version: "1.0.1" name: creating-a-coral-task description: 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.

Creating a CORAL task

A CORAL task is three things that must line up. Scaffold them with coral init, then iterate edit → coral validate until the grader scores the seed.

my-task/
├── task.yaml      # config: name, description, grader entrypoint, agent count
├── seed/          # starter code agents see at t=0 (this is workspace.repo_path)
│   └── solution.py
└── grader/        # standalone Python package — gets its own isolated venv
    ├── pyproject.toml
    └── src/my_task_grader/
        ├── __init__.py
        └── grader.py     # class Grader(TaskGrader): ...

The packaged grader is the only supported form — it gives the grader an isolated venv and bundles everything the eval needs (grader code, helpers, hidden answer keys). There is no eval/grader.py auto-discovery anymore.

Optimizing code the user already has? Scaffold inside a .coral_workspace/ at the root of their project (gitignored), and copy the code to optimize into seed/ — keeps CORAL's task/results out of their source tree. The coral-quickstart skill has the end-to-end .coral_workspace/ flow; this skill covers the grader you'll write once the code is in seed/.

"Optimize this" is a build instruction, not a question — never answer it with a process menu. A 1/2/3 like "point me to a task / create one / optimize outside coral" is the failure mode; do not produce it. The absence of a task.yaml is not ambiguity — it just means you build one from the current repo. Concretely: (1) dig for what's already measurable — a research/framework repo almost always ships an eval/benchmark script, a test suite, or a metric in its README/paper; that's your target and metric. (2) If no single number is obvious, construct one by wrapping the repo's existing evaluation — don't conclude "no measurable objective" just because there's no CORAL scaffold. (3) Scaffold the most plausible target and start building (a .coral_workspace/ + draft grader is cheap and reversible); state your assumption in one line and proceed. (4) Only as a last resort, if you've actually read the repo and it exposes nothing scorable, propose 2-3 concrete optimization targets you found (each with its metric), pick the most likely, and scaffold that — still not a process menu.

The loop

coral init my-task        # scaffold all three pieces (a runnable end-to-end example)
cd my-task
# ... edit the three pieces for your problem ...
coral validate .          # bootstraps the grader venv, runs the grader on seed/, prints a score
# repeat edit → validate until the seed scores as you expect

`coral validate` succeeding is the one checkpoint that matters — it proves the grader can score the seed. Most "agents are stuck, every eval fails" reports trace to a grader that crashes on the seed, which validate would have caught. Always start from coral init rather than hand-writing the layout; the generated files are the canonical minimal example.

The three pieces

1. The seed (seed/) — what the agent checks out at t=0 and what the grader later scores. The contract between seed and grader is the program file: a file (e.g. solution.py) with a function or stdout convention the grader invokes, named in grader.args.program_file. Put a real, runnable baseline here — agents should coral eval immediately and get a non-zero score to beat. A skeleton that crashes is a bad baseline. Runtime data goes under seed/data/ and is read by relative path.

2. The grader (grader/) — subclass TaskGrader, implement evaluate(), return a number (or ScoreBundle). The minimum:

from coral.grader import TaskGrader
class Grader(TaskGrader):
    def evaluate(self) -> float:
        result = self.run_program(self.args.get("program_file", "solution.py"))
        if result.returncode != 0:
            return self.fail(f"crashed: {result.stderr[:200]}")
        try:
            return float(result.stdout.strip())
        except ValueError:
            return self.fail(f"expected a float, got {result.stdout[:80]!r}")

This stdout-float shape is one of several. Pick the pattern that matches how your task scores → references/cookbook.md:

Full TaskGrader surface — every attribute (self.codebase_path, self.private_dir, self.args, self.eval_logs_dir, self.tune) and method (run_program, run_script, run_script_json, score, fail, bundle) — is in references/grader-api.md.

3. The task.yaml — wiring. The fields that must be right are grader.entrypoint, grader.direction, and workspace.repo_path: ./seed. Full annotated schema (agents, islands, sharing, gateway, all defaults) → references/task-yaml.md.

Hidden data

The single rule: everything inside the `grader/` package is visible to agents — the whole grader source is surfaced read-only at <shared_dir>/grader/ so they can read how they're scored — so secrets go in `grader.private`, in a dir outside grader/. CORAL copies those paths into .coral/private/ (which every runtime is denied read access to) and the grader reads them via self.private_dir. Declare a sibling, conventionally taskdata (resolving to <task_dir>/taskdata); a grader.private path inside grader/ would be copied to .coral/private/ and leaked via the surfaced source, so coral validate errors on it. Non-secret bundled data (lookup tables, helper modules) may sit inside grader/ and be read via Path(__file__).parent / ... — it's visible, so never put a secret there. Never put an answer key under seed/ either — agents read seed/ and will game the score.

Smoke-test, then scale

coral start -c task.yaml agents.count=1 run.session=local   # one agent, foreground
# watch for one real eval, confirm the score moves, then:
coral stop

Once one agent evals cleanly, raise agents.count. Driving the run from here is the running-coral-experiments skill.

Common mistakes

When in doubt, run coral init throwaway and read the generated files. Full config schema: https://coral.compounding-intelligence.ai/docs/api/config