opendfm/xcientist/resource-acquisition

Claude Code and agent skill by opendfm/xcientist

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Deterministic protocol for repository, dataset, and benchmark acquisition

Install

npx vskill@latest install opendfm/xcientist/resource-acquisition

SKILL.md

Resource Acquisition

Mission

Prepare real, verifiable resources for the current experiment.

CRITICAL: Code in `project/` must be SELF-CONTAINED

  • The code in project/ must be COMPLETE and INDEPENDENT.
  • It must NOT depend on repos/ for core functionality.
  • Repos are for REFERENCE ONLY - implement actual experiment code in project/.

Protocol

  • Read idea.json or agent_reports/prepare/artifacts/idea.md first.
  • Discover repositories, datasets, and benchmarks before writing documentation.
  • Treat pre-existing directories as unverified until file checks pass.
  • Return machine-readable status in the final worker JSON; the runtime persists the stage worker report.
  • Record an explicit real experiment target inventory in the managed prepare artifacts and final worker JSON:
  • exact models/checkpoints or API-backed models
  • exact dataset files/directories
  • exact benchmark entrypoints
  • exact API env vars and invocation surface
  • an explicit description of what later phases must run
  • If the real experiment path requires local model checkpoints, acquire them during prepare rather than deferring them to science.
  • The dataset acquisition step may also download model checkpoints when those checkpoints are part of the formal benchmark target.
  • Create agent_reports/prepare/artifacts/idea.md only after verification work is complete, not as an early summary artifact.
  • For HuggingFace downloads, use the official HuggingFace endpoints and inherit the current shell network environment instead of rewriting proxy settings inside the agent.
  • agent_reports/prepare/artifacts/idea.md must use these EXACT headings:
  • ## Idea Summary
  • ## Idea JSON Components
  • ## Code Implementation Guidance
  • ## Component Correspondence
  • ## Dataset Usage Guidance
  • ## Environment Variable Usage Guidance
  • ## Resource Acquisition Log
  • ## Repository-to-Dataset Mapping
  • ## Real Experiment Targets
  • ## Canonical Idea Components
  • Under ## Canonical Idea Components, copy every idea.json.components entry exactly once, in the same order, preserving the component name and explanation.
  • After writing agent_reports/prepare/artifacts/idea.md, reopen it and confirm those headings exist before claiming completion.

Report Requirements

The runtime owns reports. Do not write report files with tools. Each stage keeps worker/review/hook attempts under agent_reports/prepare/{worker,review,hook}/<stage>/.

Required Outputs

  • Final worker JSON response requested by the planner; the runtime persists the worker report.
  • Verified repository list with local paths
  • Verified dataset list with expected_paths
  • Verified local model/checkpoint list when the formal path depends on local models
  • Verified benchmark list with runnable entrypoint or explicit blocker
  • Real experiment target declarations with concrete model/API/data choices
  • agent_reports/prepare/artifacts/idea.md with the required headings and verified content
  • Canonical component handoff copied from idea.json.components

Failure Conditions

  • Resource directories exist but no expected files are checked.
  • A benchmark is claimed runnable without an entrypoint or smoke command.
  • A dataset is claimed downloaded without explicit file evidence.
  • Model/API/data targets are described vaguely instead of concretely.
  • An API-backed dependency is needed but no usable invocation surface is documented.
  • agent_reports/prepare/artifacts/idea.md is only a generic project summary and does not contain the required protocol headings.
  • agent_reports/prepare/artifacts/idea.md omits, renames, duplicates, or reorders the canonical idea components.
  • The agent prints completion before performing a final read-back validation of agent_reports/prepare/artifacts/idea.md.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

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