AGENTS.md

AGENTS.md — writing InfraBench tasks

This file is for coding agents and humans who author benchmark tasks. It is intentionally scoped to task packages. The evaluation harness (Syscraft) remains closed-source for now.

Non-negotiables

1. Every task is a directory with task.toml, instruction.md, environment/, and tests/.
2. The verifier is the source of truth: write reward.txt or reward.json under /logs/verifier/.
3. Do not bake secrets, API keys, or private CloudLab credentials into the task tree.
4. Prefer structured checks with scored: true|false over a single opaque score.

Layout

task/
├── task.toml
├── instruction.md
├── environment/     # Dockerfile  OR  setup.sh [+ bootstrap.sh]
├── solution/        # optional solve.sh
└── tests/           # test.sh (+ helpers)

Canonical examples in this repo:

Path	Role
tasks/hello-world	Minimal Docker task — start here
tasks/ipmi-node-power-recovery	L1 / Easy / BM Cluster
tasks/cassandra-nic-split-brain	L2 / Medium / BM Cluster
tasks/cassandra-dead-node-removal	L3 / Medium / BM Cluster
tasks/vm-ceph-bootstrap	L3 / Hard / VM cluster
tasks/db-wal-recovery	L4 / Hard / Container

Environment types

environment.type	When to use
docker	Userspace / container-local work
vm	Kernel modules, block devices, single-node systems work
vm-cluster	Multi-node distributed systems
cloudlab	Bare-metal / IPMI / real NIC scenarios

VM hosts need Linux + KVM. CloudLab tasks need a profile and site access — document that in the PR, do not commit credentials.

Verifier patterns

Scalar:

#!/bin/bash
set -euo pipefail
# ... assertions ...
echo 1.0 > /logs/verifier/reward.txt

Structured (preferred for InfraBench paper-style tasks):

#!/bin/bash
set -euo pipefail
mkdir -p /logs/verifier
python3 - <<'PY'
import json, pathlib
checks = [
  {"name": "service up", "passed": True, "scored": False},
  {"name": "data consistent", "passed": False, "scored": True},
]
reward = 1.0 if all(c["passed"] for c in checks) else 0.0
pathlib.Path("/logs/verifier/reward.json").write_text(
    json.dumps({"reward": reward, "checks": checks})
)
PY

Writing instruction.md

• State the goal and success criteria the agent can observe.
• Include operational constraints (do not destroy X, preserve Y).
• Do not paste the root-cause spoilers unless the task is explicitly a known-fault drill.
• Keep it short enough that an agent can act; put long background in comments inside environment/ scripts if needed.

PR expectations

See CONTRIBUTING.md for the human checklist. Agents should open a PR that:

1. Adds tasks/<name>/ with a complete package
2. Mentions layer (L1–L4), difficulty, and backend in the PR body
3. Links related paper sections when the task is a HotInfra / InfraBench scenario

Out of scope here

• Implementing new agent adapters
• Changing the Syscraft CLI or environment factory
• Publishing private evaluation traces

Those stay in the closed Syscraft tree until the runtime is released.