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scenario

Name: Scenario
Author: brainqub3

// Create and execute scenario YAML files for architecture what-if predictions.

تشغيل في Manus

$ git log --oneline --stat

stars:١٦

forks:٦

updated:١٢ فبراير ٢٠٢٦ في ١٦:٢٨

SKILL.md

readonly

name	scenario
description	Create and execute scenario YAML files for architecture what-if predictions.
allowed-tools	["Read","Edit","Bash"]

scenario

Steps

Resolve the SAS empirical baseline first:
- Use SAS architecture run metrics (derived_metrics.json -> P) from the relevant comparison batch.
- If multiple SAS runs exist in the batch, pick one deterministic baseline and state which run id was used.
- Do not use a hypothetical/model-predicted SAS point as the comparison baseline.
Collect model intelligence I, tool count, and candidate architectures.
Populate YAML with priors or explicit overrides, setting P_SA from the resolved SAS run baseline.
Run brainqub3 scenario run --scenario <path>.
Report scenario results relative to the SAS run baseline:
- delta_vs_sas = P_architecture - P_SAS_run_baseline
Save comparison outputs for dashboard/report consumption.

related-skills.json

نفس المستودع

arena.md

from "brainqub3/agent-labs"

Prepare and run evaluator-gated SAS and MAS experiments with explicit batch control and mandatory elasticity calibration for scenario scaling.

2026-02-1116

cleanup-reset.md

from "brainqub3/agent-labs"

Reset local experiment state to a fresh slate by clearing runs, prediction outputs, scaling snapshots, and database index files under data/.

2026-02-1116

task-author.md

from "brainqub3/agent-labs"

Create or repair Brainqub3 task packages that must pass evaluator tests before runs, including both fabricated instances and user-provided data workflows.

2026-02-1016

eval-builder.md

from "brainqub3/agent-labs"

Build or repair task evaluators and evaluator tests with deterministic-first strategy.

2026-02-0916

report.md

from "brainqub3/agent-labs"

Generate markdown reports with observed metrics, predicted metrics, and architecture recommendations.

2026-02-0916

package.json

"author": "brainqub3"

"repository": "brainqub3/agent-labs"

فتح مستودع GitHub عرض مستودعات المنشئ

$ install --global

$ download --local

تشغيل في Manus

$ useful --forSOC

مطوّرو البرمجياتمهن الحاسوب والرياضيات15-1252L4

name	scenario
description	Create and execute scenario YAML files for architecture what-if predictions.
allowed-tools	["Read","Edit","Bash"]

scenario

Steps

Resolve the SAS empirical baseline first:
- Use SAS architecture run metrics (derived_metrics.json -> P) from the relevant comparison batch.
- If multiple SAS runs exist in the batch, pick one deterministic baseline and state which run id was used.
- Do not use a hypothetical/model-predicted SAS point as the comparison baseline.
Collect model intelligence I, tool count, and candidate architectures.
Populate YAML with priors or explicit overrides, setting P_SA from the resolved SAS run baseline.
Run brainqub3 scenario run --scenario <path>.
Report scenario results relative to the SAS run baseline:
- delta_vs_sas = P_architecture - P_SAS_run_baseline
Save comparison outputs for dashboard/report consumption.

scenario

scenario

Steps

المزيد من هذا المستودع

المزيد من هذا المستودع

scenario

Steps