agentleFS
benchflow-aibenchflow-ai/benchflow/.agents/skills/benchflow-traj-upload-ops/SKILL.md

benchflow-traj-upload-ops

Skill363 starsChanged 6 days agoApache-2.0View on GitHub

Operate, test, troubleshoot, and explain `bench traj upload` for public or trusted-direct trajectory contributions, including interactive and fully specified commands, dry runs, input validation, local secret masking, trajectory reports and previews, manifest metadata, upload progress, idempotency, and production promotion checks. Use this skill when a maintainer or operator wants to test, inspect, or debug a trajectory upload; validate a trajectory, report, or manifest; or verify the public upload path end to end. For helping a contributor submit their own session, use `benchflow-traj-upload` instead.

The file

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 7 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈7

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use the public contribution route for ordinary users.

    line 8
  2. Distinctive:

    Read references/cli-contract.md before explaining non-default modes, troubleshooting a failure, reviewing a generated manifest, or claiming …

    line 11
  3. Distinctive:

    Use the released CLI when validating the published user experience:

    line 18
  4. Distinctive:

    Use the repository checkout when validating unreleased PR behavior:

    line 25
  5. Very specific:

    Do not substitute the installed bench binary for uv run bench while testing unreleased code.

    line 32
  6. Distinctive:

    Prefer the guided flow when a person wants to inspect and confirm the capture:

    line 38
  7. Distinctive:

    Use the fully specified form for scripts or an intentional no-prompt upload:

    line 48
  8. Distinctive:

    Use a dry run before a real upload when testing new files or CLI changes:

    line 61
  9. Distinctive:

    Verify these invariants in the rendered report:

    line 75
  10. Very specific:

    Every detected secret value is replaced locally with <XXX-benchflow-key-values-XXX>.

    line 83

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Operate BenchFlow trajectory uploads

Use the public contribution route for ordinary users. Treat --direct as a trusted-operator escape hatch, not as an equivalent public-path test.

Read references/cli-contract.md before explaining non-default modes, troubleshooting a failure, reviewing a generated manifest, or claiming an end-to-end production result. The reference is the complete behavior contract for the current CLI.

Choose the matching CLI build

Use the released CLI when validating the published user experience:

uv tool install --python 3.12 --upgrade benchflow
bench traj upload

Use the repository checkout when validating unreleased PR behavior:

uv sync --extra dev --locked
uv run bench traj upload

Do not substitute the installed bench binary for uv run bench while testing unreleased code. Record bench --version or the exact Git SHA so the tested artifact is unambiguous.

Run the requested flow

Prefer the guided flow when a person wants to inspect and confirm the capture:

bench traj upload

The CLI asks for the path, renders a report from a locally redacted staging copy, asks for missing GitHub and email metadata (after trying gh / git inference), and defaults the upload confirmation to No.

Use the fully specified form for scripts or an intentional no-prompt upload:

bench traj upload <PATH> --github-id <GITHUB_ID> --email <EMAIL>

Providing all three required inputs makes the command non-interactive: it still renders the report, then uploads without a confirmation prompt. If any one is missing, the session is interactive and asks only for missing values before a final confirmation. Identity that resolves through gh / git inference also skips the confirmation; without a TTY, unresolved identity fails with the one-line --github-id / --email fallback instead of hanging on a prompt.

Use a dry run before a real upload when testing new files or CLI changes:

bench traj upload <PATH> \
  --github-id <GITHUB_ID> \
  --email <EMAIL> \
  --dry-run

A dry run validates, redacts, reports, and creates the temporary manifest, but never prompts for confirmation and never makes a network request.

Inspect before uploading

Verify these invariants in the rendered report:

  • Total steps = Thinking steps + Tool-call steps + Human steps.
  • Human steps are real user messages. Tool results, status or metadata records, empty records, and invented placeholders such as Assistant response are not trajectory steps.
  • Each preview row shows up to the first 100 words of a meaningful, already redacted step. --preview-steps accepts 0 through 20 and defaults to 5.
  • Every detected secret value is replaced locally with <XXX-benchflow-key-values-XXX>. Valid JSONL containing secrets is accepted after masking; the original source files remain unchanged.
  • File count, byte size, creation time, primary file, format, step counts, masked-value count, and preview are plausible for the selected capture.

Verify the result at the right boundary

For a dry run, report only local validation. For a real public upload, verify that the trusted validator promoted the digest to sources/community/<digest>/, with manifest.json present last and bound to the uploaded artifacts. A client success message or quarantine write alone is not production end-to-end proof.

Report whether the capture was uploaded, cancelled, already present, rejected, or only dry-run validated. Never expose contributor email, detected secret values, signed upload URLs, credentials, or internal service endpoints.

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