Benchmark author variants
Skill LoogacyStudio/skills/.github/skills/benchmark-author-variants
This repository stores reusable agent skills, repo level benchmark workflow material, and plugin bundles for coding agents.
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Use when a benchmark item or judged run needs a reusable variant pack from the repository's canonical metamorphic and adversarial families, with explicit invariants, drift budget, safe-failure rules, and execution-ready transformed input packages.
SKILL.md
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Benchmark Author Variants
Use this skill when a benchmark needs metamorphic or adversarial expansion that can be rerun and judged consistently.
It covers variant-pack authoring. It does not execute variants, score them, or decide whether observed drift is acceptable.
For shared benchmark framework rules — capability registry, corpus discovery, corpus adapter contract, lifecycle boundaries, and run-record finalization conventions — defer to benchmark-core.
Purpose
Use it to:
- author metamorphic variants from the shared
M1–M5families - author adversarial variants from the shared
A1–A6families - keep hard invariants, soft invariants, drift budget, and safe-failure rules explicit
- package transformed inputs so they are ready for execution and later judging
- hand off the executed variant runs to
benchmark-judge-runinstead of judging them inline
Use this skill when
Typical requests:
- creating the first variant pack for a benchmark item
- expanding a route-sensitive item with invariance checks
- expanding a fragile item with adversarial stress cases
- translating a judged weakness into a bounded variant bundle
Trigger examples
- "Help me create metamorphic variants for B006"
- "Build an adversarial variant pack"
- "Add hard invariants and a drift budget to this benchmark"
- "Create a variant bundle focused on route drift"
Do not use this skill when
Do not use this skill when:
- the task is to execute the variant rather than author it
- the request is to judge observed drift rather than create the variant
- the variant idea is actually a benchmark mutation proposal for promotion review
- the request is only for a tiny wording correction in an existing variant file
Pattern
- Primary pattern: Generator
- Secondary pattern: Tool Wrapper
Why this fit works:
- the main job is to generate a structured variant bundle
- it also packages the repository's shared family rules, invariance expectations, and stress discipline
- a full Pipeline would add ceremony without enough value because execution and judging remain downstream roles
Inputs
Collect or infer these inputs when you can:
- base benchmark ID and title
- task family
- base route expectation
- expected artifact type
- why variant work is being requested now
- whether metamorphic, adversarial, or both modes are in scope
- known fragile findings or failure modes from prior judged runs
If some inputs are missing, make the smallest safe assumptions and mark them explicitly.
Workflow
Use the same sequence each time.
1. Frame the variant goal
State what the variant pack should reveal.
Examples:
- route stability under paraphrase
- robustness under misleading evidence
- validation-plan persistence under presentation change
- safe-failure behavior under partial visibility
Do not start from random transformations. Start from the diagnostic purpose.
2. Choose the variant mode and family set
Select only the smallest useful family or families:
- metamorphic:
M1paraphrase,M2context-order,M3noise tolerance,M4terminology substitution,M5presentation-style - adversarial:
A1injection-in-artifact,A2conflicting constraints,A3misleading evidence,A4partial visibility,A5tool degradation,A6stale-context contamination
Keep one main stressor per variant unless a compound test is explicitly justified.
3. Define the contract for each variant
For every variant, specify:
- variant type and family
- purpose
- main transformation or stressor
- route expectation
- expected invariant or safe-failure rule
- hard invariants
- soft invariants
- allowed drift budget
Do not leave drift interpretation implicit.
4. Package the transformed input cleanly
Create an execution-ready transformed input package.
It should be usable by a runner without reconstructing the scenario from scratch.
5. Return the variant bundle
Use assets/variant-bundle-template.md as the final output skeleton.
Output contract
Return the result using assets/variant-bundle-template.md in the same section order.
Output rules:
- choose only justified variant families
- keep hard invariants and soft invariants explicit
- keep drift budget explicit for metamorphic variants
- keep safe-failure expectation explicit for adversarial variants
- keep transformed input packages execution-ready
- avoid random prompt churn dressed up as evaluation rigor
Companion files
assets/variant-bundle-template.md— final variant-bundle output structurereferences/variant-sources.md— canonical family and workflow sources../benchmark-core/references/benchmark-skill-lifecycle.md— shared handoff map for benchmark skills
Validation
A good result should satisfy all of the following:
- each variant has a clear diagnostic purpose
- family choice is explicit and justified
- invariants and safe-failure rules are explicit
- drift budget is stated where metamorphic behavior matters
- transformed inputs are runnable without reconstruction theater
- one main stressor is preserved per variant unless compound stress is explicitly intended
Common pitfalls
- generating random paraphrases with no diagnostic value
- omitting hard invariants and then calling the result an invariance check
- mixing multiple stressors into one muddy variant
- leaving route expectation implicit on route-sensitive items
- writing a judge note instead of an execution-ready variant pack
Completion rule
This skill is complete when the agent has:
- defined the diagnostic goal of the variant work
- selected the right family or families
- authored explicit variant contracts with invariants or safe-failure rules
- packaged execution-ready transformed inputs
- returned a reusable variant bundle for downstream execution and judging