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Get Started Free →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
.claude/skills/human-agent-society-running-coral-experiments/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -27% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 65% | 0% |
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.
bashcoral 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
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.results/<task-slug>/<timestamp>/; agents work in isolated git worktrees and the grader daemon scores their commits.bashcoral 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.
bashcoral 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.
bashcoral 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.
bashcoral 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.
bashcoral 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
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
Other measured skills in the registry, with their headline benchmark lift.