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データ・研究 · SKILL.md

analyze-publish

analyze-publish

Inspect, compare, regrade, export, share, upload, and publish Harbor results, trajectories, artifacts, Hub records, and versioned task or dataset packages with mutation and credential gates.

Independent GitHub snapshot · Automated classification · Original documentation governs

スキルの説明

GitHub

name: analyze-publish description: "Inspect, compare, regrade, export, share, upload, and publish Harbor results, trajectories, artifacts, Hub records, and versioned task or dataset packages with mutation and credential gates." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Analyze and publish Harbor outcomes

Use this skill when a Harbor run already exists and the task is to inspect its results, understand trajectories or artifacts, compare runs, regrade a recorded execution, browse the local viewer or Hub, export traces, upload/share job results, or publish and version task/dataset packages. Keep the source record immutable unless a command below explicitly creates a derived result or asks for confirmation.

Route by intent

Route task/schema/verifier authoring to author-benchmarks, job/provider/agent execution failures to run-evaluate, and custom framework or plugin code to integrations. Do not relaunch an evaluation merely to inspect a recorded run.

Safety and mutation gates

  1. Identify whether the input is a local trial directory, local job directory, Hub job/trial UUID, task package, or dataset package before selecting a command. Check harbor --version when command behavior may be version-sensitive.
  2. Prefer local parsing, --json, harbor hub ... show/list/tasks/trials/shares, harbor version list/show, and harbor view for read-only inspection.
  3. Treat harbor analyze and harbor check as source-preserving but not cost-free: they create derived Harbor jobs, may run an evaluator agent and environment, and write reports. Show rubric, agent/model, environment, attempts, filters, and output directory before running them.
  4. Never run harbor upload, Hub copy/share/retry/cancel/delete, visibility or access changes, harbor publish, or harbor version tag without explicit approval for the exact target, credentials, visibility, recipients, and mutation. harbor traces export is local by default; --push or OTLP endpoint upload is an external mutation and needs the same gate.
  5. Keep credentials in the CLI's auth flow or environment. Do not print tokens, copy private result contents into prompts, or infer that a local login grants access to another organization.

Safe operating sequence

  1. Inventory the local tree (config.json, lock.json, result.json, job.log/trial.log, artifacts/manifest.json, agent/trajectory.json, native session files, retry folders) and preserve a digest or copy when an audit needs reproducibility.
  2. Read result and manifest JSON before opening large logs. Separate the recorded agent/model/configuration from verifier rewards, exceptions, costs, retries, and post-hoc analysis. A retry is a new attempt; a regrade is a new derived trial that does not rerun the agent.
  3. Use the narrowest read-only command or local parser. For a Hub record, inspect with harbor hub job show, tasks, trials, shares, compare, or hub trial show; use --json for machine processing and --include-retries when retry history changes the conclusion.
  4. For a regrade, verify the source has readable result.json, a trustworthy artifact manifest and bytes, and that the replacement verifier is single-step and separate-mode. Use harbor job regrade or harbor trial regrade; record the new output path and compare the printed reward delta.
  5. For any external operation, preview the exact command and expected side effects, obtain confirmation, check authentication, then verify the returned ID, visibility, revision, digest, or URL. Record partial failures and retry only the failed/idempotent portion.

What this skill does not claim

Help/parser checks and mocked/unit evidence validate command contracts only. Docker, cloud providers, model APIs, Hub/registry credentials, Hugging Face, OTLP endpoints, GPU, and frontend build toolchains are optional capabilities and must remain explicitly unverified unless actually exercised with approval.

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