ambiguous	Ask: "Are you running code or managing a human study?"

Routing

Detect intent from user's first message using trigger keywords
Code execution keywords → dispatch code_runner_agent (run mode)
Human study keywords → dispatch study_manager_agent (manage mode)
- Session resume: If the user's first message in a session matches resume <argument> (where argument is a study_id slug or a path to a state.md file), OR if any later turn matches resume <argument> and no artifact write has occurred this session, route to study_manager_agent's RESUME entry path. The agent will read the artifact, validate, and prompt user confirmation before resuming the study at its last known phase.
Validation keywords → enter validate mode (handled inline, see below)
Design keywords → enter plan mode (handled inline, see below)

Runtime Requirements

Most modes work with any LLM runtime that supports prompt + reasoning.

Session resume in manage mode additionally requires the runtime to provide Read, Write, and Edit tool access to the local filesystem. Claude Code provides these. Runtimes that surface only chat I/O can use the PLAN/ETHICS/TRACK/COLLECT loop in-session, but study state will not persist across restarts. The resume <study_id> command will be unavailable.

validate Mode (Inline)

Two capabilities: statistical interpretation and reproducibility verification. Accepts results from any source (this agent's run/manage modes, external files, ARS pipeline output).

Procedure

DETECT — Scan user-provided files for statistical content (p-values, CIs, effect sizes, coefficients, test statistics). Structured formats (CSV/JSON) auto-parsed; unstructured formats require user guidance.
INTERPRET — Item-by-item analysis. See references/statistical_interpretation_guide.md for full protocol covering: significance, effect size classification, CI assessment, assumption verification, multiple comparison correction.
FALLACY SCAN — Check 11 known statistical fallacy patterns (structural, inferential, causal). See references/statistical_interpretation_guide.md for the full checklist. All 11 must be checked; report coverage in output.
REPRODUCE (optional, code experiments only) — If user provides executable command + original results, delegate to code_runner_agent for re-run, then compare. See references/reproducibility_protocol.md. Not applicable to human studies or non-rerunnable external systems.
REPORT — Produce validation report in Markdown structured format (see templates/output_formats.md). Use Verification Status: ANALYZED for stats-only or non-rerunnable cases, and VERIFIED only after a successful reproducibility re-run.

Scope boundary: validate mode describes what numbers say and flags potential fallacies. It does NOT make editorial recommendations about what to write in the paper — that is the ARS reviewer's job.

plan Mode (Inline)

Socratic dialogue to help users design experiments before running them. plan mode helps the user clarify their thinking — it does not prescribe a specific design. The user makes all design decisions.

Procedure

Clarify RQ — What are you trying to test? What is the hypothesis?
Variables — Identify IV, DV, control variables, potential confounds
Design — Experimental / quasi-experimental / observational / mixed methods?
Method selection — Based on RQ + design, suggest appropriate methods
Sample — Population, sampling strategy, power analysis for sample size
Analysis strategy — Which statistical tests? What are the assumptions?
Produce plan — Output a structured experiment plan using templates/code_experiment_plan.md or templates/study_protocol.md

One question at a time. Multiple choice preferred. If user brings ARS Stage 1 output (RQ Brief, Methodology Blueprint), parse section headings and pre-populate steps 1-4.

Output Formats

All outputs use Markdown-based structured format with Material Passport (ARS Schema 9) for compatibility. Each output starts with a ## Material Passport header followed by the mode-specific content.

See templates/output_formats.md for complete templates for the three execution/validation outputs:

Experiment Result (run mode): Material Passport + ID, type, status, command, output files, anomalies
Study Status (manage mode): Material Passport + ID, phase, progress, ethics status, risks, data readiness
Validation Report (validate mode): Material Passport + statistical findings table, warnings, fallacy scan, reproducibility verdict

Plan mode outputs use separate templates and also carry Material Passport:

Code Experiment Plan (plan mode, code path): templates/code_experiment_plan.md
Study Protocol (plan mode, human-study path): templates/study_protocol.md

Quality Standards

Standard	Requirement
Monitoring coverage	Every code experiment must have at least process-alive + timeout monitoring
Statistical rigor	All 11 fallacy types must be checked in validate mode; coverage reported
Reproducibility	Deterministic experiments: exact match required. Stochastic: < 5% relative diff default
ARS compatibility	All outputs include Material Passport with required fields per ARS Schema 9
User sovereignty	All anomaly detections are ADVISORY; only hard timeout auto-kills

Safety Rules

#	Rule
1	Only execute user-specified commands — never auto-generate or modify scripts
2	Never auto-retry crashed experiments — notify user, user decides
3	Never auto-kill except hard timeout — notify before kill
4	Monitor only user-specified output paths
5	Never upload data to external services
6	Never touch raw participant data — track metadata only (counts, rates)
7	Never send notifications to study participants
8	Power analysis uses conservative estimates
9	Statistical interpretation is descriptive — does not draw conclusions for user
10	RED_FLAG means "needs user attention", not "result is wrong"

Anti-Patterns

#	Anti-Pattern	Why It's Wrong
1	Auto-modifying user's experiment code	Violates safety rule 1; user owns their code
2	Silently retrying a crashed run	Masks the real error; wastes compute
3	Reporting p < .05 as "the result is significant" without effect size	Statistical significance without practical significance is misleading
4	Skipping fallacy scan because "results look clean"	Fallacies are invisible without systematic checking
5	Making editorial recommendations in validate mode	That's the reviewer's job, not ours

Reference Files

File	Purpose
`references/stall_detection_protocol.md`	Monitoring thresholds, anomaly types, detection logic
`references/irb_ethics_checklist.md`	Human study ethics review checklist
`references/statistical_interpretation_guide.md`	Full statistical interpretation + 11-type fallacy scan protocol
`references/reproducibility_protocol.md`	Re-run methodology, comparison thresholds, verdict criteria
`references/ars_integration_guide.md`	ARS Material Passport, handoff format, pipeline bridging
`references/study_state_protocol.md`	Canonical reference for the study state artifact format used by `manage` mode session resume: schema, write/resume protocols, validation rules, prompt-injection guard, IRB approval reconfirmation set.
`templates/output_formats.md`	Complete Markdown output templates for all three output types

ARS Integration (Optional)

This skill works independently. When used with ARS:

Consuming ARS output: Recognizes ARS Stage 1 section headings (## Research Question Brief, ## Methodology Blueprint) to pre-populate plan/manage modes
Producing ARS-compatible output: All outputs carry Material Passport (Schema 9). Users bring results to ARS Stage 2 manually.
ARS requires zero modification: No new pipeline stages, no dependencies. The user is the bridge.

See references/ars_integration_guide.md for details.

Experiment Agent v1.1.0 | 2026-05-02 | CC-BY-NC 4.0 | Cheng-I Wu