running-coral-experiments

name

running-coral-experiments

description

Run and manage CORAL experiments from the operator side — launch agents with `coral start` (dotlist overrides, model/count, tmux vs local), monitor with `coral status` / `coral log` / `coral show` / the web dashboard, and drive the loop with `coral resume` (inject instructions, fork from an attempt), `coral heartbeat` (tune reflection cadence), and `coral stop`. Use whenever the user wants to start a CORAL run, check on agents, read scores/leaderboard, steer or resume a run, diagnose agents that keep restarting or fail every eval, scale to more agents or islands, or stop a run. Deep references for steering/heartbeat tuning and scaling/troubleshooting live alongside this skill.

Running CORAL experiments

You drive a run with five verbs: start → status → log/show → resume → stop. Everything else is a flag on those or a deeper topic in the references. Prefer coral <cmd> --help over guessing flags.

Prereq: a task (task.yaml + seed/ + grader package) that passes coral validate .. No task yet → that's the creating-a-coral-task skill. Each runtime CLI must be installed and authenticated → the setting-up-coral skill.

1. Launch

coral start -c task.yaml                                  # auto-tmux session
coral start -c task.yaml agents.count=4 agents.model=opus # dotlist overrides (no quotes needed)
coral start -c task.yaml run.verbose=true run.ui=true     # verbose logs + web dashboard
coral start -c task.yaml run.session=local                # foreground, no tmux

• Dotlist overrides (key.subkey=value) beat task.yaml for this run only — the clean way to sweep count/model without editing the file.
• run.session: tmux (default, detachable) · local (foreground) · docker.
• Each run lands in results/<task-slug>/<timestamp>/; agents work in isolated git worktrees and the grader daemon scores their commits.

2. Monitor

coral status            # agent health + leaderboard snapshot (the quick pulse)
coral runs              # active runs across tasks; --all includes finished
coral ui --port 8420    # web dashboard: live leaderboard, logs, DAG

coral status answers "who's alive, how many evals, current best". If it looks healthy but scores never move, jump to budget classes + troubleshooting in references/scaling-and-ops.md.

3. Read results

coral log                              # top 20 real attempts by score
coral log -n 5 --recent                # most recent instead of best
coral log --search "kernel" --agent agent-1
coral log --class grader_error         # surface crashing graders (first stop when unhealthy)
coral show <hash>                      # one attempt: score, explanation, files changed
coral show <hash> --diff               # full diff — see exactly what the leader did

<hash> comes from coral log/coral status. By default coral log hides tune and grader_error attempts; --all shows them, --class {real|tune|grader_error} filters to one. What the classes mean → references/scaling-and-ops.md.

4. Steer and resume

coral resume                                   # resume latest run, sessions restored
coral resume -i "Try greedy approaches first"  # inject guidance agents read next loop
coral resume --from <hash> -i "Continue this fork"   # reset an agent to an attempt, then steer
coral export <hash> -b winning-idea            # export an attempt's commit as a git branch

resume -i is how you nudge a run without restarting from scratch (stop → resume with an instruction). --from forks a promising line that later regressed. You can also retune the reflection cadence — coral heartbeat set/remove/reset — to make agents reflect less, pivot sooner, etc. Both topics, with worked examples: references/steering.md.

5. Stop

coral stop          # stop the current/latest run (picker if several)
coral stop --all    # stop every active run

Stopping leaves all results, notes, and the leaderboard on disk — coral resume later, or just inspect with coral log/coral show.

Typical loop

coral validate .                            # grader scores the seed (once)
coral start -c task.yaml agents.count=2     # launch
coral status                                # ... check periodically
coral log -n 5 --recent                     # see what agents are trying
coral show <best-hash> --diff               # inspect the leader
coral resume -i "Focus on the inner loop"   # steer if they plateau
coral stop                                  # done

Going deeper

• Steer / fork / heartbeat tuning → references/steering.md
• Budget classes, islands, gateway, troubleshooting matrix → references/scaling-and-ops.md

Note: coral eval / diff / revert / checkout / wait are agent-side commands run inside a worktree during a run — agents already know them from the generated CORAL.md. As the operator you rarely touch them; you drive the verbs above. Full CLI reference: https://coral.compounding-intelligence.ai/docs/cli/reference