CORAL

Verify and debug changes to CORAL itself — smallest reproduce loop per area (grader / daemon / CLI / hooks / manager / workspace / hub / template / config / web), where to look when something breaks (hung graders, agent restart loops, stalled agents, missing heartbeat actions, corrupted shared state, broken worktree symlinks, grader import errors, wrong-task resume), how to inspect a live or finished run under `.coral/public/`, and the canonical lint/test commands. Use when editing code under `coral/` or chasing a CORAL bug, NOT when adding a new task or extending the framework.

Add a new component to the CORAL framework itself — a new agent runtime under `coral/agent/builtin/` (claude_code/codex/cursor_agent style), a new CLI command in `coral/cli/`, a new bundled skill or subagent template under `coral/template/skills/` or `coral/template/agents/`, a new hook in `coral/hooks/`, a new field in `coral/config.py`, or a framework-level extension to the grader stack under `coral/grader/`. NOT for writing a per-task grader or adding an example task — use `coral-new-task` for that. NOT for debugging existing code — use `coral-debug`.

End-to-end recipe for adding a new task under `examples/` — the three pieces that have to line up (`task.yaml`, `seed/`, and `grader/`), what to put in each, the `TaskGrader` API surface, the `coral validate` → smoke-test loop, and the common mistakes (repo_path pointing at the wrong dir, score direction backwards, hidden answer keys leaking into seed/, grader writing to codebase_path which the daemon force-removes, private-vs-public confusion, missing `run()` signature). Use whenever the user wants to add a new CORAL task or port an existing benchmark into CORAL.

Research the problem domain before coding. Web search for techniques, save raw sources, write structured findings, update the index.

Organize the shared notes directory when it becomes hard to navigate. Restructure within research/ and experiments/, deduplicate, update index.md.

Autonomously create, test, and optimize skills by detecting reusable patterns in your own work. Use when you notice repeated tool sequences, recurring code patterns across attempts, or insights that should be captured as a packaged skill. Also use to benchmark and iterate on existing skills.

The fast path from zero to a running CORAL experiment — what CORAL is and when to reach for it, installing the `coral` CLI, registering a runtime with `coral setup`, and the `.coral_workspace/` convention for pointing CORAL at code you already have and want optimized. Use this whenever the user asks "what is coral", "should I use coral for this", wants to install or get coral set up, hits a "command not found" for coral or doesn't have it installed yet, or says "use coral to optimize / speed up / improve this code" and you need the end-to-end onboarding from install to a launched run. Hands off to `setting-up-coral` (runtime bindings), `creating-a-coral-task` (grader authoring), and `running-coral-experiments` (operating a run) for depth.

Author a new CORAL task — the three pieces that must line up (`task.yaml`, `seed/`, a packaged `grader/`), the `coral init` → `coral validate` → smoke-test loop, and how to pick a grader pattern (stdout float, test pass-rate, ratio-vs-baseline, multi-metric, or an LLM rubric judge). Use whenever the user wants to create a CORAL task, write or wire a grader, port a benchmark into CORAL, score open-ended outputs (reports/memos) with a judge, or debug a grader that crashes on the seed / ranks the leaderboard backwards / leaks the answer key. Deep references for the TaskGrader API, grader patterns, rubric judges, and the full task.yaml schema live alongside this skill.

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.

One-time machine setup after installing the `coral` CLI — register local agent runtimes as named bindings with `coral setup` / `coral setup agent`, validate them with `coral agents doctor` (incl. a live hello-ping that catches expired auth and model typos), and reference them from a task via `agents.binding`. Use when the user is configuring which agent runtimes/models coral can use, hits a "runtime not found" / auth error when starting a run, or asks how to set up claude/codex/cursor for coral.