---
name: scenario-asset-analysis
description: "Use when Scenario assets need reading rather than generating: captioning images for a dataset or alt text, extracting a reusable style description from an on-model reference, asking what an asset shows, checking a batch against a brief before delivery, or extracting a canny, depth, pose, or segmentation control map. Also when finished assets need filing into collections or tagging. Keywords: caption, describe, analyze, review, QA, control map, ControlNet, segmentation, tag, collection."
license: MIT
---

# Scenario Asset Analysis

## Overview

Four tools read assets back instead of making new ones: three fixed-purpose, one open-ended. They answer the questions that come after a batch lands, which is where most of the work actually is: is this on brief, what look is this, what does this show, what can the next model condition on.

None of them are in the default toolset. Get schemas with `scenario_tools_search`, then run each through the executor matching its `permission`: `asset_caption` and `asset_describe` are read-class, `asset_analyze` and `asset_detect` are write-class. Or reconnect with `?toolsets=full`. Connection and scope: see the `scenario` skill in this repo. 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

| Need                                 | Tool                    | Shape                                                             |
| ------------------------------------ | ----------------------- | ----------------------------------------------------------------- |
| A caption for one image              | `asset_caption` (read)  | `details_level`: `action` or `action+style`                       |
| A reusable look off one image        | `asset_describe` (read) | Returns a full description plus a promptable synthesis            |
| Anything else, in your own words     | `asset_analyze` (write) | `instruction` plus up to 10 `images` and 10 `text_inputs`         |
| A conditioning map for another model | `asset_detect` (write)  | `modality`, one of ten including canny, depth, pose, segmentation |

All four bill credits and all four take `dry_run: true` for an estimate first. Read-class does not mean free.

## The three facts that change the plan

- **`asset_analyze` batches.** One call carries up to 10 images against one `instruction`, so reviewing 200 assets is 20 calls, not 200. `num_outputs` (1 to 5) is unrelated: it returns several distinct answers to the same instruction, not one answer per image. Ask for a fixed per-image output shape in the `instruction` so the answers stay parseable.
- **`asset_detect` strips the background by default.** `remove_background` defaults to true, so a depth or pose map comes back with the frame's context already gone. Set it false whenever the map has to cover the whole image.
- **Fixed beats flexible when it fits.** `asset_caption` and `asset_describe` are purpose-built and faster than instructing an LLM to do the same job. Reach for `asset_analyze` for classification, extraction, comparison, translation, or a verdict against a brief.

`asset_analyze` and `asset_detect` wait up to 180s and then hand back a `job_id`; carry on with `jobs_wait` as anywhere else.

## Worked example: reviewing a batch against a brief

1. Collect the asset ids from the run (`jobs_wait` returns them).
2. `asset_analyze` with `dry_run: true` on the first chunk to price the pass.
3. `asset_analyze` with `images` set to 10 ids and an `instruction` that states the brief and fixes the output: "For each image in order, reply `<index>: pass|fail, <reason in under 12 words>`, a reason on every line, passes included. Fail anything not centered, not on a plain field, or carrying text." Repeat per chunk. Answers land as text assets (one, or one per image): `asset_download` them to read the verdicts.
4. `asset_describe` on the strongest pass. Its promptable synthesis goes straight into the next batch's prompt, which holds the look without a training run (see `scenario-consistency`); when the synthesis is only a short title, prompt with the description instead.
5. File the result: `collection_create`, then `collection_add_assets` in chunks of at most 49 ids. `asset_add_tags` is additive, so tag the failures rather than rebuilding a tag set.

## Common mistakes

- One `asset_analyze` call per asset when 10 fit in a call.
- Treating `num_outputs` as a batch size over `images`.
- Leaving `remove_background` at its default on an `asset_detect` map that must match the source frame.
- Running `asset_analyze` or `asset_detect` through `scenario_tool_execute_read`: both are write-class and the call is rejected by lane, not by argument.
- Sending more than 49 ids to `collection_add_assets`, or re-adding an asset already in the collection: both are hard errors, not no-ops.
- Asking `asset_analyze` to produce an image. It returns text; control maps come from `asset_detect` and final renders from `model_run`.