/deepline-plays-review

name: deepline-plays-review
description: 'Use this skill when a human needs to review a Deepline Play result and hand feedback, labels, or approval back to the agent for revision, evaluation, comparison, or bounded iteration. Triggers on “put this run in a Sheet,” “review these results,” “read my feedback,” “make this a standing rule,” “never regress on this case,” “compare these runs,” “keep improving,” or “add the revised run to the same spreadsheet.” Skip ordinary Play authoring or execution with no human review loop, generic spreadsheet work, and one-off CSV export.'

Review and Improve Deepline Plays

Quick Start

npm install -g deepline
deepline -h

CLI resolution

Run `deepline` when it is available. If the shell reports that command is missing, use `<workspace-root>/.deepline/runtime/bin/deepline` (or the npm-created `.cmd` shim on Windows). If neither exists, follow `https://code.deepline.com/INSTALL.md` to set up Deepline.

Loop: `revision → run → assess → decide → next revision`. Google Sheets is the human review surface; the Play revision, completed run, and durable dataset remain the execution record.

Route the request

| User intent | Start here | | ------------------------------------ | ---------------------------------------------- | | Put a run in a Sheet | Export the completed dataset for review | | Address edits, notes, or comments | Read fresh feedback and run one revision | | Make feedback a standing rule | Record a general expectation | | Never regress on a corrected case | Add a case-specific expectation or golden case | | Compare revisions | Evaluate both against one frozen basis | | Try several improvements | Establish a bounded agent-driven loop | | Keep improving together across turns | Resume the loop and yield after each candidate |

**Stop at planning boundaries.** When the user asks for a plan, classification, or proposed evaluation before any calls or edits, write it from the supplied context and stop. Do not inspect live Plays, runs, files, or tool contracts, even through read-only commands: exploration cannot grant missing authority or define a budget, and it can start paid work. Resume discovery only after the user asks to proceed.

To only build, run, or debug a Play, use `deepline-gtm`; for unrelated spreadsheet manipulation, use the relevant spreadsheet workflow. This skill begins when a result will be reviewed, measured, or used to change the Play.

Establish or resume the loop

• **Revision:** the Play version under test.
• **Run:** that revision executed on known inputs.
• **Expectation:** what good means, generally or for a specific case.
• **Assessment:** evidence about how a run met an expectation.
• **Golden dataset:** a versioned collection of representative inputs with

case-specific expectations.

• **Evaluation:** a reproducible assessment of one revision against a fixed

basis.

• **Optimization:** permission to repeat the loop, not a new kind of evaluation.

Before changing the Play, state or recover: the objective; baseline revision and run; applicable expectations; human-stepped or agent-driven control; allowed changes; budget and stopping rule; and run, dataset, spreadsheet, and tab breadcrumbs.

Default to one candidate and then yield. Trying multiple candidates spends credits and can move farther than the user intended, so first require explicit authority, mutation scope, budget, and a stopping rule.

Work in a durable directory: files in `/tmp` disappear along with the evidence needed to resume. Set `WORKDIR="deepline/data/<descriptive-slug>"` and run `mkdir -p "$WORKDIR"`.

Tool names in this skill are starting hints. Discover the live Workspace tools and confirm their contracts before first use:

deepline search "Play run dataset -> spreadsheet" --type tools
deepline search "spreadsheet ID -> its rows and comments" --type tools
deepline describe google_workspace_export_dataset
deepline describe google_workspace_request

Run and review

Run the Play with `deepline run` and keep its run ID from the receipt. When provider calls are involved, pilot on one or two rows before scaling, so a wrong payload or output shape does not waste credits across every candidate. Preserve source and status columns: they explain why a row passed or failed. Then inspect the completed run and choose the durable dataset the user wants to review:

STAMP="$(date -u +%Y%m%dT%H%M%SZ)"
deepline run <play> --input-file "$WORKDIR/input.json" \
  --receipt-out "$WORKDIR/run-receipt-$STAMP.json"
RUN_ID="$(jq -er .id "$WORKDIR/run-receipt-$STAMP.json")"
deepline runs get "$RUN_ID" --full --json > "$WORKDIR/run-$STAMP.json"
jq -e '
  .datasets | if type != "array" then error("Expected datasets array") else .[] end |
  if (.datasetId | type) != "string" or .datasetId == "" then error("Missing dataset identity")
  else {datasetId, name, table: .storage.table, rowCount} end
' "$WORKDIR/run-$STAMP.json"

The CLI returns a `run_overview`: datasets are at `.datasets` and raw diagnostics (including billing) at `.diagnostics`. Raw SDK/API responses use `package.datasets` and `billing`; do not apply those paths to CLI JSON. Set `DATASET_ID` to an advertised `datasetId`; the export tool accepts it directly. If the dataset list or identity is missing or malformed, stop and retrieve valid run evidence instead of guessing `result.rows`.

Export the persisted dataset by its `datasetId`, never receipt or CLI preview rows, which are only samples. Use one operation key for one intended export; reuse it only to retry that exact request. Leave `SPREADSHEET_ID` empty for a new workbook, or set it to append a new immutable run tab to an existing workbook:

: "${SKILL_DIR:?Set SKILL_DIR to the installed deepline-plays-review directory}"
: "${RUN_ID:?Se