Scenario Quality Gate

Scenario Quality Gate

Overview

One tool, asset_quality_gate_run, scores a finished image asset and returns a pass/warn/fail verdict with 0 to 100 scores and per-dimension lists of reasons and suggestions: an AI-quality check always, plus brand-brief compliance when the team or project has a brief configured. Image assets only. Quality Gate is an Enterprise add-on: when it is not enabled for the team the call fails cleanly, so detect that, fall back to an asset_analyze review (see scenario-asset-analysis), and say so. Retrying never clears it.

The tool is catalog-only and write-class: get the schema with scenario_tools_search, then run it through scenario_tool_execute_write with {name: "asset_quality_gate_run", parameters: {...}}, scope ids inside parameters (the read executor rejects it by lane and names the right one). Or reconnect with ?toolsets=full. Connection and scope: see the scenario skill. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Quick reference: what a call costs

A plain call is a read only when a usable verdict already exists; otherwise the same call silently escalates to the billed analysis. "No usable verdict" covers both never-scored and a stored error result. When spend needs approval first, probe free: dry_run: true, or asset_get, whose qualityGate field carries the verdict and scores when one is stored (summary only; the stored reasons and suggestions come back through the tool, still free). sensitivity (low, medium, high; default is the team or project setting) shapes new analyses only and is ignored on stored reads. rerun: true is for after the brief or the sensitivity changed, nothing else: it re-bills and overwrites.

Reading the verdict

The response is source plus quality_gate: verdict, overallScore, aiQualityScore, briefComplianceScore, sensitivity, appliedBriefIds, and details. With no brief configured the compliance dimension is absent entirely (appliedBriefIds: [], overallScore equals aiQualityScore); with one, details.briefCompliance sits beside details.aiQuality, each carrying a score and lists of reasons and suggestions, usually several of each.

Turning the verdict into a better image

A verdict alone only sorts assets. The gate earns its cost when the feedback drives the next attempt:

• reasons name concrete flaws ("elongated finger anatomy on the left hand", "background gradient off the brief's palette"). Use them to pick the fix path per flaw: a local defect on an otherwise approved image is a masked inpainting pass (scenario-image); a global one is a regeneration.
• suggestions are written as edit instructions, often worded for manual retouching. Apply all that fit, not just the first: translate them into the next model_run prompt or parameters, or into the edit instruction.
• A regenerated image is a new asset, so a plain call scores it. rerun is never part of the loop. It may even come back pre-scored for free: teams with auto-detect enabled (qualityGateAutoDetect: true on their teams_list row) score new generations automatically.
• When a round repeats the same flaw classes at an unmoved score, rewording will not fix them: switch models (recommend again) or repair the flaw with a masked edit before spending another round.
• Fix the exit bar and a round cap up front: verdict: "pass" by default, or a score target the user names (overallScore at or above 90, say; the named bar then outranks a bare pass), and three rounds unless told otherwise, since every round bills a generation plus an analysis. At the cap, or when a round stops moving the scores, stop: report the best asset with its remaining flaws and ask before spending more rounds; unattended, deliver that best asset and flag the miss.

scenario-refine-loop carries the loop discipline beyond the gate: one variable per round, regeneration from the approved baseline, and fix routing when the criteria go past the brief.