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Strategy: Factor-Level Design · yogsoth-ai | DecimalAI

scienceby yogsoth-ai

Strategy: Factor-Level Design

yogsoth-ai/experiment-execution-factor-level-design

Source yogsoth-ai/de-anthropocentric-research-engine ↗License Apache-2.0

Design factorial experiments to test how specific factors affect outcomes

SkillScore91Maintained

Automatic

Research pack

Last verified 8/22/2026

What it changes

Passed 20 of 22 test cases with the skill, and 17 of 22 without it.

Without skill77%

With skill91%

Checks it passed that the model alone missed

Recommends Taguchi Orthogonal Arrays.
Specifies power analysis for sample size estimation.
Calculates total baseline matrix size as 8 runs for a 2-level 3-factor setup.

and 1 more in the full report

3 fixed17 passed either way2 still failing0 broke

Pass-rate lift: +14 pp
Tokens: +17%
Agent turns: 0%
Response time: -18%

Model gemini-3.6-flashMeasured 8/22/2026v120 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/yogsoth-ai-experiment-execution-factor-level-design/SKILL.md https://app.decimal.ai/s/yogsoth-ai-experiment-execution-factor-level-design/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add yogsoth-ai-experiment-execution-factor-level-design, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/yogsoth-ai-experiment-execution-factor-level-design/ and pins it in .decimal/skills.lock

decimalai skills add yogsoth-ai-experiment-execution-factor-level-design

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/yogsoth-ai-experiment-execution-factor-level-design/SKILL.md https://app.decimal.ai/s/yogsoth-ai-experiment-execution-factor-level-design/SKILL.md

Raw file: /s/yogsoth-ai-experiment-execution-factor-level-design/SKILL.md ↗ · machine-readable summary at /s/yogsoth-ai-experiment-execution-factor-level-design.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 3

Prompt

I am preparing an AI fine-tuning experiment where I need to evaluate learning rate, batch size, dropout rate, weight decay, and sequence length simultaneously. Because GPU time is limited, we cannot test every single combination. Can you guide me through identifying the variables, picking a suitable experiment design method given our budget, outlining the level specifications, and detailing how to estimate sample sizes and metrics?

What was checked, without and with the skill
Checked	Without skill	With skill
Recommends fractional factorial design for 5 factors under budget constraints.	Missed	Passed
Specifies power analysis for sample size estimation.	Missed	Passed

case-01 · gemini-3.6-flash · 8/22/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md57 linessingle file

Strategy: Factor-Level Design

Question: Which factors to test at what levels in combination?

Methodology

Full Factorial (Fisher): All combinations of all factor levels. Gold standard but exponential cost.
Fractional Factorial: Systematic subset using defining relations. Sacrifices high-order interactions.
Plackett-Burman: Screening design for many factors. Identifies main effects only.
Response Surface Methodology (RSM): Central Composite / Box-Behnken for optimization after screening.
Taguchi Orthogonal Arrays: Robust design minimizing variance from noise factors.

Execution Flow

factor-identification → Identify all independent, dependent, and control variables
level-specification → Determine discrete levels for each factor (2-5 levels typical)
budget-constrained-design (tactic) → Select design type given budget constraints
design-matrix-construction → Build the actual design matrix
metric-specification → Define primary/secondary metrics and significance thresholds
sample-size-estimation → Power analysis to determine runs per cell

Budget Gate

Design Type	Factors	Runs (k factors, 2 levels)	When to Use
Full Factorial	2-4	2^k	Budget allows, need all interactions
Fractional (Res V)	4-6	2^(k-1)	Need 2-factor interactions
Fractional (Res III)	5-8	2^(k-p)	Screening, main effects only
Plackett-Burman	8-15	k+1 (nearest multiple of 4)	Many factors, screening phase
Taguchi L9/L18	4-8	9 or 18	Robust design with noise factors
RSM (CCD)	2-5	2^k + 2k + center	Optimization after screening

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use

Track record

Benchmark

20 / 22

test cases passed, vs 17 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

yogsoth-ai / de-anthropocentric-research-engine

skills/experiment-execution-factor-level-design/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 8/3/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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See it in action

What's inside

Strategy: Factor-Level Design

Methodology

Execution Flow

Budget Gate

Available Tactics

Track record

Benchmark

Adoption

SkillSafety

Source

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