run

name	run
description	Launch and manage CodeScaleBench benchmark runs with paired-run guardrails, quick reruns, and execution orchestration.

Skill: Run Benchmarks

Scope

Use this skill when the user asks to:

• Run benchmark suites, rerun failures, or launch gap-fill batches
• Manage multi-account parallel execution
• Execute paired baseline+MCP runs with curation guardrails
• Perform quick reruns of specific tasks or suites

Approval Gate (Required Before Running)

Before executing any benchmark run, confirm with the user:

1. Model — which model? (e.g., anthropic/claude-haiku-4-5-20251001 for test runs)
2. Suite / selection file — which benchmark suite or --selection-file?
3. Config — paired (default), --baseline-only, or --full-only? Which --full-config?
4. Parallel slots — how many? (default: auto-detect; use 8+ for multi-account)
5. Category — staging (default) or official?

Do NOT launch a run until the user has confirmed these five parameters.

Canonical Commands

• Per-suite default: ./configs/harnesses/<suite>_2config.sh
• Unified selected-task runner: ./configs/harnesses/run_selected_tasks.sh
• Config registry: configs/eval_matrix.json
• Quick rerun: use --rerun flag on base command

Run Policy (Mandatory)

• Default execution is paired by task: baseline + sourcegraph_full
• Single-lane runs are gap-fill only:
  • --baseline-only requires valid existing sourcegraph_full counterpart runs
  • --full-only requires valid existing baseline counterpart runs
• Emergency bypass only: ALLOW_UNPAIRED_SINGLE_CONFIG=true
• Account readiness: Always run python3 scripts/infra/account_health.py status before launching

Standard Launch Patterns

# Paired per-suite run
./configs/harnesses/pytorch_2config.sh --parallel 4

# Paired selected-task run
./configs/harnesses/run_selected_tasks.sh --benchmark csb_sdlc_pytorch

# Gap-fill baseline only (guarded)
./configs/harnesses/run_selected_tasks.sh --benchmark csb_sdlc_pytorch --baseline-only

# Quick rerun of failed tasks
./configs/harnesses/run_selected_tasks.sh --benchmark csb_sdlc_pytorch --rerun failed

Infrastructure

• Default environment: Daytona (see docs/DAYTONA.md)
• Parallelism: Auto-detected from account count and rate limits; override with --parallel N
• Orchestration: scripts/running/control_plane.py manages multi-account scheduling
• Monitoring: scripts/running/monitor_and_queue.sh watches active runs

Related Skills

• /status — monitor active runs, check completion
• /audit — post-run validation and integrity checks
• /evaluate — extract and score results